Daily Global, Regional & Local Rice E-Newsletter February 10,2016
Vol 6 Issue I
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Daily Global, Regional & Local Rice E-Newsletter
Today Rice News Headlines...
Editorial Board Chief Editor
Agriculture Research: VEC recommends 14 new rice hybrids Cochran, Wicker, Kelly Urge Use of American-grown Rice for Humanitarian Relief Rice Prices Genetically Modified Rice Could Withstand the Ravages of Climate Change Japan says El Nino has weakened, weather to return to normal by summer Cochran, Wicker, Kelly Urge Use of American-grown Rice for Humanitarian Relief Is Deadly Afla-Toxin Eating Your Rice? Importer Says Yes, Bureau Of Standards Says No Rice Prices Nagpur Foodgrain Prices Open-Feb 10 Rice yields tempt traditional canegrowers Rice-farmers-cry-foul Average price of rice down slightly Nothing to beat black rice Rice yields tempt traditional canegrowers Average price of rice down slightly 02/09/2016 Farm Bureau Market Report Returning to Greener Pastures in Wayanad USA Rice Continues to Push for Open Trade with Cuba President Releases FY 2017 Budget Recommendations, Not Very Ag-Friendly Genome-wide prediction models that incorporate de novoGWAS are a powerful new tool for tropical rice improvement Hutchinson Selects New Arkansas Plant Board Director GINNA PARSONS: Asian meatballs sweet and spicy
News Detail... Agriculture Research: VEC recommends 14 new rice hybrids Published: February 10, 2016
Hamlik
Managing Editor
Abdul Sattar Shah Rahmat Ullah Rozeen Shaukat
English Editor
Maryam Editor Legal Advisor Advocate Zaheer Minhas
Editorial Associates
Admiral (R) Hamid Khalid Javed Islam Agha Ch.Hamid Malhi Dr.Akhtar Hussain Dr.Fayyaz Ahmad Siddiqui Dr.Abdul Rasheed (UAF) Islam Akhtar Khan
Editorial Advisory Board
Dr.Malik Mohammad Hashim
Assistant Professor, Gomal University DIK
Dr.Hasina Gul
Assistant Director, Agriculture KPK
Dr.Hidayat Ullah
Assistant Professor, University of Swabi
Dr.Abdul Basir
Assistant Professor, University of Swabi
Zahid Mehmood PSO,NIFA Peshawar
Falak Naz Shah
ISLAMABAD: The Variety Evaluation Committee (VEC) on Rice of the Head Food Science & Pakistan Agriculture Research Council (PARC) has recommended 14 new rice Technology ART, Peshawar hybrids to the National Seed Council for their commercial cultivation in the By APP country. Some of the recommended hybrids have yielded up to 106 maunds per acre, according to a statement. 2|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Parc Chairman Dr Nadeem Amjad said that with the addition of new recommended hybrids in the national system of the country there would be a significant improvement in the rice production in Pakistan. In the VEC rice meeting sixteen private seed companies presented their rice hybrids proposals. The VEC rejected fifteen (15) rice hybrids due to poor paddy yield, BLB disease susceptibility and poor grain quality. The National Coordinator (Cereal Systems) Parc Mian Abdul Majid appreciated the role of private seed companies for taking interest in rice research and development and working in close collaboration with public sector.
Published in The Express Tribune, February 10th, 2016. http://www.dailytimes.com.pk/business/09-Feb-2016/pakistan-s-exportpotential-to-get-push-at-gulfood http://tribune.com.pk/story/1043513/agriculture-research-vec-recommends-14-new-rice-hybrids/
Cochran, Wicker, Kelly Urge Use of American-grown Rice for Humanitarian Relief
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Daily Global, Regional & Local Rice E-Newsletter WASHINGTON – U.S. Senators Thad Cochran, R-Miss., and Roger Wicker, R-Miss., along with U.S. Representative Trent Kelly, R-Miss., today joined a bipartisan group of lawmakers advocating for government procurement of U.S.-grown rice for use in ―Food For Peace‖ emergency response efforts. The U.S. Department of Agriculture (USDA) and U.S. Agency for International Development (USAID) are in the process of determining commodities for use in humanitarian relief shipments. In a letter to Agriculture Secretary Tom Vilsack and USAID Administrator Gayle Smith, the Mississippians joined lawmakers from other rice-producing states to ask that U.S.-grown rice be prioritized for delivery through ―Food For Peace‖ and other relief programs. ―It is no secret that rice is the most consumed commodity in the world and is an excellent staple food in addressing hidden hunger,‖ the lawmakers wrote. ―Milled U.S.-grown rice has a long history as an important part of both USDA‘s and USAID‘s ongoing food assistance programs. The recent addition of fortified rice to the U.S. government commodity master list will also provide new opportunities to address the devastating impact of acute micronutrient deficiencies.‖ The Consolidated Appropriations Act for Fiscal Year 2016 (Public Law 114-113) provided funding for food aid to international populations in need. In addition to ―Food For Peace,‖ the food aid funding supports the McGovern-Dole Food for Education Program and the Food for Progress Program. These programs help support food security where it is most in jeopardy and provide relief to conflict-stricken areas around the globe. Rice is a leading commodity crop grown in Mississippi. The Mississippi Delta produced 13.6 million hundredweight of rice in 2014, with a $174 million production value. The letter, spearheaded by Sen. John Boozman, R-Ark., was signed by Senators Cochran, Wicker, Tom Cotton, R-Ark., David Vitter, R-La., Bill Cassidy, R-La., and John Cornyn, RTexas, and Representatives Kelly, Rick Crawford, R-Ark., Ralph Abraham, M.D., R-La., Mike Bost, R-Ill., Bruce Westerman, R-Ark., French Hill, R-Ark., Jason Smith, R-Mo., Charles Boustany Jr., M.D., R-La., Doug LaMalfa, R-Calif., Steve Womack, R-Ark., John Fleming, M.D., R-La., Ted Poe, R-Texas, and John Garamendi, D-Calif. Full text of the letter: Dear Secretary Vilsack and Administrator Smith: 4|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Thank you for your work in utilizing the bounty of the U.S. agriculture sector to provide food to the world‘s hungry and to conflict-stricken areas around the globe, particularly through the use of the Food For Peace program. This partnership between American agriculture and global humanitarian efforts dates back over 60 years and we hope to see it continue to improve nutrition and food security globally. As you know, in December H.R. 2029, the Consolidated Appropriations Act for Fiscal Year 2016, was signed into law. This bill directly supports U.S. international food aid efforts by providing additional funding to deliver critically needed food aid to populations in need. The additional food aid funds not only support the continued success of critical programs, such as the McGovern-Dole Food For Education Program and the Food for Progress Program, but also provide the first significant increase in P.L. 480 funding in many years for the Food For Peace program. The additional $250 million is to be directed towards supporting emergency response efforts to the ongoing refugee crisis and famine through in-kind food aid assistance. As you know, in-kind food aid contributions leverage the bounty of U.S. agricultural commodities to provide a safe and reliable source of food to populations in need. It is no secret that rice is the most consumed commodity in the world and is an excellent staple food in addressing hidden hunger. Milled U.S.-grown rice has a long history as an important part of both USDA‘s and USAID‘s ongoing food assistance programs. The recent addition of fortified rice to the U.S. government commodity master list will also provide new opportunities to address the devastating impact of acute micronutrient deficiencies. The U.S. rice industry also has a long and successful partnership with both the U.S. government and private humanitarian agencies and shares the goal of meeting the nutritional needs of vulnerable populations around the world. With a strong crop of rice this past year resulting in significant stocks, we see a clear opportunity to provide greater assistance in an even more economical fashion to help those in need as a result of the refugee crisis. We ask that during the procurement process for the in-kind commodities used in the emergency response efforts in the Middle East that U.S.-grown rice, including fortified rice, be prioritized for delivery to those in need. - See more at: http://www.newsms.fm/26795-2/#sthash.SOXvpKfc.
dpufhttp://www.newsms.fm/26795-2/,
http://7newsbelize.com/contact.php
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Daily Global, Regional & Local Rice E-Newsletter
Rice Prices as on : 10-02-2016 02:36:20 PM Arrivals in tonnes;prices in Rs/quintal in domestic market. Arrivals Current
Price
% Season Prev. Prev.Yr Modal change cumulative Modal %change Rice
Gadarpur(Utr)
1431.00 68.35
56169.00
2390
2050
33.52
Bharthna(UP)
400.00 -42.86
4300.00
2250
2250
3.45
Kanpur(Grain)(UP)
360.00
-5.26
5465.00
2165
2140
1.88
Etawah(UP)
300.00
-6.25
14170.00
2255
2255
2.50
Manjeri(Ker)
290.00
NC
6090.00
3000
3000
-9.09
Allahabad(UP)
200.00
-9.09
4400.00
2100
2100
3.45
Faizabad(UP)
180.00
12.5
2591.50
2150
2150
-
Bahraich(UP)
175.00
6.06
2267.50
2075
2080
NC
Gondal(UP)
152.00
3.05
9384.10
2010
2020
-0.74
Ballia(UP)
150.00
-6.25
5130.00
1960
1960
-1.51
Sitapur(UP)
150.00
1.35
4005.00
2220
2222
2.78
Shahjahanpur(UP)
140.60
-7.19
38171.00
2200
2200
9.18
Agra(UP)
135.00
3.85
3219.00
2100
2110
3.70
Pilibhit(UP)
120.00
-40
15918.00
1250
2190
-46.70
Lucknow(UP)
119.00
-3.25
2542.00
2150
2150
-1.15
Basti(UP)
110.00 -28.57
2920.00
2060
2070
5.10
Bareilly(UP)
106.00
5505.00
2200
2125
4.27
7.61
6|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Murud(Mah)
102.00
3300
153.00
2750
2750
71.88
Mathabhanga(WB)
100.00 -23.08
2100.00
1950
1950
-
Sehjanwa(UP)
93.00
-
270.00
2120
-
-
Nalbari(ASM)
88.50
-
357.50
2000
-
NC
Kalna(WB)
84.00
5
769.00
1785
1770
-6.05
Aligarh(UP)
80.00 14.29
1340.00
2150
2150
13.76
Saharanpur(UP)
74.00 23.33
3121.00
2035
2040
-3.10
P.O. Uparhali Guwahati(ASM)
72.00
5.88
1907.00
2100
2100
-19.23
Ghaziabad(UP)
70.00
75
1350.00
2075
2070
-2.12
Barasat(WB)
60.00 -14.29
1325.00
2200
2200
-2.22
Lanka(ASM)
50.00
25
1535.00
1725
1725
-
Muzzafarnagar(UP)
45.00 -19.64
686.00
2060
2060
-
Bindki(UP)
45.00
12.5
1488.00
2245
2230
7.93
Gauripur(ASM)
43.00 -15.69
1729.50
4500
4500
-
Cachar(ASM)
40.00
100
1080.00
2700
2700
NC
Jaunpur(UP)
40.00
8.11
851.00
1940
1940
-
Gazipur(UP)
40.00
5.26
915.50
1910
1910
3.80
Banda(UP)
30.00
20
251.50
2150
2165
-
Haldibari(WB)
30.00
20
441.50
2400
2350
-7.69
Mirzapur(UP)
27.50
5.77
805.00
1920
1915
2.67
Madhoganj(UP)
27.00 42.11
148.00
2075
2100
-
Kolhapur(Laxmipuri)(Mah)
25.00
NC
676.00
3000
3000
-
Ramkrishanpur(Howrah)(WB)
25.00
2.46
624.40
2300
2300
-11.54
Purulia(WB)
24.00
-20
1200.00
2200
2180
-8.33
Partaval(UP)
22.50
12.5
834.50
2050
2050
6.49
7|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Karimganj(ASM)
20.00
-
100.00
2800
-
-
Lakhimpur(UP)
18.00
-10
338.50
2120
2110
-
Karvi(UP)
16.50 26.92
134.50
2150
2150
16.22
Baraut(UP)
16.00
6.67
139.00
2135
2130
2.15
Kolaghat(WB)
16.00
NC
303.00
2300
2300
-
Tamluk (Medinipur E)(WB)
15.00
7.14
326.00
2300
2300
-
Tanakpur(Utr)
14.00
180
118.10
2150
1900
4.88
Jeypore(Kotpad)(Ori)
12.20 258.82
136.20
3250
3250
NC
Dibrugarh(ASM)
12.00 -42.86
552.30
2400
2400
-
Firozabad(UP)
11.00
-8.33
287.00
2150
2140
5.39
Raiganj(WB)
11.00
NC
494.00
2800
2800
-
Muradabad(UP)
10.00
5.26
309.50
2230
2245
12.91
North Lakhimpur(ASM)
9.40
40.3
775.00
1900
1900
-
Bhivandi(Mah)
9.00 28.57
150.00
2250
2000
32.35
Nilagiri(Ori)
9.00
12.5
253.00
2300
2400
NC
Dibiapur(UP)
9.00
12.5
40.00
2230
2240
1.83
Kaliaganj(WB)
9.00
NC
336.00
2700
2700
-
Katwa(WB)
9.00
NC
69.00
2300
2300
-4.17
Naugarh(UP)
8.50 -39.29
340.50
2060
2065
9.87
Raibareilly(UP)
8.00
6.67
170.50
2000
2000
-1.23
Buland Shahr(UP)
8.00 14.29
239.50
2050
2045
0.99
Orai(UP)
7.50
-40
20.00
2050
2000
-
Chengannur(Ker)
7.00 16.67
321.00
2500
2500
-10.71
Nimapara(Ori)
6.50 44.44
116.00
2200
2200
NC
Cherthalai(Ker)
6.00
196.50
2400
2300
-11.11
-40
8|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Jhansi(UP)
6.00
20
128.50
2100
2100
-
Karanjia(Ori)
5.50
10
144.80
2600
2600
4.00
Pakur(Jha)
5.20 13.29
55.80
3151
3147
-
Khairagarh(UP)
5.00
-37.5
215.00
2090
2050
2.96
Jeypore(Ori)
4.30
-2.27
137.90
325
325
-
Thoubal(Man)
3.70
-7.5
58.20
2700
2600
-
Fatehpur(UP)
3.50
-12.5
91.50
2165
2160
4.09
Aroor(Ker)
3.00 328.57
103.70
6900
6900
-25.81
Alibagh(Mah)
3.00
NC
69.00
3750
3750
134.38
Bonai(Bonai)(Ori)
3.00
-40
37.10
2000
2000
-9.09
Melaghar(Tri)
2.50 -16.67
73.80
2350
2350
NC
Karsiyang(Matigara)(WB)
2.50
8.7
36.40
2600
2600
-
Darjeeling(WB)
2.40
-4
39.10
2800
2800
-
Gulavati(UP)
1.50
NC
20.00
2050
2060
NC
Siyana(UP)
1.50
NC
46.50
2045
2050
1.49
Kalimpong(WB)
1.40
40
15.00
2450
2450
-
Sardhana(UP)
1.20
20
45.10
2075
2070
1.22
Shillong(Meh)
0.60
-40
29.90
3500
3500
NC
Silachhari(Tri)
0.60
-
1.50
2800
-
-
http://www.thehindubusinessline.com/economy/agri-business/article8218354.ece
Genetically Modified Rice Could Withstand the Ravages of Climate Change ―I‘ve never had so many students want to take part in a project,‖ says Jane Langdale. ―They want to save the world.‖ 9|www.ricepluss.com , www.riceplusmagazine.blosgspot.com
Daily Global, Regional & Local Rice E-Newsletter Langdale, a professor of plant development at the University of Oxford, is part of a team of scientists from 12 universities in eight countries working to develop a new strain of hyperefficient, drought-resistant rice known as C4. And in a world with a rapidly changing climate where nearly a billion people live in hunger, it could have a huge impact. Over 3 billion people across the globe depend on rice for survival—it‘s one of the most widely consumed food crops, providing over one-fifth of the calories consumed by humans worldwide. As populations grow, this demand will increase. According to the International Rice Research Institute, each hectare of land (about 2.5 acres) used to cultivate rice in Asia provides food for 27 people. But by 2050, that same hectare will need to feed 43 people. Meanwhile, climate change will make production more difficult. Increased global temperatures will bring more erratic weather patterns, including more frequent and more intense droughts, and this will increase water scarcity and make the cultivation of this vital crop ever more difficult. ―The planet is set to increase to 9 billion by 2043,‖ says Paul Quick, a principal scientist at the International Rice Research Institute in the Philippines. ―As the world gets hotter, we have to think of new and novel ways of improving agriculture to meet the food demands of the future.‖ Now this group of scientists from around the world is working to create a strain of hyperefficient rice resistant to the effects of climate change; it produces a greater yield in warmer temperatures while using less water. Traditionally, rice plants grow through a chemical process known as C3 photosynthesis: They take carbon dioxide (CO2) out of the air, break it down and use the carbon molecules in forming 3phosphoglyceric acid (3-PGA), which, as one paper puts it, ―is subsequently used to build the organic molecules of life.‖
This process keeps C3 plants alive, but it‘s relatively inefficient because of the way a key enzyme, ribulose-1,5-bisphosphate carboxylase/oxygenase, works. RuBisCO, as it is commonly referred to, helps facilitate the CO2 reaction. But it can also react with oxygen in the air, creating a toxic compound the plant then needs to address. This process wastes energy and reduces the plant‘s food-making efficiency. 10 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter And when it‘s hot, this becomes even more of a problem: At higher temperatures, RuBisCO is more likely to confuse O2 for CO2. On the other hand, natural C4 plants, like corn, are more efficient thanks to the cell structure of their leaves. In C4 plants, RuBisCO transforms CO2 into energy away from the leaf surface in specialized cells, called bundle sheath cells. This prevents RuBisCO from reacting with oxygen in the air and forces it to react only with CO2, allowing the photosynthetic process to operate at maximum efficiency. Stomata (tiny apertures in the leaf‘s outer layer) can remain more closed in C4 plants during this process, meaning they don‘t lose as much water through transpiration—extra helpful in the expected drier environment of the future. If these scientists can replicate the C4 process in a rice plant, the result could be a hypercharged rice with the ability to resist the effects of climate change. ―It‘s like putting a turbocharger in a car,‖ says Quick. ―These plants focus CO2 so that instead of having 400 parts per million, you‘ve got 1,000 or 1,500 parts per million.‖ As a result of this increased efficiency, C4 plants also have greater drought resistance. ―C4 plants grow in hotter, drier areas,‖ says Julian Hibberd, a professor of molecular physiology at Cambridge University. ―They have a better tolerance for periods of low water supply. With increased fluctuations in climate, we are going to need a crop that is more resistant. C4 could be the answer.‖ Research technician Eleazar Manalaysay checks on rice inside a greenhouse at the International Rice Research Institute headquarters in Los Banos, the Philippines, February 23, 2012. Researchers are working on identifying the genes in C4 plants responsible for activating a more efficient photosynthetic process.JULIAN ABRAM WAINWRIGHT/BLOOMBERG/GETTY
Researchers are working on identifying the genes in C4 plants responsible for creating the plants‘ cell structure and activating the more efficient photosynthetic process. Once these genes are identified, the goal is to figure out how to insert them into the rice genome. Scientists are quietly hopeful of a breakthrough soon; the Massachusetts Institute of Technology named the C4 project one of the ―10 Breakthrough Technologies of 2015.‖ If successful, C4 rice could revolutionize a planet in which a steadily changing climate is putting the world‘s food supply at risk. ―A stable supply of food in emerging 11 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter economies would be an incredible boost to the global economy,‖ says Hibberd. ―It could also create greater societal stability worldwide.‖
But there is at least one catch: Rice cultivation is a massive contributor to climate change. Methane is the most potent greenhouse gas in the atmosphere because of its ability to trap heat within the atmosphere, producing 21 times as much global warming as CO2 and accounting for 20 percent of the global greenhouse effect. And up to 17 percent of global methane emissions come from rice cultivation. In large part, that‘s because the warm, waterlogged soil in rice paddies provides ideal conditions for the growth of a particular kind of bacteria known as methanogens. ―When they consume carbon dioxide that has been emitted by the roots, they metabolize it and produce methane,‖ says Christer Jansson, director of plant sciences at the Environmental Molecular Sciences Laboratory in Richland, Washington. ―This
12 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter methane then travels up through the ground and the plant and into the atmosphere.‖ The result is that rice farming leads to 25 million to 100 million tons of methane emitted into the air every year. Jansson is part of a group, led by Chuanxin Sun of the Swedish University of Agricultural Sciences, working to solve this problem by creating a rice plant that produces less methane. Sun and his team set out to see if they could channel carbon in the plant from belowground, in the roots, to aboveground, in the stems and leaves, and therefore stop bacteria near the roots from producing so much methane. By taking a gene from the barley plant that regulates where and how carbon is stored and inserting it into the rice plant, the scientists have created a new rice variety, dubbed SUSIBA2 rice. Thanks to the barley gene, the SUSIBA2 plant captures more CO2 in its leaves, stems and grains while reducing the carbon allocated to the roots. ―Through this process,‖ says Jansson, ―the methane-producing bacteria near the roots are starved and cannot produce methane.‖ The concentration of carbon in the grains also produces larger, starchier rice grains, as well as an overall yield increase of around 10 percent. Test results so far are positive: A study published last year inNature found that three-year field trials in China were associated with a significant reduction in methane emissions. ―It‘s potentially huge,‖ says Jansson. ―If we have a rice that can produce more food for the population at the same time as reducing methane, it would be an incredible breakthrough.‖ Excited as they are, both groups of scientists are cautious and admit that it will likely be 10 to 15 years before these strains are commercially available, even if all the testing goes according to plan. A major challenge facing both studies is increasing worldwide skepticism of genetically modified organism foods. ―If there is something viable that could be commercialized, the concern would be around the unintended consequences,‖ says Megan Westgate, executive director of the Non-GMO Project, a U.S.-based nonprofit. ―It‘s justified that consumers are concerned to know what the impact will be on the environment and on human health.‖ Scientists are keenly aware of the concern. Sun says that ―so far we have not seen any negative impact on the environment.‖ However, he admits that ―if we drive carbon aboveground to the grain, it might affect the soil ecosystem, so we have to do more experiments to understand these effects.‖ Likewise, Jansson says that ―we need to investigate to see the total benefit of this product, to see the pros and cons. If there 13 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter are negative effects on human consumption or the environment, we need to identify those and mitigate them.‖ For the scientists behind the C4 project, arguments against GMO crops are diminished by the fact that C4 plants are naturally occurring and that, in a sense, they are just reproducing what nature has already achieved. ―Evolution itself has done this 60 times,‖ says Langdale. ―Twenty to 30 million years ago, plants evolved C4 mechanisms on their own.‖ But another major concern for Westgate and others from the anti-GMO movement is what happens when corporate players become involved. Monsanto, the American biotechnology company involved in numerous lawsuits over the health and environmental effects of its products, is their boogeyman. ―The biggest problem with corporate involvement is specifically around the patenting and what that does to food sovereignty,‖ says Westgate. ―When corporations have control of the genetic sequencing of our major foods, it becomes very problematic.‖ In fact, the International Rice Research Institute‘s Quick admits that if C4 rice becomes commercially viable, ―only large agri-businesses would have the capacity to distribute it properly.‖ However, he is adamant that he and his team would negotiate so that developing countries would be free from the intellectual property laws that govern this kind of genetic patenting. Ultimately, most scientists feel that the potential benefits of the C4 rice project work far outweigh any potentially negative consequences. ―We are doing this as a humanitarian project to stop world hunger,‖ Langdale says. ―At the end of the day, if someone is starving, would they rather eat genetically modified rice or nothing at all?‖
Japan says El Nino has weakened, weather to return to normal by summer TOKYO
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Daily Global, Regional & Local Rice E-Newsletter
A lake with low levels of water can be seen in a drought affected farming land on the outskirts of Canberra in Australia January 12, 2016. REUTERS/DAVID GRAY
Japan's meteorology agency said on Wednesday that the El Nino weather pattern, which is often linked to heavy rainfall and droughts, had somewhat weakened and that the weather would likely return to normal by summer.The Japan Meteorological Agency said last month that the El Nino phenomenon peaked between November and December, and that there was a strong possibility weather patterns would return to normal by summer. The El Nino - a warming of sea-surface temperatures in the Pacific - can trigger drought in Southeast Asia and Australia and floods in South America, hitting production of key foods such as rice, wheat and sugar. (Reporting by Chang-Ran Kim; Editing by Tom Hogue) http://www.reuters.com/article/us-weather-elnino-japan-idUSKCN0VJ09E
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Daily Global, Regional & Local Rice E-Newsletter
Cochran, Wicker, Kelly Urge Use of American-grown Rice for Humanitarian Relief WASHINGTON – U.S. Senators Thad Cochran, R-Miss., and Roger Wicker, R-Miss., along with U.S. Representative Trent Kelly, R-Miss., today joined a bipartisan group of lawmakers advocating for government procurement of U.S.-grown rice for use in ―Food For Peace‖ emergency response efforts.The U.S. Department of Agriculture (USDA) and U.S. Agency for International Development (USAID) are in the process of determining commodities for use in humanitarian relief shipments. In a letter to Agriculture Secretary Tom Vilsack and USAID Administrator Gayle Smith, the Mississippians joined lawmakers from other rice-producing states to ask that U.S.-grown rice be prioritized for delivery through ―Food For Peace‖ and other relief programs.
―It is no secret that rice is the most consumed commodity in the world and is an excellent staple food in addressing hidden hunger,‖ the lawmakers wrote. ―Milled U.S.-grown rice has a long history as an important part of both USDA‘s and USAID‘s ongoing food assistance programs. The recent addition of fortified rice to the U.S. government commodity master list will also provide new opportunities to address the devastating impact of acute micronutrient deficiencies.‖ 16 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter The Consolidated Appropriations Act for Fiscal Year 2016 (Public Law 114-113) provided funding for food aid to international populations in need. In addition to ―Food For Peace,‖ the food aid funding supports the McGovern-Dole Food for Education Program and the Food for Progress Program. These programs help support food security where it is most in jeopardy and provide relief to conflict-stricken areas around the globe. Rice is a leading commodity crop grown in Mississippi. The Mississippi Delta produced 13.6 million hundredweight of rice in 2014, with a $174 million production value. The letter, spearheaded by Sen. John Boozman, R-Ark., was signed by Senators Cochran, Wicker, Tom Cotton, R-Ark., David Vitter, R-La., Bill Cassidy, R-La., and John Cornyn, RTexas, and Representatives Kelly, Rick Crawford, R-Ark., Ralph Abraham, M.D., R-La., Mike Bost, R-Ill., Bruce Westerman, R-Ark., French Hill, R-Ark., Jason Smith, R-Mo., Charles Boustany Jr., M.D., R-La., Doug LaMalfa, R-Calif., Steve Womack, R-Ark., John Fleming, M.D., R-La., Ted Poe, R-Texas, and John Garamendi, D-Calif. Full text of the letter: Dear Secretary Vilsack and Administrator Smith: Thank you for your work in utilizing the bounty of the U.S. agriculture sector to provide food to the world‘s hungry and to conflict-stricken areas around the globe, particularly through the use of the Food For Peace program. This partnership between American agriculture and global humanitarian efforts dates back over 60 years and we hope to see it continue to improve nutrition and food security globally. As you know, in December H.R. 2029, the Consolidated Appropriations Act for Fiscal Year 2016, was signed into law. This bill directly supports U.S. international food aid efforts by providing additional funding to deliver critically needed food aid to populations in need. The additional food aid funds not only support the continued success of critical programs, such as the McGovern-Dole Food For Education Program and the Food for Progress Program, but also provide the first significant increase in P.L. 480 funding in many years for the Food For Peace program.
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Daily Global, Regional & Local Rice E-Newsletter The additional $250 million is to be directed towards supporting emergency response efforts to the ongoing refugee crisis and famine through in-kind food aid assistance. As you know, in-kind food aid contributions leverage the bounty of U.S. agricultural commodities to provide a safe and reliable source of food to populations in need. It is no secret that rice is the most consumed commodity in the world and is an excellent staple food in addressing hidden hunger. Milled U.S.-grown rice has a long history as an important part of both USDA‘s and USAID‘s ongoing food assistance programs. The recent addition of fortified rice to the U.S. government commodity master list will also provide new opportunities to address the devastating impact of acute micronutrient deficiencies. The U.S. rice industry also has a long and successful partnership with both the U.S. government and private humanitarian agencies and shares the goal of meeting the nutritional needs of vulnerable populations around the world. With a strong crop of rice this past year resulting in significant stocks, we see a clear opportunity to provide greater assistance in an even more economical fashion to help those in need as a result of the refugee crisis. We ask that during the procurement process for the in-kind commodities used in the emergency response efforts in the Middle East that U.S.-grown rice, including fortified rice, be prioritized for delivery to those in need. http://www.newsms.fm/26795-2/#sthash.SOXvpKfc.dpuf
Is Deadly Afla-Toxin Eating Your Rice? Importer Says Yes, Bureau Of Standards Says No (February
10, 2016)
Is there a deadly contaminant in your rice? Importer Jack Charles says there is! And he says he’s confirmed it with a top lab in the US. The contaminant is called Afla Toxin – which is carcinogenic, meaning it causes cancer, and it’s also linked to liver disease and other ailments.Today, after holding back for more than a week to get some response from Government, Charles sent out the test results, when it became clear that Government wouldn’t budge.
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Daily Global, Regional & Local Rice E-Newsletter They show three different brands of local rice, which he bought off the shelf and sent to Eurofins Laboratory in New Orleans, Louisiana. The test results for all three brands of rice show the presence of aflatoxins – but at less than two micrograms per kilogramme.The law – which is Statutory Instrument of October 2015, says, quote, “Rice shall be free of…contaminants…that is, aflatoxin which is created by improper storage. “So, then Charles takes the words “free of contaminants” to mean that the rice should have no aflatoxins, meaning that it has tested negative for the contaminant. He declined an interview, but sent a release, which says, quote, “ The results (were) outside the limits…especially those related to biological contaminants which are harmful to human health. ”But, the rice producers say he has it all wrong. They say that the tests, in fact, confirm that local rice is safe to eat. Attorney for the rice producers, Eamon Courtenay told us quote, "The test results establish that Belizean produced rice is safe for human consumption. Mr Charles' press release amounts to a libel on my clients good name. This latest desperate attack by the importer is misleading and calculated to cause panic in the Belizean community. It confirms what I said: he will do absolutely anything in order to make a profit by supporting imported rice."We couldn’t get comment from BAHA to find out what “free of…. contaminants” means specifically in terms of micrograms, but the meaning is clear enough. The Ministry of Agriculture referred us to the Bureau of Standards – which sent a release late this evening. It says, quote, “ the Bureau…concluded that the test results provided…by Mr. Jack Charles shows that none of the rice samples analysed contain detectable levels of aflatoxins and as a result does not pose any threat to human health.”And the usually indifferent agency ends with a flourish, adding, quote, “It is regrettable that the commercial interests of one individual, veiled under the guise of food safety concerns, is being used in an attempt to erode the confidence in food safety which the people of Belize now enjoy.”What we have found from our own research is that regulatory authorities in different countries have set tolerance limits for aflatoxins that range from 0 to 50 micrograms per kilogram.
In India, for example, a tolerance limit of 30 micrograms is their standard. The rice tested in Belize is less than two micrograms.And what causes aflatoxin contamination? Well to really simplify it aflatoxin is like a type of mold – but a very harmful one, that is created when stored rice or grains get wet. It affects all types of grains, including corn, and is considered an unavoidable contaminant of food.
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Daily Global, Regional & Local Rice E-Newsletter http://7newsbelize.com/contact.php
Rice Prices as on : 10-02-2016 02:36:20 PM Arrivals in tonnes;prices in Rs/quintal in domestic market. Arrivals Current
Price
% Season Prev. Prev.Yr Modal change cumulative Modal %change Rice
Gadarpur(Utr)
1431.00 68.35
56169.00
2390
2050
33.52
Bharthna(UP)
400.00 -42.86
4300.00
2250
2250
3.45
Kanpur(Grain)(UP)
360.00
-5.26
5465.00
2165
2140
1.88
Etawah(UP)
300.00
-6.25
14170.00
2255
2255
2.50
Manjeri(Ker)
290.00
NC
6090.00
3000
3000
-9.09
Allahabad(UP)
200.00
-9.09
4400.00
2100
2100
3.45
Faizabad(UP)
180.00
12.5
2591.50
2150
2150
-
Bahraich(UP)
175.00
6.06
2267.50
2075
2080
NC
Gondal(UP)
152.00
3.05
9384.10
2010
2020
-0.74
Ballia(UP)
150.00
-6.25
5130.00
1960
1960
-1.51
Sitapur(UP)
150.00
1.35
4005.00
2220
2222
2.78
Shahjahanpur(UP)
140.60
-7.19
38171.00
2200
2200
9.18
Agra(UP)
135.00
3.85
3219.00
2100
2110
3.70
Pilibhit(UP)
120.00
-40
15918.00
1250
2190
-46.70
Lucknow(UP)
119.00
-3.25
2542.00
2150
2150
-1.15
Basti(UP)
110.00 -28.57
2920.00
2060
2070
5.10
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Daily Global, Regional & Local Rice E-Newsletter Bareilly(UP)
106.00
7.61
5505.00
2200
2125
4.27
Murud(Mah)
102.00
3300
153.00
2750
2750
71.88
Mathabhanga(WB)
100.00 -23.08
2100.00
1950
1950
-
Sehjanwa(UP)
93.00
-
270.00
2120
-
-
Nalbari(ASM)
88.50
-
357.50
2000
-
NC
Kalna(WB)
84.00
5
769.00
1785
1770
-6.05
Aligarh(UP)
80.00 14.29
1340.00
2150
2150
13.76
Saharanpur(UP)
74.00 23.33
3121.00
2035
2040
-3.10
P.O. Uparhali Guwahati(ASM)
72.00
5.88
1907.00
2100
2100
-19.23
Ghaziabad(UP)
70.00
75
1350.00
2075
2070
-2.12
Barasat(WB)
60.00 -14.29
1325.00
2200
2200
-2.22
Lanka(ASM)
50.00
25
1535.00
1725
1725
-
Muzzafarnagar(UP)
45.00 -19.64
686.00
2060
2060
-
Bindki(UP)
45.00
12.5
1488.00
2245
2230
7.93
Gauripur(ASM)
43.00 -15.69
1729.50
4500
4500
-
Cachar(ASM)
40.00
100
1080.00
2700
2700
NC
Jaunpur(UP)
40.00
8.11
851.00
1940
1940
-
Gazipur(UP)
40.00
5.26
915.50
1910
1910
3.80
Banda(UP)
30.00
20
251.50
2150
2165
-
Haldibari(WB)
30.00
20
441.50
2400
2350
-7.69
Mirzapur(UP)
27.50
5.77
805.00
1920
1915
2.67
Madhoganj(UP)
27.00 42.11
148.00
2075
2100
-
Kolhapur(Laxmipuri)(Mah)
25.00
676.00
3000
3000
-
NC
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Daily Global, Regional & Local Rice E-Newsletter Ramkrishanpur(Howrah)(WB)
25.00
2.46
624.40
2300
2300
-11.54
Purulia(WB)
24.00
-20
1200.00
2200
2180
-8.33
Partaval(UP)
22.50
12.5
834.50
2050
2050
6.49
Karimganj(ASM)
20.00
-
100.00
2800
-
-
Lakhimpur(UP)
18.00
-10
338.50
2120
2110
-
Karvi(UP)
16.50 26.92
134.50
2150
2150
16.22
Baraut(UP)
16.00
6.67
139.00
2135
2130
2.15
Kolaghat(WB)
16.00
NC
303.00
2300
2300
-
Tamluk (Medinipur E)(WB)
15.00
7.14
326.00
2300
2300
-
Tanakpur(Utr)
14.00
180
118.10
2150
1900
4.88
Jeypore(Kotpad)(Ori)
12.20 258.82
136.20
3250
3250
NC
Dibrugarh(ASM)
12.00 -42.86
552.30
2400
2400
-
Firozabad(UP)
11.00
-8.33
287.00
2150
2140
5.39
Raiganj(WB)
11.00
NC
494.00
2800
2800
-
Muradabad(UP)
10.00
5.26
309.50
2230
2245
12.91
North Lakhimpur(ASM)
9.40
40.3
775.00
1900
1900
-
Bhivandi(Mah)
9.00 28.57
150.00
2250
2000
32.35
Nilagiri(Ori)
9.00
12.5
253.00
2300
2400
NC
Dibiapur(UP)
9.00
12.5
40.00
2230
2240
1.83
Kaliaganj(WB)
9.00
NC
336.00
2700
2700
-
Katwa(WB)
9.00
NC
69.00
2300
2300
-4.17
Naugarh(UP)
8.50 -39.29
340.50
2060
2065
9.87
Raibareilly(UP)
8.00
170.50
2000
2000
-1.23
6.67
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Daily Global, Regional & Local Rice E-Newsletter Buland Shahr(UP)
8.00 14.29
239.50
2050
2045
0.99
Orai(UP)
7.50
-40
20.00
2050
2000
-
Chengannur(Ker)
7.00 16.67
321.00
2500
2500
-10.71
Nimapara(Ori)
6.50 44.44
116.00
2200
2200
NC
Cherthalai(Ker)
6.00
-40
196.50
2400
2300
-11.11
Jhansi(UP)
6.00
20
128.50
2100
2100
-
Karanjia(Ori)
5.50
10
144.80
2600
2600
4.00
Pakur(Jha)
5.20 13.29
55.80
3151
3147
-
Khairagarh(UP)
5.00
-37.5
215.00
2090
2050
2.96
Jeypore(Ori)
4.30
-2.27
137.90
325
325
-
Thoubal(Man)
3.70
-7.5
58.20
2700
2600
-
Fatehpur(UP)
3.50
-12.5
91.50
2165
2160
4.09
Aroor(Ker)
3.00 328.57
103.70
6900
6900
-25.81
Alibagh(Mah)
3.00
NC
69.00
3750
3750
134.38
Bonai(Bonai)(Ori)
3.00
-40
37.10
2000
2000
-9.09
Melaghar(Tri)
2.50 -16.67
73.80
2350
2350
NC
Karsiyang(Matigara)(WB)
2.50
8.7
36.40
2600
2600
-
Darjeeling(WB)
2.40
-4
39.10
2800
2800
-
Gulavati(UP)
1.50
NC
20.00
2050
2060
NC
Siyana(UP)
1.50
NC
46.50
2045
2050
1.49
Kalimpong(WB)
1.40
40
15.00
2450
2450
-
Sardhana(UP)
1.20
20
45.10
2075
2070
1.22
Shillong(Meh)
0.60
-40
29.90
3500
3500
NC
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Daily Global, Regional & Local Rice E-Newsletter Silachhari(Tri)
0.60
-
1.50
2800
-
-
http://www.thehindubusinessline.com/economy/agribusiness/article8218354.ecehttp://www.thehindubusinessline.com/economy/agri-business/article8212003.ece
Nagpur Foodgrain Prices Open-Feb 10 Nagpur Foodgrain Prices - APMC & Open Market-February 10 Nagpur, Feb 10 Gram and tuar prices showed weak tendency in Nagpur Agriculture Produce and Marketing Committee (APMC) here on lack of demand from local millers amid goodsupply from producing regions. Fresh fall on NCDEX in gram and reports about good overseasarrival also pulled down prices, according to sources. *
*
*
*
FOODGRAINS & PULSES GRAM * Gram varieties ruled steady in open market here but demand was poor. TUAR * Tuar gavarani and tuar Karnataka reported down in open market in absence of buyers amid increased supply from producing regions. * Rice basmati moved down in open market on poor buying support from local traders amid good arrival from producing belts. * In Akola, Tuar New - 8,000-8,200, Tuar dal New - 12,500-13,700, Udid 12,600-13,600, Udid Mogar (clean) - 14,900-16,700, Moong 8,400-8,600, Moong Mogar (clean) 9,400-9,700, Gram - 4,100-4,200, Gram Super best bold - 5,300-5,700 for 100 kg. * Wheat, other varieties of rice and other commodities moved in a narrow range in scattered deals, settled at last levels. Nagpur foodgrains APMC auction/open-market prices in rupees for 100 kg FOODGRAINS Available prices Previous close Gram Auction 4,000-4,550 4,000-4,600 Gram Pink Auction n.a. 2,100-2,600 Tuar Auction 7,200-8,130 7,200-8,250 Moong Auction n.a. 6,400-6,600 Udid Auction n.a. 4,300-4,500 Masoor Auction n.a. 2,600-2,800 Gram Super Best Bold 5,900-6,100 5,900-6,100 24 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter Gram Super Best n.a. n.a. Gram Medium Best 5,500-5,700 5,500-5,700 Gram Dal Medium n.a. n.a Gram Mill Quality 4,500-4,600 4,500-4,600 Desi gram Raw 4,450-4,500 4,450-4,500 Gram Filter new 4,600-4,900 4,600-4,900 Gram Kabuli 6,000-8,000 6,000-8,000 Gram Pink 6,500-7,300 6,500-7,300 Tuar Fataka Best-New 12,500-13,000 12,500-13,000 Tuar Fataka Medium-New 11,800-12,300 11,800-12,300 Tuar Dal Best Phod-New 11,500-12,000 11,500-12,000 Tuar Dal Medium phod-New 10,500-11,000 10,500-11,000 Tuar Gavarani New 7,450-7,950 7,500-8,000 Tuar Karnataka 8,150-8,450 8,200-8,500 Tuar Black 12,500-13,000 12,500-13,000 Masoor dal best 6,200-6,400 6,200-6,400 Masoor dal medium 5,800-6,000 5,800-6,000 Masoor n.a. n.a. Moong Mogar bold (New) 9,600-10,000 9,600-10,000 Moong Mogar Med 8,900-9,300 8,900-9,300 Moong dal Chilka 7,800-8,800 7,800-8,800 Moong Mill quality n.a. n.a. Moong Chamki best 8,600-8,800 8,600-8,800 Udid Mogar best (100 INR/KG) (New) 16,100-17,000 16,100-17,000 Udid Mogar Medium (100 INR/KG) 13,500-14,500 13,500-14,500 Udid Dal Black (100 INR/KG) 9,700-9,900 9,700-9,900 Batri dal (100 INR/KG) 5,550-5,900 5,550-5,900 Lakhodi dal (100 INR/kg) 4,400-4,600 4,400-4,600 Watana Dal (100 INR/KG) 3,250-3,400 3,250-3,400 Watana White (100 INR/KG) 3,000-3,200 3,000-3,200 Watana Green Best (100 INR/KG) 3,100-3,600 3,100-3,600 Wheat 308 (100 INR/KG) 1,700-1,800 1,700-1,800 Wheat Mill quality (100 INR/KG) 1,700-1,800 1,700-1,800 Wheat Filter (100 INR/KG) 1,650-1,850 1,650-1,850 Wheat Lokwan best (100 INR/KG) 2,200-2,500 2,100-2,500 Wheat Lokwan medium (100 INR/KG) 2,000-2,100 1,950-2,250 Lokwan Hath Binar (100 INR/KG) n.a. n.a. MP Sharbati Best (100 INR/KG) 3,200-3,600 3,200-3,600 MP Sharbati Medium (100 INR/KG) 2,500-3,000 1,500-3,000 Rice BPT best New(100 INR/KG) 2,600-2,800 2,600-2,800 Rice BPT medium (100 INR/KG) 2,000-2,250 2,000-2,200 Rice Parmal (100 INR/KG) 1,800-2,000 1,800-2,000 Rice Swarna best (100 INR/KG) 2,100-2,450 2,100-2,450 Rice Swarna medium (100 INR/KG) 1,800-2,000 1,800-2,000 Rice HMT best New (100 INR/KG) 3,000-3,500 3,000-3,500 25 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter Rice HMT medium (100 INR/KG) 2,400-2,800 2,400-2,800 Rice Shriram best New(100 INR/KG) 4,100-4,400 4,100-4,400 Rice Shriram med New(100 INR/KG) 3,700-4,100 3,700-4,100 Rice Basmati best (100 INR/KG) 9,700-11,500 9,700-11,600 Rice Basmati Medium (100 INR/KG) 7,600-8,000 7,700-8,000 Rice Chinnor best New(100 INR/KG) 4,700-4,800 4,700-4,800 Rice Chinnor med. New (100 INR/KG) 4,200-4,400 4,200-4,400 Jowar Gavarani (100 INR/KG) 1,800-2,100 1,800-2,100 Jowar CH-5 (100 INR/KG) 1,700-1,800 1,700-1,800 WEATHER (NAGPUR) Maximum temp. 31.3 degree Celsius (88.3 degree Fahrenheit), minimum temp. 14.3 degree Celsius (57.4 degree Fahrenheit) Humidity: Highest - n.a., lowest - n.a. Rainfall : n.a. FORECAST: Mainly clear sky. Maximum and minimum temperature would be around and 33 and 15 degreeCelsius respectively. Note: n.a.--not available (For oils, transport costs are excluded from plant delivery prices, but included in market prices.) http://in.reuters.com/article/nagpur-foodgrain-idINL3N15P1D2
Rice yields tempt traditional canegrowers February 10, 2016 3:00am Bernice KellyTownsville Bulletin
Cane grower Frank Pirrone is happily choosing rice over cane after record setting yields from last year's crop.THE Burdekin is quickly establishing itself as the nation‘s superior rice growing region, with two local farmers producing some of Queensland‘s highest yields.
Allan Milan of Giru and Frank Pirrone of Ayr achieved outstanding results in the 2015 dry season, with both growers producing more than 10 tonnes of rice per hectare. According to food giant SunRice, anything above nine tonnes per hectare is a fantastic result, yet Mr Milan produced an amazing 11.1 tonnes. Mr Pirrone was not far behind with 10.3 tonnes of rice per hectare in his first crop.He is so buoyed by his success that he has already planted a crop for the wet season cycle. ―I really can‘t believe it,‖ he said. ―I didn‘t think I‘d get the results that I have. It has met all my expectations so far. I‘m really rapt about it at the moment.‖ Mr Pirrone believes the soil conditioning benefits of mungbeans helped produce the big rice tonnages. He said he had grown and harvested a crop of mungbeans on the same ground that he later put the rice crop on. 26 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter
―A lot of people know that when you plant soy, mungbeans or any sort of beans, the ground soaks up horizontally instead of going down and we want it to go horizontally,‖ Mr Pirrone said. ―It‘s only because of the mungbeans doing the job for this crop (of rice) that it‘s allowed this to happen.‖ According to Burdekin-based Blue Ribbon Grain and Pulses manager Chris Richards, planting mungbeans or soy beans can improve the soil before the crop of rice is planted. ―It provides a good base for the rice when you‘re going to plant it,‖ Mr Richards said. ―Although it‘s not necessary, it is preferable to put a legume crop in beforehand as it can really help.‖ Burdekin growers are continuing to turn to rice as an alternative crop with the number of rice growers in the region doubling for this year‘s wet season crop. With construction already under way to expand the Brandon Mill, SunRice‘s business development and agronomy manager Rob Eccles said the company was continuing to make plans to develop the rice industry in North Queensland. ―The interest is continuing to increase and, because we‘re constructing the mill and securing more rice crops than ever before, we‘re expecting the industry to continue to grow,‖ he said. ―Rice offers a good cash crop between cane crops and the growers are starting to see this opportunity. ―It‘s a quick crop – only 110 days – and they get paid quickly.‖ http://www.townsvillebulletin.com.au/news/rice-yields-tempt-traditional-canegrowers/newsstory/ca4ec3dce32e3247d8f46723d66f3d9b
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Daily Global, Regional & Local Rice E-Newsletter
Rice-farmers-cry-foul Local rice producers have warned they will lose out massively on benefits of the ECOWAS Common External Tariff to the rice industry if efforts are not aimed at resolving challenges confronting them.Rice importation to Ghana still attracts a twenty percent charge at the ports despite the implementation of the CET.Head of tax policy unit of the Ministry of Finance, Anthony Dzadzraa tells Citi Business News, the move is to enable local rice producers improve on quality so they can be able to compete within five years time when government may be compelled to reduce the current charge to ten percent. But in an interview with Citi Business News, a rice farmer and the Director of Network of Rice Farming Associations, NETRICE, Amishadai Owusu fears they risk losing out if the local rice producers are not adequately resourced to aid fair competition.―Locally what kind of equipment do we have to compete with China or Thailand or any other country? In a whole district we have about three or four tractors. We should have our swamps developed; our swamps are not developed and they are full of stumps, and it is rather impossible for mechanized agriculture,―We also require driers http://www.ghanaweb.com/GhanaHomePage/NewsArchive/Rice-farmers-cry-foul-414302
Average price of rice down slightly FAO NOTES SOFTENING COST OF COMMODITIES 12:23 AM February 10th, 2016 THE GLOBAL average price of rice declined only marginally as overall food prices fell to a seven-year low in January, according to the Food and Agriculture Organization.Even then, the FAO said ―world rice stocks would need to be drawn down to bridge the expected gap between world production and consumption.‖The United Nations agency said worldwide consumption of the staple grain was expected to expand by 1.1 percent, which would keep stable the average consumption per person.At the start of 2016, the FAO‘s Food Price Index averaged at 150.4 points—three points or 1.9-percent lower than the 153.4 points recorded last December. The index was also 28.5 points or 16 percent lower than the 178.9 points observed in January 2015. More notably, the latest reading was the lowest since April 2009 or close to seven years ago.The index is tradeweighted and tracks prices of five major food commodity groups on international markets, including meat, dairy, cereals, vegetable oils and sugar.―The main factors underlying the lingering decline in basic food commodity prices are the generally ample agricultural supply conditions, a slowing global economy and the strengthening of the US dollar,‖ the FAO said in a statement.The agency said that as of last month, the Cereal Price sub-index eased by 1.7 percent ―amid ample global supplies and increased competition for export markets, especially for wheat and maize, as well as a strong US dollar. ‖The FAO-administered Agricultural Market Information System added that overall farm conditions for growing rice in the Philippines remained favorable despite the strong El Niño having reached its peak two months ago.―In the Philippines, the dry-season crop conditions are favorable but developing dry conditions are expected to impact the crop next month due to El Niño,‖ the Amis said.Even then, it said 28 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter the drought was expected to continue in parts of Southeast Asia where rainfall was already way below normal.In January, the Philippine Statistics Authority (PSA) said palay output was expected to ease down by 1.5 percent or 20,000 tons in the first semester of 2016 to settle at 8.2 million tons.The PSA said in its latest production outlook that the volume might decrease as rising yield could fail to offset shrinkage in the area harvested.
http://business.inquirer.net/206904/average-price-of-rice-downslightly#ixzz3zrZusq9V
Nothing to beat black rice KP PRABHAKARAN NAIR
THE HINDUTransition
time Let's switch to black rice, grown the SRI way
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Daily Global, Regional & Local Rice E-Newsletter Arguably of Japanese origin, and full of therapeutic properties, it grows in Manipur. Why not popularise it all over India? Rice is the staple diet of South Asia, in particular, the Indian sub continent. Like India, China, Japan, the Philippines and other neighbouring South and South East Asian populations also prefer rice to wheat. Indians have, in general, a propensity for the colour white, whether it is skin tone or body wear. So it is not surprising that black rice is relatively unknown to most Indians.In imperial China, black rice (Orayza sativa) was forbidden in China. Not because it looked poisonous because of its black colour, but because of its high nutritional value, which meant it could only be eaten by the Emperor.For long, the nutritional value of this wild rice eluded the commoners.It is only in recent times that rice researchers have begun to study the sticky varieties of black rice and discovered that it has several medicinal and nutritional properties.It has anti carcinogenic properties and its bran soothes inflammation due to allergies, asthma and other diseases. Black rice is sold in local markets for as much as Rs. 300 a kg. Black rice is indigenous to north-east India and is extensively grown in Odisha, West Bengal and Jharkhand. It is commonly eaten in Manipur because of its medicinal value. Called chak-hao, meaning rice (chak) which is delicious (ahaoba), black rice is eaten during traditional feasts. Chak-hao kheer is a popular pudding in these regions and the water in which black rice is boiled is used in these parts to wash hair, in the belief it makes hair strong. Mystery surrounds its origin. Japanese researchers discovered that its genetic trait is traceable to a rearrangement in a gene called Kala4, which activates the production of anthocyanin, a water soluble pigment which might show different colours like red, purple or blue depending on the pH. These researchers concluded that this rearrangement must have originally occurred in the tropical Japonica sub species of rice (Oryza sativa. var. Japonica) and the black rice trait was then transferred to other varieties, including those found today, by cross breeding. The findings of the origin of black rice help explain the history of domestication of black rice by ancient humans, during which they selected desirable traits, including grain colour. Medicinal properties
Black rice contains more Vitamins B and E, niacin, calcium, magnesium, iron and zinc compared to white rice. It is rich in fibre and the grains have a nutty taste. The anthocyanins not only act as antioxidants, they also activate detoxifying enzymes. The rice arrests proliferation of cancerous cells, by inducing death of cancerous cells (apoptosis). It has anti-inflammatory properties and has anti-angiogenesic effects (inhibition of the formation of new blood vessels which encourages tumour growth). In fact, it prevents invasion of cancer cells and induces differentiation – the greater the differentiation in cancerous cells, the less likely they are to spread, and be harmful, as testified by Li-Ping Luo, a celebrated Chinese cancer specialist and his research team. The research, in particular, shows that anthocyanins from black rice specifically arrest growth of breast cancer cells. Scaling up
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Daily Global, Regional & Local Rice E-Newsletter A Manipur-based farmer, Potshangbam Devakanta, whose rice germplasm collection is wide, attests to the drought resistance trait of black rice. The farmer received the prestigious ―Protection of Plant Varieties and Farmers Rights Act Award‖ in 2012 for conserving black rice varieties and promoting its cultivation in north-east India. The prime constraint against the spread of black rice is that, as of now, it covers just about 10 per cent of the total cultivated rice area in Manipur. And the prime reason for this poor spread in acreage is its poor yield. Black rice is best suited to organic farming, which consumes much less water than the hybrid rice. But is labour intensive, it works well under the Madagascar-evolved System of Rice Intensification (SRI). SRI has grown in popularity in parts of India where water is a constraint for rice cultivation. Black rice could become a very welcome substitute provided there is a concerted effort by the agriculturel bureaucracy to promote it. In contrast to the situation in Manipur, the situation in Assam is encouraging. Assam‘s agriculture department is going in for massive cultivation of black rice as it brings a premium price as an organic product, and has great potential in overseas markets. Way forward
Other State governments, where rice cultivation is a major enterprise, should take to black rice cultivation. A niche market can be established primarily for export purposes. The recent successful venture of the Union Government at Amuguripara in Goalpara distrct in Assam, where a total of 12 tonnes of black rice was produced in 13.2 hectares, which comes close to a tonne per hectare, is an inspiring example. It shows how, by providing infrastructure, market support and financial incentives, black rice can, indeed, be good bet for Indian rice producers and consumers, domestic as well as foreign. Black rice germplasms must be included in the all-India rice research project based in Hyderabad, which will not only show its potential throughout India, but also open the eyes of enterprising farmers. There is no time to waste. The writer is an agricultural scientist (This article was published on February 9, 2016)
Rice yields tempt traditional canegrowers February 10, 2016 3:00am Bernice KellyTownsville Bulletin
Cane grower Frank Pirrone is happily choosing rice over cane after record setting yields from last year's crop.
THE Burdekin is quickly establishing itself as the nation‘s superior rice growing region, with two local farmers producing some of Queensland‘s highest yields. Allan Milan of Giru and Frank Pirrone of Ayr achieved outstanding results in the 2015 dry season, with both growers producing more than 10 tonnes of rice per hectare. 31 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter
According to food giant SunRice, anything above nine tonnes per hectare is a fantastic result, yet Mr Milan produced an amazing 11.1 tonnes. Mr Pirrone was not far behind with 10.3 tonnes of rice per hectare in his first crop. He is so buoyed by his success that he has already planted a crop for the wet season cycle. ―I really can‘t believe it,‖ he said.
―I didn‘t think I‘d get the results that I have. It has met all my expectations so far. I‘m really rapt about it at the moment.‖ Mr Pirrone believes the soil conditioning benefits of mungbeans helped produce the big rice tonnages. He said he had grown and harvested a crop of mungbeans on the same ground that he later put the rice crop on. ―A lot of people know that when you plant soy, mungbeans or any sort of beans, the ground soaks up horizontally instead of going down and we want it to go horizontally,‖ Mr Pirrone said. ―It‘s only because of the mungbeans doing the job for this crop (of rice) that it‘s allowed this to happen.‖
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Daily Global, Regional & Local Rice E-Newsletter According to Burdekin-based Blue Ribbon Grain and Pulses manager Chris Richards, planting mungbeans or soy beans can improve the soil before the crop of rice is planted. ―It provides a good base for the rice when you‘re going to plant it,‖ Mr Richards said. ―Although it‘s not necessary, it is preferable to put a legume crop in beforehand as it can really help.‖ Burdekin growers are continuing to turn to rice as an alternative crop with the number of rice growers in the region doubling for this year‘s wet season crop. With construction already under way to expand the Brandon Mill, SunRice‘s business development and agronomy manager Rob Eccles said the company was continuing to make plans to develop the rice industry in North Queensland. ―The interest is continuing to increase and, because we‘re constructing the mill and securing more rice crops than ever before, we‘re expecting the industry to continue to grow,‖ he said. ―Rice offers a good cash crop between cane crops and the growers are starting to see this opportunity. ―It‘s a quick crop – only 110 days – and they get paid quickly.‖ http://www.townsvillebulletin.com.au/news/rice-yields-tempt-traditional-canegrowers/newsstory/ca4ec3dce32e3247d8f46723d66f3d9b
Average price of rice down slightly 12:23 AM February 10th, 2016
THE GLOBAL average price of rice declined only marginally as overall food prices fell to a seven-year low in January, according to the Food and Agriculture Organization.Even then, the FAO said ―world rice stocks would need to be drawn down to bridge the expected gap between world production and consumption.‖The United Nations agency said worldwide consumption of the staple grain was expected to expand by 1.1 percent, which would keep stable the average consumption per person. At the start of 2016, the FAO‘s Food Price Index averaged at 150.4 points—three points or 1.9-percent lower than the 153.4 points recorded last December.The index was also 28.5 points or 16 percent lower than the 178.9 points observed in January 2015. More notably, the latest reading was the lowest since April 2009 or close to seven years ago.
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Daily Global, Regional & Local Rice E-Newsletter The index is trade-weighted and tracks prices of five major food commodity groups on international markets, including meat, dairy, cereals, vegetable oils and sugar.―The main factors underlying the lingering decline in basic food commodity prices are the generally ample agricultural supply conditions, a slowing global economy and the strengthening of the US dollar,‖ the FAO said in a statement.The agency said that as of last month, the Cereal Price subindex eased by 1.7 percent ―amid ample global supplies and increased competition for export markets, especially for wheat and maize, as well as a strong US dollar.‖The FAO-administered Agricultural Market Information System added that overall farm conditions for growing rice in the Philippines remained favorable despite the strong El Niño having reached its peak two months ago.―In the Philippines, the dry-season crop conditions are favorable but developing dry conditions are expected to impact the crop next month due to El Niño,‖ the Amis said. Even then, it said the drought was expected to continue in parts of Southeast Asia where rainfall was already way below normal.In January, the Philippine Statistics Authority (PSA) said palay output was expected to ease down by 1.5 percent or 20,000 tons in the first semester of 2016 to settle at 8.2 million tons.The PSA said in its latest production outlook that the volume might decrease as rising yield could fail to offset shrinkage in the area harvested. http://business.inquirer.net/206904/average-price-of-rice-down-slightly#ixzz3zrZusq9V
02/09/2016 Farm Bureau Market Report Rice High Low Long Grain Cash Bids - - - - - Long Grain New Crop - - - - - -
Futures:
ROUGH RICE
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Daily Global, Regional & Local Rice E-Newsletter High
Low
Last Change
Mar '16 1094.0 1068.0 1076.0 -16.0 May '16 1119.5 1099.5 1104.0 -15.0 Jul '16 1146.0 1143.5 1132.0 -15.0 Sep '16
1143.5 -14.5
Nov '16
1164.0 -14.5
Jan '17
1167.5 -14.5
Mar '17
1167.5 -14.5
Rice Comment The selling accelerated in the rice pit today. The monthly supply/demand balance sheet was little changed, but the on-farm price was lowered again. The average expected price for long grain is now $11.00-$11.60/cwt, and mid-south medium grain is expected to bring $11.70-$12.30. March has been working lower and is set up for a retest of the recent low of $10.65
Returning to Greener Pastures in Wayanad BY NANDITA JAYARAJ ON 09/02/2016 •
A paddy field along the Bylakuppe-Wayanad Road. Credit: Nicolas Mirguet/Flickr CC BY-NC 2.0
Wayanad has a rice variety for every situation. Many of them may be on their way out, but local farmers and biodiversity scientists have determined that a panchayat-led effort is our best chance at getting them back. The 2004 Indian Ocean tsunami left paddy farmers in affected countries literally at sea. Rice is one of the most salt-sensitive food crops, and the seawater that swamped fields destroyed a lot of crops. Farmers in lowlands face this challenge every day, but thanks to special salt-tolerant varieties like Pokkali, they are able to tackle the salinity. It‘s in situations like this that the worth of having a pool of rice varieties can truly be appreciated.
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The hilly town of Wayanad in Kerala is known for being particularly rich when it comes to rice biodiversity. ―Wayanad‘s unique climate makes it home to several varieties that can only grow here,‖ says Mareen Abraham, a scientist at Kerala Agricultural University. This includes the GItagged Gandhakasala and Jeerakasala –known to be as aromatic as Basmati – the versatile Veliyan that can thrive in the midst of both droughts and floods, as well as north Kerala‘s Navara, which is known to have several medicinal benefits. Just not worth it? However, rice farming in Wayanad is in crisis. From covering 40,000 hectares in the 1960s, paddy fields today cover merely 8,000-13,000 hectares in the region. With this decline, many traditional rice varieties have already gone completely extinct while some are slowly on their way out. The reason for this trend is common knowledge – rice cultivation is simply not profitable anymore. ―The returns are low compared to cash crops and labour is a big problem,‖ says Prajeesh Parameswaran, a senior scientist at the Community Agro-Biodiversity Centre of the M. S. Swaminathan Research Foundation (CABC-MSSRF) in Wayanad. 36 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter A Nature India article quoted a local farmer from the Paniya tribe saying that while one acre of land can yield them 12 quintals of paddy, worth between Rs 1,400 and Rs. 1,800 per quintal in the markets, the same area of land can get them Rs 3 lakhs by growing banana. Discouraging them even further is the difficulty of obtaining labour. Labourers are opting for less physically demanding jobs under the MNREGA scheme, which, according to Parameswaran does not include agricultural work on paddy fields. Because of this, more and more traditional farmers, comprising, in the main, tribal communities like the Kurichya and the Kuruma, are abandoning the crop that they know inside-out through knowledge passed on over generations. Traditionally, most of these tribes consisted of joint families. ―As the joint family system disintegrates, the fields get continually split up,‖ says Parameswaran. As a result, if some stretches are converted to banana, then neighbouring lands are also pressured into doing so. Naturally banana plantations are taking over. The price of rice As it is, most of the rice consumed in Kerala comes from Andhra Pradhesh and Tamil Nadu. ―This dependency, and the accompanying extra costs, will only increase if fewer farmers are growing the crop in Kerala,‖ says Abraham. This is a shame, she says, because nobody knows the intricacies of farming Wayanad rice like these farmers do – ―not even scientists and researchers.‖ Conserving rice varieties is even more important in the light of climate change. ―In Wayanad itself, rain patterns are changing,‖ said Parameswaran. ―Depending on the conditions, farmers know just which variety to grow.‖ With both the number of rice varieties and the rice farmers dwindling, biodiversity conservers have their work cut out for them. Cultivating rice is not just advantageous for the yield. It is crucial to preserving the unique features of the wetland, like water retention. ―It‘s not as if farmers are unaware of the benefits of conserving their paddy fields. The proof is in the decreasing well water levels,‖ said Parameswaran. ―However, without incentives in place for rice conservators, they will continue to move on to other crops.‖ Plant genome saviours In spite of all this, there are still some farmers in Wayanad who are staying strong and doing everything they can to encourage their peers to do the same. Cheruvayal Raman is one of them. Raman,64, belongs to the Kurichya Adivasi community and has been a farmer for over 50 years. 37 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter In his own way, Raman is doing what he can to encourage his peers to grow rice. He gives away his paddy seeds to other farmers citing only one condition: they have to return to him the exact quantity – a clever tactic to ensure that the recipients cultivate the seeds they are given. In an effort to encourage agricultural stalwarts like Cheruvayal Raman, he has been given the title of ‗Plant Genome Saviour‘ by the government. Obviously, it will take more than awards to make paddy farming attractive again. A significant step to do so was taken by the Indian government in 2001, with the passing of the Plant Varieties Protection and Farmers‘ Rights Act (PVPFRA). This act is known as being one of the most progressive farmers‘ rights acts in the world, primarily because of its holistic view of the farmer as not just the cultivator, but also a breeder and a conserver. Righting the wrongs The PVPFRA came about in response to the urgent need to ensure that farmers were not left without a share of a profits made using their crops and varieties. Once a farmer has registered his or her variety under the act, he or she attains rights to produce or reproduce, offer for sale, distribute, import, export, stock and transfer the rights to any other persons. Organisations like MSSRF-CABC and grass roots associations like Seed Care frequently conduct programmes to make sure farmers avail these rights. They also organise an annual function called ―Seed Fest‖ which aims to ―spread the message of conservation, cultivation and consumption of diverse, safe and healthy plant and animal genetic resources, and by arranging seed exchange, melas and sales‖. The second edition of the fest, which concluded on January 30th, was the fruit of two months of planning. ―We held extensive discussions with farmers and panchayat members from all 23 local panchayats and three municipalities,‖ says Parameswaran. The potential role of the panchayat in biodiversity governance was the main topic of these discussions. It was agreed that an incentive policy to retain farmers needed to come from a local level. At the inaugural function, district panchayat president T. Ushakumari acknowledged the vital service of traditional farmers and agreed that all governing bodies needed to support paddy farmers and foster paddy cultivation. http://thewire.in/2016/02/09/returning-to-greener-pastures-in-wayanad-21105/
USA Rice Continues to Push for Open Trade with Cuba By Deborah Willenborg 38 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter
WASHINGTON, DC -- The U.S. Agriculture Coalition for Cuba celebrated their one-year anniversary here today with a renewed call for action to lift the embargo between the United States and Cuba, and to ease the way for trade for U.S. agricultural products. The press conference featured keynote remarks from U.S. Department of Agriculture (USDA) Secretary Tom Vilsack, who headlined the Coalition kick-off last year and was described as "the first Administration official to publicly vocalize support for the lifting of the embargo, especially for agricultural products." Vilsack spoke about the potential importance of the market, and the value of being able to use promotion check-off dollars in Cuba, and the need to have USDA officials on the ground in Cuba. He said, "Until we have improved relations, we are at a severe disadvantage when it comes to accessing the Cuban market. We need people on the ground in Cuba to talk about our products' quality, quantity, and stability of supply." Of course USA Rice has existing relationships there having participated in the Havana Trade Fair since the 1990's, and meeting with ALIMPORT, the government agency that coordinates all overseas purchases and authorizes the import of products to Cuba, as recently as last fall. Cuban Ambassador José Ramón Cabañas was interviewed at the press conference and several Members of Congress were also in attendance, including rice state legislators Rick Crawford (R-AR), Ted Poe (RTX), and Ralph Abraham (R-LA).
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Daily Global, Regional & Local Rice E-Newsletter
Crawford told the crowd, "We are punishing ourselves [with this embargo]. Now is the time to take action." Poe called for Congress to "lift the financial restrictions...to allow American banks to take the risk and get American agriculture products overseas." And Abraham emphasized the proximity of U.S. infrastructure and products, saying, "With our shipping, trucking, and ancillary services, we can get American products to Cuba in 36 hours."
Prior to the press conference, Vilsack met privately with representatives from the Coalition. Ben Noble, executive director of Arkansas Rice, thanked the Secretary for his support on this issue. Noble also represented USA Rice on a panel with other commodity groups discussing the effects the embargo has had on agriculture. He said, "When the embargo was put in place there were decades where there were no rice sales to Cuba. Back in 2000 when the law was changed to allow cash sales, we saw an increase in activity. Unfortunately, that opportunity was shut down and we lost one of our top export markets." He concluded, "USA Rice continues to support all legislative efforts to lift the trade embargo with Cuba that will allow for free and unfettered trade."
President Releases FY 2017 Budget Recommendations, Not Very Ag-Friendly By Peter Bachmann
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Daily Global, Regional & Local Rice E-Newsletter WASHINGTON, DC -The President's recommendations for the Department of Agriculture's (USDA) funding were up in some program areas and down in others with net cuts to the Department's activities by more than $4.5 billion, down seven percent from the 2016 level. Crop insurance was once again a target of the Administration's proposal with $18 billion worth of cuts over ten years. Specifically, the budget proposal suggests a 10 percent reduction in premium subsidies for policy holders electing the harvest price option and additional savings would be generated by reforming prevented planting coverage.Also recommended were $20 million in cuts to the McGovern-Dole Food for Education program and $116 million in cuts to the P.L. 480 (Food for Peace) program which provides food aid procurement and delivery. On a positive note, the budget calls for doubling of the Agriculture and Food Research Initiative, the USDA's competitive grant program to $700 million for FY 2017. Also, for the first time a $1.5 million line item for staffing USDA personnel at the recently opened U.S. Embassy in Havana, Cuba.Dr. Steve Linscombe, rice breeder and USA Rice member who serves as regional director of the Louisiana State University AgCenter in Crowley, said, "I'm pleased to see support for the agriculture research field. Agriculture and Food Research Initiative grants definitely help keep the LSU AgCenter's projects going."The Administration's budget recommendations are not enforceable and largely used as messaging to Congress. The House and Senate Budget Committees are not expected to invite the Administration to Capitol Hill to present their recommendations, signaling widespread disapproval.
Genome-wide prediction models that incorporate de novoGWAS are a powerful new tool for tropical rice improvement OPEN J E Spindel1, H Begum2,4, D Akdemir1, B Collard2, E Redo単a2,5, J-L Jannink1,3and S McCouch1 1. 2.
1
Department of Plant Breeding and Genetics, 240 Emerson Hall, Cornell University, Ithaca, NY, USA Department of Plant Breeding, Genetics and Biotechnology, International Rice Research Institute, Los Ba単os, Philippines 3. 3USDA-ARS, North Atlantic Ares, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, USA 2
Correspondence: Professor S McCouch, Plant Breeding and Genetics, 240 Emerson Hall, Cornell University, Ithaca, NY 14853, USA. E-mail: srm4@cornell.edu 4
Current address: Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh. 41 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
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Current address: Delta Research and Extension Center, 82 Stoneville Road, PO Box 197, Stoneville, MS 38776, USA. Received 24 June 2015; Revised 17 November 2015; Accepted 25 November 2015 Advance online publication 10 February 2016 Topof page Abstract
To address the multiple challenges to food security posed by global climate change, population growth and rising incomes, plant breeders are developing new crop varieties that can enhance both agricultural productivity and environmental sustainability. Current breeding practices, however, are unable to keep pace with demand. Genomic selection (GS) is a new technique that helps accelerate the rate of genetic gain in breeding by using whole-genome data to predict the breeding value of offspring. Here, we describe a new GS model that combines RR-BLUP with markers fit as fixed effects selected from the results of a genome-wide-association study (GWAS) on the RR-BLUP training data. We term this model GS + de novo GWAS. In a breeding population of tropical rice, GS + de novo GWAS outperformed six other models for a variety of traits and in multiple environments. On the basis of these results, we propose an extended, two-part breeding design that can be used to efficiently integrate novel variation into elite breeding populations, thus expanding genetic diversity and enhancing the potential for sustainable productivity gains. Topof page Introduction
Rice (Oryza sativa) is a model species for crop genomics. In 2002, rice became the first crop species to have its genome sequenced (Goff et al., 2002), and it remains one of the best characterized crop genomes due to its small size, abundant genetic variation and high-quality sequence data (Ronald and Leung, 2002; Huang et al., 2013). Since the sequencing and annotation of the rice genome, there have been >4278 publications on rice genetics, ~3000 genes have been structurally and/or functionally annotated (Ouyang et al., 2007) and 3000 rice genomes have recently been sequenced at ~10X coverage (Alexandrov et al., 2014). Although these numbers represent great progress for rice biology, they have so far had little impact on rice agriculture (Bernardo, 2008). One of the major challenges facing the agricultural community as it seeks to integrate genomic information into applied plant improvement has been that until recently genotyping was expensive and laborious (Desta and Ortiz, 2014;Varshney et al., 2014; Lipka et al., 2015). As a result, genomic applications were constrained by the number of marker data points that could be assayed per generation in large breeding populations. Consequently, most of the characterized genes in rice are those associated with Mendelian traits, that is, traits controlled by a few genes
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Daily Global, Regional & Local Rice E-Newsletter of large effect. The majority of agronomic traits of interest to plant breeders, however, are quantitative and polygenic, governed by many genes of small effect. This paradox has determined how molecular markers and genomic information have been applied to breeding programs. In particular, it has lent itself to marker-assisted selection (MAS) in rice as well as in a variety of other species. In all cases, markers in critical large effect genes or genomic regions are used to predict the presence or absence of agriculturally valuable traits (Collard et al., 2005; Bernardo, 2008; Collard and Mackill, 2008). Although MAS contributed to shortening the time required to develop and release new rice varieties, from ~12 years to ~6 years, it is predicated on prior knowledge about major-effect genes and quantitative trait loci (QTLs) that serve as the targets of selection. Furthermore, MAS is generally used to introduce one gene at a time into an existing and highly valued variety. It is not designed to manage the recombination of many genes simultaneously (Collard and Mackill, 2008; Desta and Ortiz, 2014). Genomic Selection (GS) offers an alternative to MAS and conventional phenotypic selection. GS has the capacity to use full-genome data to increase breeding efficiency. In GS, a training population is phenotyped for a trait(s) of interest and genotyped using genome-wide markers. A statistical/machine learning model is built from the training population data that take the genotypes of individuals in a breeding population as input and outputs a measure of the value those individuals hold as parents of future breeding – the genome estimated breeding value (GEBV; Meuwissen et al., 2001; Lorenz et al., 2011). GS improves breeding efficiency by providing GEBVs for all individuals in a population, thus enabling the breeder to make informed decisions about which individuals to use in crossing or in allocating phenotyping resources among individuals and generations. Appropriate use of GEBVs saves time, effort and money in a breeding program by reducing the requirement to phenotype the entire population every generation, increasing the proportion of top performers in the breeding population, and enabling selection based on allele rather than line means (Heffner et al., 2009; Bernardo, 2010). It can also accelerate the delivery of new varieties by keeping the breeding pipeline full of high-quality material. Unlike in MAS, in GS even infinitesimally small effect alleles contribute to model development, and are thus tracked and accounted for by the model. As a result, many loci can be under selection simultaneously. GS is known to be effective for maize and small grains, and recent studies suggest it will also be useful for rice (Asoro et al., 2013; Massman et al., 2013b; Crossa et al., 2014; Onogi et al., 2015; Spindel et al., 2015). Depending on the crop, trait, and breeding population design, the choice of statistical method used to build the GS model has been shown to have a significant effect on prediction accuracy (Daetwyler et al., 2013). Interestingly, for breeding crops such as rice and wheat, in which large effect QTL are common, models that incorporate a select number of molecular markers as fixed effects have been shown to contribute to improved prediction accuracy. Bernardo (2014) first proposed based on simulation experiments that when 1–3 major genes for a trait are known and each accounts for 10% of the phenotypic variance that these genes should be included as fixed effects in GS models (Bernardo, 2014). Empirical results by Rutkoski et al., 2014 confirmed the utility of the strategy for stem rust resistance in a wheat breeding population, whileOwens et al. (2014) showed that it was effective for predicting pro-vitamin A content in a maize diversity 43 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter panel. Other groups showed similar utility by weighting known genes of large effects in wheat (Bentley et al., 2014; Zhao et al., 2014). It is currently unknown, however, how including markers as fixed effects will improve rice GS models. A second open question is how markers to be fit as fixed effects should be identified and/or selected. The previously cited experiments have, for the most part, relied on known functional markers for traits of interest (Bentley et al., 2014; Owens et al., 2014; Rutkoski et al., 2014; Zhao et al., 2014), or propose using previously published genome-wide association study (GWAS) results (Zhang et al., 2014). Here, we propose for the first time directly using only the results of GWAS run using GS training population data, a method we are calling ‗GS + de novo GWAS‘, or simply ‗GS+GWAS‘. There are numerous benefits to this approach. Both GS and GWAS use the same input data sets, a phenotype data set and a genotype data set, thus no additional data are required, only additional analysis. Furthermore, we hypothesize that the significant single-nucleotide polymorphisms (SNPs) identified from de novo GWAS will be more directly relevant to the population undergoing selection than SNPs identified as significant in previously published GWAS on potentially disparate populations, and thus will improve prediction accuracy beyond that which could be obtained using previously published GWAS results. Using de novo GWAS as part of the GS model is also more accessible to breeders, as it does not require an extensive knowledge or literature search on the underlying genetics of a trait of interest. There are several points in a standard pedigree breeding program where an intervention based on GS could significantly shorten the breeding cycle by eliminating a generation of phenotyping or providing the breeder a mechanism for eliminating poor-performing offspring before the next generation of costly field trials (Spindel et al., 2015). In this study, we assess the potential of introducing fixed variables identified using de novo GWAS into GS models to improve prediction accuracy as compared with GS models that make use of historical GWAS data or other standard GS models, and also consider the contribution of multi-location field trials to GS prediction accuracy. Using available multi-environment trial (MET) data from the International Rice Research Institute (IRRI) irrigated rice breeding program, we extract information from our models to evaluate which of currently used environments in IRRI's MET program can be combined for the purposes of model training, and thus define the best combination of environments for trait modeling and prediction in Southeast (SE) Asia. The current study was undertaken as part of a rigorous evaluation of the irrigated rice breeding program at IRRI, and was specifically designed to investigate opportunities for integrating GS into that program. The data sets presented here were collected in eight locations in SE Asia between 2009 and 2012, and are used to highlight opportunities to use existing unbalanced phenotypic data sets in combination with genotyping-by-sequencing genotype data to develop and optimize GS models. Given that the cost of genotyping is continuing to decline while costs of field-based phenotyping are high and generally increasing, it is time for the rice breeding community to consider if and how GS can be implemented in rice breeding programs and to define the pilot breeding studies that can be used to help test and transition to genomics-assisted selection methods. Topof page 44 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter Materials and methods Plant material and phenotyping
In all, 369 elite breeding lines (F6–F7) were selected for genotyping from the IRRI irrigated rice breeding program based on the planned inclusion of the lines in the 2011 Multi-Environment Testing Program and presence in the 2011 and 2012 Replicated Yield Trials (RYT) at IRRI (Los Baños). Two phenotype data sets were used in this study, (1) the RYT data set, consisting of field data from 2009–2012, two seasons per year (dry season (DS) and wet season (WS)) collected in a single field at IRRI in Los Baños, Philippines, and (2) the MET data set, consisting of field data from 2011 and 2012, two season per year (dry and wet), at a total of eight sites in SE Asia (Table 1). Table 1 - Summary of the two data sets used in this study.
Full table
For the RYT data set, phenotypes collected included plant height, flowering time, maturity date, number of effective tillers or panicles per plant, lodging score, grain yield and rep number (Supplementary Materials and Methods). For the MET data set, in addition to the phenotypes collected for the RYT data set, data were collected on field row, field column, phenotypic acceptability score for whole plant, phenotypic acceptability score for panicle and phenotypic acceptability score for grain (Supplementary Materials and Methods). The eight sites at which the MET data were collected to compose IRRI's target population of environments for irrigated rice in SE Asia including IRRI/Los Baños (‗MET field‘), Isabela, Nueva Ecija, Agusan del Norte, Bohol, and Midsayap—all in the Philippines, Batalagoda, Sri Lanka, and Hai Dong, Vietnam. Data were highly unbalanced (Table 1; Supplementary Materials and Methods). Genotyping
Genotyping by sequencing was performed on 369 breeding lines as described inSpindel et al., 2015 (Spindel et al., 2015). For details on analysis and filtering see the Supplementary Methods. The cross-validation (CV) results reported inSupplementary Tables S2 and S4 were obtained using a genotype data set consisting of 108,005 SNPs with call rates 0.75 and monomorphic SNPs removed. CV results reported in Supplementary Table S3 were obtained using subsets of this SNP data set after removing SNPs with minor allele frequency <0.05 (subsets were thus 45 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter selected from a total of 58 318 SNPs, minor allele frequency filtering was performed for purposes of making GWAS more robust), for details on selection of SNP subsets, see 'CV using SNP subsets' below. For all data sets, after SNP filtering, individuals with >60% missing data were dropped from the data set, which resulted in the removal of six individuals that failed sequencing for a total of 363 genotyped lines. Heritabilities (Table 1) were calculated using a subset of the 108 005 SNPs with call rates 90%. Subpopulation and family structure analysis and CV fold design
The majority of the 363 lines were characterized a priori from pedigree records as belonging to the indica or indica-admixed subpopulation groups. To identify outlier individuals belonging to the japonica or japonica-admixed groups, principal components analysis was performed in R (version 3.0.1; https://www.r-project.org) using a 73 147 SNP subset of the 108 005 SNPs used for GS with imputed call rates 0.9 (remaining missing data were then filled using the line means). The results of the principal components analysis were used to identify 31 subpopulation outliers belonging to either the japonica subgroup or containing substantial japonica admixture. These 31 outliers were removed from the data set and were not included in any further analyses (Spindel et al., 2015). It was also known from studying the breeding program pedigrees that differing degrees of family relatedness existed within the remaining 332 lines, including half sibs, full sibs, parents and offspring, and unrelated lines. The presence of highly related individuals in the data set could have the effect of artificially inflating prediction accuracy if the most closely related individuals are randomly assigned to different folds, and one of those folds is then used as training, whereas the other is used as testing. To control for this possibility when designing our folds, we performed a partitioning around k-medoids analysis (pamk) using the R fpc package (function pamk; https://cran.r-project.org/web/packages/fpc/index.html) with the 73 147 imputed SNPs (Hennig, 2015; Kaufman and Rousseeuw, 1990). The largest average silhouette width was found to occur at k=87, so individuals found within the same cluster of 87 were assigned to the same fold, making it impossible for the most closely related individuals to be split across training and testing folds. Full clusters were assigned to one of five folds randomly, controlling only for cluster size to produce three folds of 66 individuals and two folds of 67 individuals. A similar procedure was used by Ly et al. (2013). CV experimental design
For each CV experiment, one of the five folds served as the validation fold, and the other four folds served as the training folds. The process was repeated five times so that each fold served once as the validation fold, resulting in predicted GEBV values for all individuals. Accuracy was assessed as the mean Pearson correlation of the predicted GEBV and adjusted phenotype in the validation population. For the RYT data, CV experiments were performed to test all logical combinations of years and seasons in the training and validation populations. A year's WS was never used to predict the same year's DS because in SE Asia, the DS arrives first chronologically. We did, however, predict the 2012 WS both with and without the preceding 2012 DS present in the training population. We tested scenarios in which both seasons per year were included in the training population, as well as scenarios where only the data from the seasons matching the validation population were included in the training data (for example, using 46 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter only the WS data to predict the WS). We also sought to test scenarios using only more recent year data in the training population (for example, only 2011, or 2010–2011) and scenarios using more historical year data in the training population (for example, 2009–2011; Supplementary Table S1A). The same logic was applied to the combination of years and seasons for the different MET CV experiments. In addition, the MET experiments were varied in terms of the site composition in the training population. In addition to CV experiments in which all sites were combined into a single training population and only the validation site was used to compose the training population, other combinations of MET sites were chosen based on (1) the geographic location of the sites, (that is, more northern sites were placed together, more southern sites were placed to together, where sites in between the northernmost and southernmost sites were tested in a variety of groupings) and (2) phenotypic data correlation, that is, if sites appeared to be correlated for any of the phenotypes of interest, they were tested in combination (Supplementary Table S1B). Note that Vietnam was excluded from all experiments except the 'validation site only' experiments because it was not correlated with any other site (Supplementary Figure S1). The 2012 Isabela DS was both included and excluded in any CV experiment in which it would normally have been included because it also was not correlated with any other site or season, including other Isabela years/seasons (Supplementary Figure S1). For additional details on MET experiments, calculation of adjusted phenotypes for validation folds and correlation analyses and inclusion of validation population year/season in training population, see Supplementary Materials and Methods. GS modeling
Seven statistical methods were used for each RYT experiment, including six GS methods: GS + de novo GWAS, GS + historical GWAS, RR-BLUP, Bayesian LASSO (BL), Reproducing Kernel Hilbert Spaces (RKHS) and random forest (RF), and one non-GS method: multiple linear regression (MLR). The four non GS+GWAS GS statistical methods were chosen based on their demonstrated success in accurately predicting GEBVs in variety of crops and because they represent the different types of statistical methodologies used to build GS models, that is, linear parametric methods (RR-BLUP, BL), non-linear semi-parametric methods (RKHS), non-linear, non-parametric methods (RF), as well as maximum-likelihood methods (RR-BLUP, RKHS), Bayesian methods (BL) and machine learning methods (RF; Breiman, 2001; Gianola and van Kaam, 2008; Gianola et al., 2009; Heslot et al., 2012; Perez-Rodriguez et al., 2012; Rutkoski et al., 2012; Crossa et al., 2014). For an overview of the methods, see Lorenz et al.(2011). MLR using a subset of markers derived from single marker regressions (MLR) served as our non-GS marker-based prediction control. For each fold, single marker regression was run for all markers and P-values determined for each marker by F-test. Note that this is statistical equivalent of a crude GWAS. Linear models were then tested using 1 through the first 100 most significant markers, and the model with the best fit was returned. The returned model was then used to calculate the accuracy for the given fold. For the marker subset experiments where the number of markers in the subset (p) was <100, models were tested using 1 through p markers. Note that our MLR methodology was modified slightly from Spindel et al., 2015 to be more conservative: the validation data were not used in this case to calculate model fit, only the 47 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter training data were used. This resulted in markedly lower MLR prediction accuracies than those previously reported, particularly for flowering time (Supplementary Materials and Methods). For the RYT experiments, three CV accuracies were calculated. CV1 accuracies that are also the accuracies reported for the MET data and the CV experiments using marker subsets, were calculated by including the validation year/season in the training population, excluding individuals in the validation fold, for example, for experiment 1, CV1, the training population consisted of data on all training population individuals from years 2009 to 2011 all seasons, as well as the 2012 (the validation season; Supplementary Table S1A). For an explanation of CV2 and CV3, see the Supplementary Materials and Methods 'Inclusion of validation population year/season in training population'. Narrow sense heritabilities (Table 1) were calculated for each trait in each season, year and site (MET data only) on a per line basis using the rrBLUP package (Endelman, 2011; https://cran.rproject.org/web/packages/rrBLUP/index.html), function mixed.solve, with the least square means for the complete population used as input. The narrow-sense heritabilities were calculated as the additive genetic variance divided by the total phenotypic variance. RR-BLUP+Fixed effects model
When no markers are included as fixed effects, the model is equivalent to standard RR-BLUP (Equation 1), where y is the vector of observations, X is an incidence matrix for fixed effects containing only a vector of 1s for the intercept, β is a vector of fixed effect estimates containing the intercept, Z is an incidence matrix for random effects relating individuals to observations and u is a vector of random individual effects with u ~ N(0, Gσ2μ), where G is a genomic relationship matrix calculated using all markers (Endelman, 2011). When up to four markers are added to the model as fixed effects, their allele dosages are added as columns to the X matrix and β expands accordingly. The markers are then also removed from the calculation of G. All aspects of model fitting otherwise remain the same.
Selection of fixed effects for RR-BLUP+fixed effects models
The markers fit as fixed effects were selected from GWAS output, either from a GWAS calculated using the genotype and phenotype data on the individuals in the training population (GS + de novo GWAS) or from previously published GWAS data (GS +historical GWAS). For selection of fixed effects using de novo GWAS, the algorithm described below was used for each CV experiment: 1. Run GWAS using Genome-wide Efficient Mixed Model Association (GEMMA) five times, once for each validation fold (Zhou and Stephens, 2012). The input to GEMMA consists of genotype 48 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter
2.
3.
4.
5.
and phenotype data on the individuals in the combined four training folds. Data on individuals in the validation fold is not included in the GEMMA input. Sort the GWAS output by P-value (low to high) and perform multiple-test correction using False Discovery Rate (FDR), then bin the SNPs on each chromosome into 500 Kb bins and output the lowest P-value SNP in each bin. This step was performed to group SNPs into GWAS peaks—as this was a breeding population with extensive linkage disequilibrium (LD) (Supplementary Figure S2), peaks were large, often spanning ~500 Kb. In other population types, the bin size would need to be modified, most likely decreased, to account for smaller peak size or lower LD. FDR was performed for all SNPs using the R p.adjust() function, method=‗BH‘ (=benjamini hochberg; Benjamini and Hochberg, 1995). For each fold, load respective GEMMA fold output(s) and save up to the three most significant SNPs (FDR=0.1) for the trait of interest. If no SNPs pass FDR, save only the lowest P-value SNP. If only one or two SNPs pass the FDR threshold, save only the SNPs that pass the threshold. Save also the single most significant SNP for flowering time. Test the markers saved from 3. in all combinations and select the markers that, by themselves, constitute the best linear fit using only the training data, that is, select the combination of markers that results in the maximal correlation between the phenotype training data and a prediction resulting from a linear model of the selected marker genotypes. Calculate the average of the FDR corrected P-values of the selected fixed effect SNPs=average corrected P-value for model. Proceed with model solving and validation phenotype prediction using markers selected in 4.
For the RYT data set, both the 2012 DS and 2012 WS phenotype data were used as the input for GEMMA for each CV experiment. For the MET data set, the 2012 DS RYT data, 2012 WS RYT data, and the 2012 MET phenotype data for the respective validation year, site and season of a given CV experiment were tested as the input to GEMMA for each CV experiment. Note that we tested models both with and without the lowest P-value SNP for flowering time (the 'fourth' marker added to the yield and plant height top three markers) and found that the differences between these models were generally small, although in most cases including the flowering time SNP as a fixed effect improved model accuracy. Given that flowering time alone can shift performance of other agronomic traits, it is always worth controlling for its effect in breeding populations that encounter significant differences in flowering time, as is typical in many rice breeding programs. As SNP selection was performed for each fold, the RRBLUP+fixed effects models differed slightly in terms of the markers fit as fixed effects by fold. The above methodology was appropriate in our case because we had already analyzed the GWAS results on the training population data. For a group that wished to replicate our methodology on a new population, we recommend first running GWAS on their training population data to broadly visualize the trait genetic architecture. If a trait has no significant GWAS peaks or peaks that are very near the significance threshold after applying multiple-test correction, the above methodology is not recommended. This GS+GWAS method is intended for traits with one or more medium–large effect QTL segregating in the population. In other words, if random forest is NOT at all predictive for a given trait in a given population, the GS + de novo GWAS method presented here will also most likely be unsuitable. For a detailed analysis of the GWAS results by themselves, including candidate gene analysis, see Begum et al. (2015).
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Daily Global, Regional & Local Rice E-Newsletter For the GS + historical GWAS models, the same procedure was used as for the GS + de novo GWAS models, except the input to step two of the above algorithm (the GWAS results) was derived from the literature, in our case, from the results of Zhao et al. (2011). The RYT CV results (reported in Supplementary Table S2) were analyzed using analysis of variance (ANOVA) and pairwise Student's t-test (that is, Student's t-test was performed for each pair of group levels testing only individual comparisons) to determine the effect of statistical method, validation population, and composition of the training population on prediction accuracy. The MET CV results (reported in Supplementary Table S4) were analyzed using ANOVA and pairwise Student's t-test to determine the effect of statistical method, combination of sites in the training population, validation site and combination of seasons/years in the training population on prediction accuracy (Supplementary Materials and Methods). Analysis of linkage disequilibrium
Pairwise LD matrices were calculated separately for each chromosome using PLINK (http://pngu.harvard.edu/purcell/plink/). Heat maps were generated using Python3 Matplotlib.pcolormesh (http://matplotlib.org) (Supplementary Figure S2). CV using SNP subsets
SNP subsets that were chosen to be evenly distributed across the genome (distributed) or chosen at random were selected from the 58,318 genotyping-by-sequencing SNPs with call rates 75% and minor allele frequency 0.05 as described in the Supplemental Materials and Methods. For each marker subset, a genotype matrix for each of the five validation folds was constructed. These genotype matrices were used in conjunction with the phenotype data on the training population individuals to run GEMMA for each subset, for each fold. The GEMMA GWAS results were then used as described in the section 'RR-BLUP +Fixed effects model' above with the marker subset genotype matrices to run GS +de novo GWAS CV for each marker subset. RR-BLUP was also run using the marker subsets as a means of comparison. CV was run using the best performing experiments for each validation season—the same experiments that are reported in Supplementary Table S2A and Figures 1 and 2; see Supplementary Table S3. For the GS + de novo GWAS models, the RYT 2012 DS data were used for the phenotype input for flowering time, and the RYT 2012 WS data were used for the phenotype input for plant height and grain yield, as these produced the best GS + GWAS models using the full genotyped data set (Figure 1; Supplementary Table S2A).
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Daily Global, Regional & Local Rice E-Newsletter Figure 1.
Cross-validation prediction accuracies of flowering time (FLW, top), plant height (PH, middle) and grain yield (YLD, bottom) in the RYT data set, comparing GS + de novo GWAS models (blue) to RR-BLUP (yellow) and random forest (RF) (green) models, left axis. Plots show the results using the optimized training population for prediction of each trait in the RYT 2012 dry season (DS) and RYT 2012 wet seasons (WS) (that is, the cross-validation experiment that resulted in the best prediction accuracy for each trait in each validation season, see Supplementary Table S2A). GWAS for the GS + de novo GWAS models were run using both the RYT 2012 DS data (light blue) and the RYT 2012 WS data (dark blue). Percent decrease in accuracy of RRBLUP and RF models versus the average of the two GS + de novo GWAS models (FLW), or the GS + de novo GWAS WS model are shown over the RR-BLUP and RF bars, respectively. Bars not labeled with the same letter (Pairwise Student‘st-test) indicate a significant difference in accuracy of the statistical methods across all experiments. Red X's mapped to the right axis=−log* average P-value (using the Wald test) of the SNPs fit as fixed effects in the GS + de novo GWAS models, after FDR multiple-test correction. Full figure and legend (136K)
Figure 2.
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Daily Global, Regional & Local Rice E-Newsletter Comparison of GS + de novo GWAS with GS + historical GWAS models for flowering time (FLW, top), and plant height (PH, bottom). Graphs shows the results using the optimized training population for prediction of each trait in the RYT 2012 dry season (DS) and RYT 2012 wet seasons (WS; that is, the cross-validation experiment that resulted in the best prediction accuracy for each trait in each validation season; seeSupplementary Table S2A). GS + GWAS models differed in the GWAS data used to select the SNPs fit as fixed effects. GS + de novo GWAS: 2012 DS (light blue)=de novo GWAS using 2012 DS data on training population individuals, GS + de novo GWAS: 2012 WS (dark blue) =de novo GWAS run using 2012 WS data on training population individuals, GS + historical GWAS: 44K all (red)=previously published (historical) GWAS data were used from Zhao et al., 2011 the 'all subpopulations' results, GS + historical GWAS: 44K indica (burnt orange)= the indicasubpopulation results from Zhao et al. 2011 were used, GS + historical GWAS: 44K TRJ (green)=the tropical japonica results from Zhao et al.(2011) were used. Bars not labeled with the same letter indicate a significant difference in model accuracies across all experiments. Full figure and legend (100K)
Accuracy was calculated for each of the 10 SNP subsets. A mean accuracy and s.e. for each subset size were also calculated by averaging the CV results of the 10 subsets for each subset size. ANOVA and Pairwise Student's t were used to determine the effect of SNP number, SNP type (that is, random or distributed) and statistical method on accuracy (Îą=0.05). Figure 3 plots the accuracy of CV1 for random and distributed SNPs for each validation season and were created using JMP v 12.0 (SAS, Cary, NC, USA). Figure 3.
Mean accuracies of cross-validation for prediction of flowering time (FLW, top), plant height (PH, middle) and grain yield (YLD, bottom) in the 2012 dry season (left), and the 2012 wet season (right), using 10 selections of SNP subsets chosen to be either distributed evenly throughout the genome (light shades) or chosen at random (dark 52 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter shades); left axis. The best performing GS + de novo GWAS models (blues), as well as RR-BLUP models (oranges) and previous best performing CV experiments were run for each trait, see Supplementary Table S2A. Right axis (blue X's)=–log * average P-value (Wald test) of the SNPs fit as fixed effects in the GS + de novo GWAS models, after FDR multiple-test correction. All error bars were construed using 1 s.e. of the mean. Full figure and legend (218K)
Topof page Results
Building on recently published studies reporting the results of GWAS and GS in a population of breeding lines from the IRRI irrigated rice breeding program (Begum et al., 2015; Spindel et al., 2015), we investigated the possibility of improving GS prediction accuracy through model refinement by (a) incorporating markers as fixed effects derived from a GWAS performed using the training data set itself (GS + de novo GWAS), (b) markers as fixed effects extracted from the literature (GS + historical GWAS) and (c) adding data from multiple environments to the training population. Two data sets consisting of 108 005 SNPs on ~363 elite irrigated rice breeding lines were used, a RYT data set consisting of 4 years of data (2009–2012), two seasons per year (dry and wet), taken at a single site at IRRI in Los Baños, Philippines, and a multienvironment trial (MET) data set, collected over two years (2011–2012, DS and WS per year) at four locations in 2011 and eight locations in 2012 (Table 1; Methods). For both data sets, we focused on prediction of three traits that differed in their genetic architecture, as shown by the results of GWAS run on the same data sets: flowering time (FLW), a trait controlled by a few large effect QTL, grain yield (YLD), a trait controlled by many small effect QTL, and plant height (PH), a trait controlled by both large and small effect QTL (Begum et al., 2015; Spindel et al., 2015). In the RYT data set, narrow-sense heritabilities ranged from 0.27 to 0.44 for FLW, 0.24 to 0.39 for PH and 0.07 to 0.32 for YLD depending on the year and season, while in the MET data set, heritabilities range from 0.05 to 0.64 for FLW, 0.0 to 0.52 for PH, and 0.0 to 0.74 for YLD, depending on the year, season and site (Table 1). Use of fixed effects extracted from the training population to improve accuracy of GS models (GS + de novo GWAS)
One means of boosting GS prediction accuracies is to incorporate additional genomic and/or biological information, such as that revealed in a GWAS, into the GS model. To exemplify how such integrated models can improve performance, we performed CV using the RYT data set. For all CV experiments, we developed RR-BLUP models in which 1–3 of the most significant SNPs identified by fold-specific GWAS (run using the 2012 phenotype data on individuals in the training population) were included as fixed effects (model=GS + de novo GWAS;Supplementary Table S2, Methods section). GWAS were run using both the 2012 DS and 2012 WS data, and for 53 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter every cross-validation experiment, two models were tested, one in which the most significant SNPs (binned on a 500 Kb basis) from the DS GWAS were tested for incorporation as fixed effects, and one in which the most significant binned SNPs from the WS GWAS were tested. The results of the GS + de novo GWAS were compared with 1. GS + historical GWAS models, in which the markers fit as fixed effects were selected from previously published GWAS data, and 2. the five other genotype-based prediction methods previously tested in this population: RRBLUP without any fixed effects, RKHS, random forest (RF), Bayesian LASSO, and multiple linear selection (MLR; Methods section; Spindel et al., 2015). The results of all experiments are given in Supplementary Table S2B. The effect of training population composition and validation population on prediction accuracy in this population has been discussed elsewhere (Spindel et al., 2015), and is presented in Supplementary Table S2B for the sake of completeness. We focus here on the effect of statistical method on accuracy in the best performing cross-validation experiments, that is, the combination of training years/seasons that, on average, produced the best prediction accuracies. Across all traits and experiments, the most accurate statistical methods of those tested were the GS + de novo GWAS models (Figure 1; Supplementary Table S2). For the best performing CV experiments for each trait and season (Supplementary Table S1A) the GS + de novo GWAS using the 2012 WS data as input to the GWAS outperformed simple RR-BLUP in all cases (Figure 1;Supplementary Table S2). The percent improvement ranged from ~29.8% for FLW in the DS, to ~7.0% for PH in the WS (Figure 1; Supplementary Table S2A). Furthermore, for all traits and seasons, the GS + de novo GWAS model (using the 2012 WS data for PH and YLD) was also the most accurate overall, outperforming RF, the next best performing model for some trait x season combinations (Figure 1; Supplementary Table S2A). These gains were generally modest, ranging from ~12.8% for YLD in the WS to ~7.7% for FLW in the DS. Although not all differences were significant (Figure 1; Supplementary Table S2), these results demonstrate that identifying markers that tag important genes and adding them as fixed effects to GS models can enhance GEBV prediction, sometimes markedly. In no case did adding fixed markers identified chosen based on the GWAS of the 2012 WS data decrease accuracy relative to RR-BLUP or any other tested statistical method (Figure 1; Supplementary Table S2). It is not entirely clear why the 2012 WS data were a more effective source of fixed SNPs for plant height and grain yield than the 2012 DS data, however, a few possibilities exist. For YLD, the best explanation is that the most significant SNPs identified using the 2012 WS data were considerably more significant after multiple-test correction than those identified using the 2012 DS data (Figure 1;Supplementary Table S2). Although all SNPs included in all models were significant by FDR=0.1, if the significance threshold were to be raised slightly, some SNPs would have been dropped (Figure 1). Furthermore, for YLD, decreased average corrected Pvalue of the SNPs fit as fixed effects correlated with decreased prediction accuracy. This was not the case, however, for plant height, where all SNPs fit as fixed effects were well above the FDR threshold, and where using the 2012 DS data as the GWAS input resulted in lower GS prediction accuracies, but higher average corrected P-values of fixed effect SNPs. It thus does not seem likely that the significance of the GWAS results was a contributing factor in the improved 54 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter performance of the 2012 WS data for plant height (Figure 1). Instead, it is possible that stochasticity in the data resulted in more informative QTL being identified in the WS than in the DS, or that increased disease pressure in the WS resulted in more reliable prediction from wet to dry than vice versa. Overall, the results suggest that when there are highly significant peaks identified in a GWAS, adding markers that tag these GWAS peaks as fixed effects in an RRBLUP+ fixed effects models improves accuracy over those obtained from more complex models like RF. Use of fixed effects extracted from the literature to improve accuracy of GS models
To determine whether significant GWAS-SNPs identified for the same traits but using different germplasm would be equally useful as fixed variables in our GS models, we compared prediction accuracies for flowering time and plant height of the above GS + de novo GWAS models to three additional RR-BLUP+fixed effects GS models in which the fixed SNPs were selected using GWAS data fromZhao et al. (2011). (There were no previously published GWAS data available for YLD, so it was not possible to compare the results for this trait.) The three additional models tested utilized SNPs identified by GWAS in a rice diversity panel representing (1) the indica subpopulation, (2) the tropical japonicasubpopulation, and (3) the all, or combined subpopulations (Zhao et al., 2011). As our training population consisted of only indica individuals (Methods section;Spindel et al., 2015), this allowed us to test, in addition to the effect on model accuracy of using previously published GWAS results, the effect of using previously published GWAS results derived from individuals from the same subpopulation undergoing selection in breeding, versus the effect of using GWAS results derived from individuals from different subpopulations than the one undergoing selection (Figure 2; Supplementary Table S2). For flowering time, using GWAS results derived from the training population proved to be significantly more accurate than using any of the historical GWAS results. For the DS, it made little difference which historical GWAS data were used, all data sets performed badly. For the WS, the all subpopulation results were significantly better than using either the indica only or tropical japonicaonly results, but again, all were significantly worse than using either the 2012 DS or 2012 WS data. (Figure 2; Supplementary Table S2). For plant height in the DS, using the 2012 WS data resulted in significantly more accurate GS models than using the historical GWAS data, but for the WS, the all subpopulation historical data performed about as well as the 2012 WS data (Figure 2; Supplementary Table S2). In essence, these results suggest that in some cases, the researcher may get lucky when utilizing previously published results, that is, in some cases, historical GWAS results will be relevant to a given breeding population, as was the case for plant height in our WS. In other cases, however, such as for flowering time in this breeding population, SNPs identified in previously published GWAS will not be relevant to a given population, and will thus decrease accuracies relative to simple RR-BLUP models. Regardless of whether previously published results might perform as well as de novo results, in our experiment, the previously published GWAS data never improved model accuracy over the GS+ de novo GWAS models, thus, there appears to be no reason to pursue this strategy (Figure 2; Supplementary Table S2). 55 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter Number of markers and GS accuracy
We also tested the accuracy of the best performing GS + de novo GWAS models at decreasing numbers of genome-wide markers. (GWAS were also run with the decreasing number of markers as it is fully integrated into the RR-BLUP+fixed effects GS model). Consistent with previous results, we found that ~5000 SNPs were as effective for prediction as the full marker set of 108 005 SNPs (Figure 3;Supplementary Table S3). After that, accuracies began to decrease significantly, regardless of whether the genome-wide SNPs were evenly distributed or not. Average corrected P-value of the SNPs fit as fixed effects generally decreased in tight correlation with decreasing SNP number. This is to be expected given that a smaller pool of SNPs also means a smaller chance of identifying a SNP in high LD with a QTL of interest. These results suggest that it may be possible to design smaller fixed SNP arrays for GS that reduce genotyping costs and increase turn-around time (Thomson, 2014; Yu et al., 2014). Heat maps showing the extent of linkage disequilibrium across each chromosome are given in Supplementary Figure S2. GS model refinement using multi-environment data
To evaluate the effect of multi-environment data on GS model accuracy, we ran five-fold crossvalidation using the IRRI METs consisting of two years of yield data (2011–2012) over two seasons/year (dry and wet) taken at IRRI headquarters in Los Baños and at four additional sites in SE Asia during 2011, and at eight additional sites in 2012. Because of typhoons and pests in 2011, data were available for only two of four sites in the DS and one of four sites in the WS, while in 2012, data were available on only a subset of lines due to breeding program progression (Table 1). The unbalanced nature of the data set is typical of historical public breeding program data, so it is worth determining the value of such data sets, even if they are statistically non-ideal. The composition of the training population was varied in terms of sites, seasons, and years, as is typical for MET data (Supplementary Table S1B). We tested GS +de novo GWAS and RRBLUP models for all traits, as well as GS + historical GWAS and RF models for flowering time and plant height. For the GS + de novo GWAS models we used as GWAS inputs the results of GWAS run on the validation site and year (on the individuals in the training population), as well as the RYT 2012 DS and 2012 WS GWAS results used previously. Validation site, validation season, the combination of sites in the training population, and the statistical method all contributed significantly to prediction accuracy in the MET data set (Supplementary Table S4). The combination of sites in the training population was of particular importance. Across all traits, two groupings consistently produced the highest mean prediction accuracies for the sites in each group, a group consisting of the sites with southernmost latitudes: Bohol, Midsayap, Sri Lanka, and Agusan, and a group consisting of the sites with northernmost latitudes plus Agusan: Nueva Ecija, Isabela, Los Baños/IRRI and Agusan (Table 2). Agusan was unusual; it could improve the prediction at the northern sites despite the fact that it is in the southern Philippines and is best predicted by the southern group (Table 2). One possible explanation for this may be the unusually wet 'dry season‘ at Agusan, which could mean that individuals that do well in Agusan are likely to also do well almost anywhere else in SE Asia. 56 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter Table 2 - Analysis of IRRI multi-environment (MET) program target population of environments (TPE) for SE Asia irrigated rice.
Full table
Historically, these eight sites have been treated as representative of the key rice growing regions in the Philippines/SE Asia, or as a single target population of environments for breeding purposes. Our results here indicate that splitting these sites into the 'northern' and 'southern' groups, with Agusan included in both groups, improves prediction accuracies up to 10-fold. Results were especially significant for prediction of grain yield (Table 2). These results reflect the correlation of phenotypes within the northern and southern groupings; Figure 4 shows a multi-dimensional scaling analysis using the 2012 WS data for grain yield, in which the points can be superimposed on a geographical map of the sites. The phenotypic correlation of traits measured in Agusan with both the northern and southern sites offers an explanation for why inclusion of Agusan in both groups improves prediction accuracies of all sites (Figure 4; Supplementary Figure S1). These results are consistent with current breeding practice within the Philippines—the Philippines national variety release system now makes region-specific varietal recommendations. Notably, for all but three site x trait combinations, the highest mean site grouping was also significantly better than using only the validation site, evidencing the benefit of utilizing correlated multi-environment data for genomewide prediction. Figure 4.
Multi-dimensional scaling (MDS) analysis of the distance matrix of the MET adjusted 2012 wet season yield data overlaid on a map of the sites. Triangles=locations of sites, Circles= MDS points, site locations and MDS points have corresponding colors. Values= highest grain yield CV accuracy obtained for that site using the displayed site grouping, bubbles= groupings of sites that produced the highest mean prediction 57 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter accuracies at those sites. Agusan is clearly an outlier—while it geographically belongs to the southern group and is best predicted by southern group, blue dashed line, it can also improve prediction accuracies of northern group, red dashed line. Squiggle at the top of the plot indicates a break in longitudinal map space. Full figure and legend (86K)
The maximum prediction accuracies obtained for each site and trait using training populations containing data from the site groupings described above are shown in Figure 4, while the highest overall prediction accuracies for each site x season x trait combination are shown in Figure 5. In some cases, higher accuracies were obtained using different combinations of sites in the training population than the northern and southern groupings, despite these groupings producing the best average accuracies. Given that many sites and seasons had low heritabilities and low accuracies overall, we must be cautious when drawing conclusions regarding use of statistical method from this data set. In general, the RYT results are considered more reliable. A few trends are, however, apparent. For the majority of site x season combinations for flowering time, GS + de novoGWAS was clearly the best statistical method across experiments. For the best overall experiments for grain yield (Supplementary Table S4A), the GS + de novoGWAS models generally outperformed the other statistical methods, but for most sites and seasons, the difference was not significant across experiments. For plant height, by contrast, RR-BLUP was generally the best performing statistical method for the best experiments, but again, in all but two cases, the difference in accuracy when using RR-BLUP versus the other statistical methods was not significant across experiments (Figure 5; Supplementary Table S4). In general, using the RYT 2012 DS and RYT 2012 WS data as the GWAS input for the GS + de novo GWAS models resulted in better prediction accuracies than using the validation data, most likely as a result of the higher quality of the RYT data and generally more significant P-values (Figure 5; Supplementary Table S4). Figure 5.
Cross-validation prediction accuracies of flowering time (FLW, top), plant height (PH, middle), and grain yield (YLD, bottom) using multienvironment (MET) data. Data show the best overall MET accuracies obtained for each trait in each validation season, the 2012 dry season (DS; light shades) and the 2012 wet season (WS; dark shades), and validation site, left axis (Supplementary Table S4A). Accuracies are compared for GS + de novo GWAS models using, as GWAS input, the RYT 2012 DS GWAS results (blue bars), the RYT 2012 WS GWAS 58 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter results (purple bars), and GWAS run using the validation site and season (gray/black bars) to RR-BLUP results (yellow bars), and for FLW and PH only, the GS + historical GWAS results (red, orange, and green bars for 44K all, 44K indica, and 44K tropical japonica results, respectively), and random forest (RF) results (brown bars). Bars not labeled with the same lower case letter indicate a significant difference in the performance of statistical methods across all experiments where the validation population=2012 DS, bars not labeled with the same capital letter indicate a significant difference in the performance of statistical methods across all experiments where the validation population=2012 WS. Circles mapped to right axis=−log * average Pvalue (Wald test) of the SNPs fit as fixed effects in the GS + de novo GWAS models, after FDR multiple-test correction. Full figure and legend (195K)
The prediction accuracies themselves ranged for flowering time from a high of 0.70 to a low of −0.34 for Sri Lanka in 2012 WS. For plant height, the highest overall accuracies ranged from 0.55 for Midsayap in 2012 WS, the best, to 0.01 for Isabela in the 2012 DS, the worst. For yield, the highest was 0.50 for Midsayap in the 2012 WS and the lowest was 0.09 for Los BaĂąos/IRRI in the 2012 WS. These large differences in the maximum accuracies obtained at different sites and in different seasons are largely explained by the amount of data available for inclusion in the training population, given the high frequency of natural disasters in the region, by the degree of correlation between years and sites, and to a lesser extent, by the trial heritability and statistical method. For example, prediction accuracies at Agusan were generally high because training data were available from 3 seasons (2011 WS, 2012 DS and WS), and Agusan was well correlated with the other southern sites (Figures 4 and 5;Supplementary Tables S4). Isabela, on the other hand, had very low accuracies for prediction in the 2012 DS, very low trial heritabilities (0 and 0.7 for PH and YLD, respectively, and the site/season was not correlated with any other site or season, including itself in the WS, all of which evidence that the phenotyping data for this site and season were compromised in some way (Table 1, Figure 5;Supplementary Figure S1, Supplementary Table S4). The highly negative accuracy for flowering time at Sri Lanka, on the other hand, was obtained using the GS + historical GWAS results, which could indicate that in some cases using historical GWAS results on a population in which they are not relevant can result in negative correlation accuracies, possibly as a result of differences in linkage phase between the tagged SNP and QTL in the two populations, or as a result of epistasis. Conducting multi-location field trials is a challenging and massive logistical operation that, in the public sector, is also under-resourced. GS, on one hand, appears to be more sensitive to lowquality data and/or a poorly defined target population of environments (TPE) than phenotypic selection (Heslot et al., 2015), and our results indeed highlight a strong need for high-quality phenotype data. They also suggest, however, that collecting higher quality data at fewer sites could enable good genome-based predictions at other correlated sites, an observation in 59 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter agreement with multi-environment experiments performed in maize and wheat (Burgue単o et al., 2012; Heslot et al., 2013; Crossa et al., 2014;Zhang et al., 2015). By focusing on quality data at a few key, representative environments, it could thus be possible to improve gain-from-selection across an entire region. Ultimately, GS prediction accuracies suffer when badly correlated environments are combined due to GxE effects, that is, the fact that a given variety may perform well in one environment, but poorly in another. Even in a traditional pedigree breeding scheme, it is important to accurately group common environments in order to avoid deleterious GxE effects in released varieties. An alternative solution to utilizing multi-environment data is to explicitly model GxE in the GS model, as has recently been shown effective in wheat (Lopez-Cruz et al., 2015). This strategy is likely to be more useful with cleaner and more complete data sets, but warrants further research for the refinement of rice GS models. Use of GS+GWAS to expedite the introduction of novel genetic variation into elite breeding populations
GS, while new in application, is conservative in breeding effect. Genome-wide prediction models are trained only on alleles and genetic diversity present in a given population, and as such, the alleles selected for by GS are those already known to contribute to good performance. In rice and many other crops, however, new diversity and GxG interactions are important sources of trait improvement that are essential for enhancing genetic gain (Li et al., 1997;Thomson et al., 2006; McCouch et al., 2012; McCouch et al., 2013). As a result, to make GS work for rice breeding, it is necessary to 1) identify and tap sources of novel variation, and 2) to develop methods that will introduce these favorable new alleles into adapted varieties while retaining the highly productive allele combinations that are the foundation of food production. To address this issue, we propose a two-stream/two-part GS breeding schema in which underutilized germplasm is systematically incorporated into a GS breeding pipeline to test for and predict the presence of new, highly effective allele combinations (Figure 6). Stream 1 consists of pre-breeding, in which new alleles sourced from diverse germplasm are sequentially introduced into a population of adapted material. After several rounds of backcrossing and recombination, necessary to break linkages after the initial F1 cross between un-adapted and adapted material, GS + de novo GWAS models would be used to increase the frequency of desirable exotic QTL while simultaneously selecting against alleles from un-adapted material that conferred negative effects. Such desirable QTL would be identified by the de novo GWAS, and quickly fixed as a direct result of the GS + de novo GWAS approach. Stream 2 continues the process of refining and improving existing elite material, and by feeding the output of Stream 1 into Stream 2, the genetic base of modern varieties would be expanded. Valuable QTL in the Stream 2 breeding population would also be identified via de novo GWAS and fit as fixed effects. These fixed effects could also include the exotic QTL from Stream 1, and any other large effect QTL a breeder might wish to target for either positive or negative selection.
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Daily Global, Regional & Local Rice E-Newsletter Figure 6.
Diagram of proposed two-stream GS breeding program. Stream 1 (yellow boxes) consists of pre-breeding, in which favorable alleles from exotic germplasm are introduced into adapted germplasm. Exotic parents are crossed with elite germplasm to develop Breeding Population 1. Selection of individuals from Breeding Population 1 is performed using a combination of GS + de novo GWAS models (GS+), in which the exotic QTL are fit as fixed effects, and phenotype. The training population GS would be a subset of breeding population 1, that is, a fraction of breeding population 1 would be both genotyped and phenotyped, while the rest of breeding population 1 would be genotyped only. Adapted materials from Breeding Population 1 are crossed into Breeding Population 2 (Stream 2, blue boxes) where they are further refined using GS + de novo GWAS models, where the fixed effects would include valuable QTL identified based on GWAS performed in Breeding Population 2, the exotic QTL from Stream 1, or any other large effect QTL a breeder might normally target for trait improvement. Output from Stream 2 can be advanced toward variety release or fed back into Stream 1 to serve as parents for further crossing and population development. Full figure and legend (88K)
The above approach would enable the breeder to learn directly from data on new and diverse germplasm and make rapid genetic gain in a way that would not be possible using simple RRBLUP models, as it is the GS + de novo GWAS strategy that makes it possible to extract the information necessary for fixing valuable exotic alleles during model development as well as enhancing prediction accuracy. Furthermore, no prior knowledge about genes, QTL, or gene networks is required. Thus, while it is interesting to extrapolate as to which genes are involved in a given biological process and to compare new GWAS results to those previously published, the breeder is not required to do so, and is not encumbered by the need to identify causal polymorphisms or candidate genes underlying potentially large regions of significance a priori. As a result, this approach empowers the breeder to move forward immediately with selection in a breeding population based on GEBVs with the knowledge that GEBVs are derived de novofrom 61 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter relevant breeding material, trait evaluations, and target environments, in keeping with the objectives and realities of the breeding program at hand. Topof page Discussion
GS, or genome-wide prediction, has been heralded as a strategy that can help increase the rate of genetic gain in plant and animal breeding without prior knowledge of the genes or QTLs underlying agronomic traits (Rutkoski et al., 2012; Asoro et al., 2013; Massman et al., 2013a; Crossa et al., 2014; Beyene et al., 2015; Onogi et al., 2015; Spindel et al., 2015; Zhang et al., 2015). Our work suggests that using biological knowledge about genotype–phenotype associations, as demonstrated by the GS+ de novo GWAS model results presented here, can improve the prediction accuracies of GS in rice. Given the amount of basic biological information available for many crop species today, and for rice in particular, bypassing the opportunity to integrate genic and QTL information into GS models means forfeiting a significant component of model accuracy. However, integrating inaccurate or inappropriate priors or fixed effects can have a negative impact on GS models. In this study, we aimed to develop a generically useful approach to identifying SNPs to include in GS prediction models as fixed effects that would take advantage of the matrix of genotypic and phenotypic data already generated for the GS study, and would not require independent, a priori information about known functional markers for traits of interest. Previous work has relied on the use of historical information to identify markers that can be fit as fixed effects (Bentley et al., 2014; Bernardo, 2014; Owens et al., 2014; Rutkoski et al., 2014;Zhang et al., 2014; Zhao et al., 2014; Lipka et al., 2015). Although these examples provide evidence that introducing fixed effects can improve the prediction accuracy of GS models, this study is the first to quantify the benefit of including markers as fixed effects in RR-BLUP models for a rice breeding population, and is also the first, to our knowledge, in which the identification of SNPs to include as fixed effects in the GS model was accomplished based entirely on de novo GWAS (information from GWAS performed on the breeding population undergoing selection) an easier and more streamlined procedure than delving into the literature to ensure that the GWAS markers tagged previously reported genes of large effect. In a study by Zhang et al. (2014), the authors used the results of previously published GWAS studies to improve GS prediction in dairy cattle and rice, but rather than performing a GWAS using data from the training (breeding) population under selection, as we have done here, they used generic models for each species. Although that strategy may be effective for dairy cattle, which are characterized by an extremely narrow genetic base and highly uniform production environments, it was not expected to prove as effective for plant species such as rice where deep population structure and highly variable production environments create the need to derive population- and environment-specific fixed variables (Zhao et al., 2011; Guo et al., 2014; Heslot et al., 2015;Supplementary Note). To confirm this hypothesis, we compared our models in which markers fit as fixed effects were selected based on the results of GWAS performed on the breeding population to models in which SNPs were selected as fixed effects based on previously published GWAS data, both when subpopulation did and did not match the 62 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter individuals undergoing selection. In no case did using previously published GWAS data significantly improve accuracy over using de novo GWAS, and in the majority of cases, the GS + historical GWAS models were significantly worse than the GS + de novo GWAS models (Figures 2 and 5;Supplementary Tables S2). In a few rare cases, the GS + historical GWAS models even resulted in negative prediction accuracies, suggesting that extreme care would need to be taken if perusing this strategy in rice populations. While the GS + de novo GWAS models performed well overall, the data used as input to GS + de novo GWAS models did have a significant effect on prediction accuracy for 2/3 traits examined, that is, plant height and grain yield. The grain yield case is of particular interest, because the difference in performance of the GS + de novo GWAS models that used as GWAS input the 2012 WS data performed significantly better than the GS + de novo GWAS models that used the 2012 DS data as input across experiments for both validation seasons. As discussed in the results, this observation is best explained by the difference in FDR corrected P-value of the most significant SNPs resulting from the two GWAS. From these results, we conclude that the utility of GS + de novo models is noteworthy when the de novo GWAS results identify highly significant SNPs, but may not improve accuracies significantly if GWAS P-values are borderline. Based on this population, we would recommend utilizing the GS + de novo GWAS model over alternatives such as RR-BLUP or RF when the -log (FDR corrected P-values of the most significant SNPs) 2.0. Finally, it is worth noting that as with any GWAS-based methodology, controlling for subpopulation structure in the population is essential, otherwise, associations may be spurious and lead to decreased prediction accuracies. Our results indicate that the GS + de novo GWAS approach will be successful for rice breeding within the boundaries described above. The approach should allow breeders to extract information from the training population and, simultaneously, to learn which regions of the genome are significantly associated with traits of interest in their material. The breeder can then use that information to improve the accuracy of their GS models. Consequently, breeding programs can operate with significant autonomy, unencumbered by the need to identify genes or QTL underlying traits of interest, as was the case for MAS and many previously tested GS plus fixed effect models. Furthermore, the accuracy of our GS + de novoGWAS models can be iteratively improved, as information from subsequent training populations is continuously fed back into the model to improve model fit and accuracy. How, then, does the GWAS and GS information generated by a breeding program intersect with published information about genes, QTLs, expression networks, physiological pathways, developmental phenotypes, etc. As breeding programs increasingly invest in the sequencing, genotyping and large scale phenotyping of populations and germplasm resources in environments that are relevant to the development of new, commercial crop varieties, they will build important bridges that enable data and information to flow between the world of basic biological research and that of applied or translational science, leading to the development of collaborations and research networks that will help bring these two worlds closer together. Scientists interested in population biology, molecular genetics and gene discovery will help discover and characterize genes and alleles associated with phenotypes of interest to the plant breeder, providing useful tools and insights about natural variation that can be help breeders, 63 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter agronomists, gene bank managers, physiologists and ecologists to better manage natural variation and to generate sustainable systems capable of producing the food, feed, fiber and fuel needed for a growing world population. Another important source of phenotype variation in breeding populations is derived from the environment (VpE) and Genotype x Environment (GxE) interactions (VpGE). Previous GS experiments in wheat and barley have found that multi-environment GS models can lead to improved accuracies by borrowing information from correlated environments (Burgueño et al., 2012; Heslot et al., 2013; Crossa et al., 2014). To see if we could likewise improve accuracies using multi-environment data in rice, we performed CV using phenotype data collected at an additional seven sites in SE Asia. When data were well correlated across sites, GS accuracies increased up to 60 times that of single site models (Table 2). However in other cases, when uncorrelated sites were combined in the training populations, accuracies essentially plunged to zero. These results emphasize the importance of accurately defining a TPE before attempting to perform GS using multi-environment data. It has been suggested that defining a TPE is of greater importance when performing GS than phenotypic selection because the results of 'bad data' going into a GS model can have long-term impacts on breeding program gains (Heslotet al., 2015). Our MET results strongly indicate two groupings of environments among the eight sites tested here—a ‗northern‘ TPE consisting of the sites at Nueva Ecija, Isabela, Los Baños and Agusan, and a 'southern' TPE consisting of the sites at Agusan, Midsayap, Bohol and Sri Lanka (Table 2, Figures 4 and 5;Supplementary Table S4). Hai Dong, Vietnam, was an outlier that should not be included in either group (Supplementary Figure S1). Finally, the results highlight the extreme variability in phenotype data quality across sites, seasons and years, and the strong need for consistent phenotype quality to for GS to be implemented effectively. This need is all the more important in the tropics where extreme weather events (for example, typhoons) may eliminate one or more sites/seasons of data. One useful strategy may be to collect higher quality data at a few, key, representative sites and then predict performance at other correlated locations. The final question is when and how to incorporate knowledge about GEBVs derived from GS models into applied breeding pipelines. GEBVs offer plant breeders an opportunity to integrate knowledge about quantitative trait performance in their breeding populations early in their breeding pipelines, while traditionally, this knowledge is only available at the end of a multienvironment/multi-year replicated yield trial. Because the GEBV‘s summarize information that's derived directly from the breeders‘ fields, it is, in essence, simply a more objective measure of what a breeder practicing phenotypic selection would use as the basis for making selections. GS also provides that information in advance so the breeder can use it to select the lines that merit more in-depth phenotypic evaluation. Thus, while the use of GEBVs may differ depending on the trait, breeder and the kind of training population from which the estimated breeding values were derived, they provide the breeder with an additional selection criterion that can be used to increase the rate of genetic gain. The greatest and most accessible source of untapped genetic diversity for plant improvement can be found in the wild and cultivated accessions housed in the world's gene banks. Although there 64 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter have been many efforts to screen gene bank material to identify individuals carrying specific traits of interest, it has not been feasible to screen large numbers of accessions for favorable alleles that contribute to useful quantitative variation (McCouch et al., 2012), particularly where the phenotype of the donor germplasm is not obviously superior. While we do not report doing this here, the potential to use GS models, in combination with high throughput genotyping, makes it possible. The first step would be to genotype the gene bank materials to facilitate a systematic sampling and exploration of the variation in the gene bank. The second would be to incorporate a diverse selection of gene bank materials into a GS breeding pipeline, generating advanced backcross or possibly multi-parent advanced generation inter-cross populations in adapted genetic backgrounds (pre-breeding). To determine which lines from these populations to use as parents in future crossing and population development, training populations representing these new populations would be genotyped and phenotyped as the basis for GWAS to identify new and highly effective allele combinations that were predictive of top-performing offspring (that is, GWAS in breeding panels/training populations). The significant GWAS-SNPs would then be used as fixed variables in GS models (previously developed to facilitate the selection of adapted, elite breeding materials) to facilitate the efficient selection of lines from the new populations that carried favorable exotic alleles in the genetic background of elite lines with high GEBVs. In summary, here we demonstrate that by incorporating information from GWAS and correlated sites into GS models, prediction accuracies can increase to the point that genotyping and performing GS is more cost-effective than planting and phenotyping additional yield trials (Spindel et al., 2015). With the addition of novel diversity from gene banks, genomics-assisted selection should be a transformative strategy for rice improvement. Topof page Data archiving
The genotype data and RYT phenotype data are available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.7369p. MET phenotype data will be uploaded to rice diversity.org on article publication and are also available from the Dryad Digital Repository: http://dx.doi.org/10.5061/dryad.vv28j. Topof page Conflict of interest
The authors declare no conflict of interest. References 1. Alexandrov N, Tai S, Wang W, Mansueto L, Palis K, Fuentes RR et al. (2014). SNP-Seek database of SNPs derived from 3000 rice genomes.Nucleic Acids Res 43 (Database issue): D1023–D1027. | Article | PubMed | 65 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter 2. Asoro FG, Newell MA, Beavis WD, Scott MP, Tinker NA, Jannink J-L. (2013). Genomic, marker-assisted, and pedigree-BLUP selection methods for β-glucan concentration in elite oat. Crop Sci 53: 1894–1906. | Article | 3. Begum H, Spindel J, Lalusin AG, Borromeo TH, Gregorio GB, Hernandez JEet al. (2015). Association mapping and genomic selection in rice (Oryza sativa): association mapping for yield and other agronomic traits in elite, tropical rice breeding lines. PLoS One 10: 1371. | Article | 4. Benjamini Y, Hochberg Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc B 57: 289–300. | ISI | 5. Bentley AR, Scutari M, Gosman N, Faure S, Bedford F, Howell P et al. (2014). Applying association mapping and genomic selection to the dissection of key traits in elite European wheat. Theor Appl Genet 127: 2619–2633. | Article | PubMed | 6. Bernardo R. (2008). Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48: 1649. | Article | ISI | 7. Bernardo R. (2010). Genomewide selection with minimal crossing in self-pollinated crops. Crop Sci 50: 624–627. | Article | 8. Bernardo R. (2014). Genomewide selection when major genes are known.Crop Sci 54: 68. | Article | 9. Beyene Y, Semagn K, Mugo S, Tarekegne A, Babu R, Meisel B et al. (2015). Genetic gains in grain yield through genomic selection in eight bi-parental maize populations under drought stress. Crop Sci 55: 154–163. | Article | 10. Breiman L. (2001). Random forests. Mach Learn 45: 5–32. | Article | ISI | 11. Burgueño J, de los Campos G, Weigel K, Crossa J. (2012). Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52: 707. | Article | ISI | 12. Collard BC, Mackill DJ. (2008). Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci 363: 557– 572. | Article | PubMed | CAS | 13. Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK. (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142: 169–196. | Article | CAS | 14. Crossa J, Perez P, Hickey J, Burgueno J, Ornella L, Ceron-Rojas J et al. (2014). Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity (Edinb) 112: 48– 60. | Article | PubMed | 15. Daetwyler HD, Calus MP, Pong-Wong R, de Los Campos G, Hickey JM. (2013). Genomic prediction in animals and plants: simulation of data, validation, reporting, and benchmarking. Genetics 193: 347–365. | Article | PubMed | 16. Desta ZA, Ortiz R. (2014). Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19: 592–601. | Article | PubMed | CAS | 17. Endelman JB. (2011). Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4: 250–255. | Article |
66 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter 18. Gianola D, de los Campos G, Hill WG, Manfredi E, Fernando R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics 183: 347–363. | Article | PubMed | ISI | 19. Gianola D, van Kaam JB. (2008). Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.Genetics 178: 2289– 2303. | Article | PubMed | ISI | 20. Goff SA, Ricke D, Lan T-H, Presting G, Wang R, Dunn M et al. (2002). A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science296: 92–100. | Article | PubMed | ISI | CAS | 21. Guo Z, Tucker D, Basten C, Gandhi H, Ersoz E, Guo B et al. (2014). The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127: 749– 762. | Article | PubMed | 22. Heffner EL, Sorrells ME, Jannink JL. (2009). Genomic selection for crop improvement. Crop Sci 49: 1–12. | Article | ISI | CAS | 23. Hennig C.. (2015). CRAN. 24. Heslot N, Jannink J-L, Sorrells ME. (2013). Using genomic prediction to characterize environments and optimize prediction accuracy in applied breeding data. Crop Sci 53: 921. | Article | 25. Heslot N, Jannink J-L, Sorrells ME. (2015). Perspectives for genomic selection applications and research in plants. Crop Sci 55: 1–12. | Article | 26. Heslot N, Yang H-P, Sorrells ME, Jannink J-L. (2012). Genomic selection in plant breeding: a comparison of models. Crop Sci 52: 146. | Article | ISI | 27. Huang X, Lu T, Han B. (2013). Resequencing rice genomes: an emerging new era of rice genomics. Trends Genetics 29: 225–232. | Article | 28. Kaufman L, Rousseeuw PJ. (1990) Finding Groups in Data: An Introduction to Cluster Analysis. New York: Wiley. 29. Li Z, Pinson SR, Park WD, Paterson AH, Stansel JW. (1997). Epistasis for three grain yield components in rice. Genetics 145: 453–465. | PubMed | ISI | CAS | 30. Lipka AE, Kandianis CB, Hudson ME, Yu J, Drnevich J, Bradbury PJ et al. (2015). From association to prediction: statistical methods for the dissection and selection of complex traits in plants. Curr Opin Plant Biol24: 110–118. | Article | PubMed | 31. Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL et al. (2015). Increased prediction accuracy in wheat breeding trials using a marker x environment interaction genomic selection model. G3 (Bethesda)5: 569–582. | PubMed | 32. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H et al. (2011). Genomic Selection in Plant Breeding et al. Advances in Agronomy, Vol.110, pp 77–123. 33. Ly D, Hamblin M, Rabbi I, Melaku G, Bakare M, Gauch HG et al. (2013). Relatedness and genotype × environment interaction affect prediction accuracies in genomic selection: a study in cassava. Crop Sci 53: 1312. | Article | 34. Massman JM, Gordillo A, Lorenzana RE, Bernardo R. (2013a). Genomewide predictions from maize single-cross data. Theor Appl Genet 126: 13–22. | Article | PubMed | ISI |
67 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter 35. Massman JM, Jung HJG, Bernardo R. (2013b). Genomewide Selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53: 58–66. | Article | ISI | 36. McCouch S, Baute GJ, Bradeen J, Bramel P, Bretting PK, Buckler E et al. (2013). Agriculture: feeding the future. Nature 499: 23–24. | Article | PubMed | CAS | 37. McCouch SR, McNally KL, Wang W, Sackville Hamilton R. (2012). Genomics of gene banks: a case study in rice. Am J Bot 99: 407–423. | Article | PubMed | 38. Meuwissen THE, Hayes BJ, Goddard ME. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157: 1819–1829. | PubMed | ISI | CAS | 39. Onogi A, Ideta O, Inoshita Y, Ebana K, Yoshioka T, Yamasaki M et al. (2015). Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.). Theor Appl Genet 128: 41–53. | Article | PubMed | 40. Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K et al. (2007). The TIGR Rice Genome Annotation Resource: improvements and new features. Nucleic Acids Res 35 (Database issue): D883–D887. | Article | PubMed | ISI | CAS | 41. Owens BF, Lipka AE, Magallanes-Lundback M, Tiede T, Diepenbrock CH, Kandianis CB et al. (2014). A foundation for provitamin a biofortification of maize: genome-wide association and genomic prediction models of carotenoid levels. Genetics 198: 1699–1716. | Article | PubMed | 42. Perez-Rodriguez P, Gianola D, Gonzalez-Camacho JM, Crossa J, Manes Y, Dreisigacker S. (2012). Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3 (Bethesda)2: 1595–1605. | PubMed | 43. Ronald P, Leung H. (2002). The most precious things are not jade and pearls. Science 296: 58– 59. | Article | PubMed | 44. Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, Sorrells M. (2012). Evaluation of genomic prediction methods for fusarium head blight resistance in wheat. Plant Genome 5: 51– 61. | Article | 45. Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H et al. (2014). Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome 127: 1441– 1448. 46. Spindel J, Begum H, Akdemir D, Virk P, Collard B, Redo√±a E et al. (2015). Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. PLoS Genet 11: e1004982. | Article | PubMed | 47. Thomson MJ. (2014). High-throughput SNP genotyping to accelerate crop improvement. Plant Breed Biotechnol 2: 195–212. | Article | 48. Thomson MJ, Edwards JD, Septiningsih EM, Harrington SE, McCouch SR. (2006). Substitution mapping of dth1.1, a flowering-time quantitative trait locus (QTL) associated with transgressive variation in rice, reveals multiple Sub-QTL. Genetics 172: 2501– 2514. | Article | PubMed | ISI | CAS | 49. Varshney RK, Terauchi R, McCouch SR. (2014). Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding. PLoS Biol 12: e1001883. | Article | PubMed | 68 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter 50. Yu HH, Xie WB, Li J, Zhou FS, Zhang QF. (2014). A whole-genome SNP array (RICE6K) for genomic breeding in rice. Plant Biotechnol J 12: 28–37. | Article | PubMed | 51. Zhang X, Perez-Rodriguez P, Semagn K, Beyene Y, Babu R, Lopez-Cruz MAet al. (2015). Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs. Heredity 114: 291–299. | Article | PubMed | 52. Zhang Z, Ober U, Erbe M, Zhang H, Gao N, He JL et al. (2014). Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies. PLoS One 9: e93017. | Article | PubMed | CAS | 53. Zhao K, Tung C-W, Eizenga GC, Wright MH, Ali ML, Price AH et al. (2011). Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2: 467. | Article | PubMed | CAS | 54. Zhao Y, Mette MF, Gowda M, Longin CFH, Reif JC. (2014). Bridging the gap between markerassisted and genomic selection of heading time and plant height in hybrid wheat. Heredity 112: 638–645. | Article | PubMed | ISI | CAS | 55. Zhou X, Stephens M. (2012). Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44: 821–824. | Article | PubMed | CAS | Topof page Acknowledgements
We thank Francisco Agosto-Perez, Genevieve DeClerck and Anthony Greenberg for statistical and computational support, Sandra Harrington and Gen Onishi for greenhouse support, and Charlotte Acharya and Wenyan Zhu for assistance with genotyping-by-sequencing library preps. Funding provided by NSF PGRP 102655 to SMC, a NSF graduate research fellowship to JES, the Organization for Women in Science for the Developing World (OWSD) for the PhD scholarship for HB and the Japan Government via the Japan Rice Breeding Project at IRRI. All data sets are publicly available at http://www.ricediversity.org. Supplementary Information accompanies this paper on Heredity website .
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article‘s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visithttp://creativecommons.org/licenses/by/4.0/. http://www.nature.com/hdy/journal/vaop/ncurrent/full/hdy2015113a.html
o Gay Talese on Journalism‘s Woes 69 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter
Hutchinson Selects New Arkansas Plant Board Director Arkansas News Bureau LITTLE ROCK — Gov. Asa Hutchinson on Wednesday named Terry Walker, currently serving as assistant director of the Arkansas State Plant Board, to be the board‘s next director.Walker has worked for the board for 13 years. He replaces Daryl Little, who retired in January.The Plant Board is a division of the state Agriculture Department that regulates products and services such as seed, feed, fertilizer, pesticides, weights and measures, petroleum, honeybees, plant pests, nurseries and pest control services.A graduate of the University of Arkansas, Walker began his career in 1971 with the Rice Branch Experiment Station, now the Rice Research and Extension Center, in Stuttgart. He later worked in private soybean research in Alabama and Tennessee before becoming project leader for soybean and small grain variety testing at the University of Arkansas Agronomy Department in 1984.He returned to the private soybean industry from 1991-96, then managed research for a seed company and later managed a cotton gin. In 2002, he accepted the position of Plant Industry Division director for the Plant Board, and in 2012, he became assistant director.―From row crops, to livestock, to horticultural products and fish, Arkansas products go toward providing a safe and economical food source for everyone,‖ Walker said in a statement. ―This is a major concern for everyone associated in a governmental and/or regulatory capacity. Not only does agriculture provide a livelihood for our producers but (it) also puts food on our tables.‖ http://swtimes.com/news/state-news/hutchinson-selects-new-arkansas-plant-boarddirector#sthash.G2G1QLvT.dpuf
GINNA PARSONS: Asian meatballs sweet and spicy Posted on February 10, 2016 by Ginna Parsons in Food, Lifestyle GINNA PARSONS
Last week after I wrote about Super Bowl appetizers, I received a flurry of emails from more public relations agencies offering their best dishes for the big game day. Two in particular caught my eye: buffalo pork kebabs with blue cheese dipping sauce and spicy Korean-style meatballs. I forwarded the recipes to my husband, Charlie, with a note that said, ―Both of these look good to me.‖ A few minutes later, I received his response: ―Let‘s make them both.‖
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Daily Global, Regional & Local Rice E-Newsletter Well, I didn‘t make them both, but I did make the meatballs Saturday night. I was a little leery of grated pear in meatballs, but I have to tell you that was one of the best parts. I found the pear at Kroger and it was like none I‘ve ever tasted: sweet and crunchy (and expensive … one pear was $1.99, but so worth it). In the bin it was called an Asian pear, but on the grocery receipt it says Korean pear. It actually looks more like an apple. These meatballs were sweet, spicy and delicious. I served them over basmati rice with a side of sugar snap peas. Korean-Style Meatballs Meatballs 1⁄2 Asian pear, peeled, cored and grated 11⁄2 pounds lean ground pork 1⁄2 cup panko Japanese breadcrumbs 3 scallions, thinly sliced 3 cloves garlic, finely minced 2 tablespoons chili paste 2 tablespoons soy sauce 1 large egg, lightly beaten 1 teaspoon rice vinegar 1⁄2 teaspoon black pepper 1⁄2 teaspoon kosher salt 2 tablespoons canola oil Glaze 71 | w w w . r i c e p l u s s . c o m , w w w . r i c e p l u s m a g a z i n e . b l o s g s p o t . c o m
Daily Global, Regional & Local Rice E-Newsletter 1⁄2 cup blackberry jam or jelly 3 tablespoons rice vinegar 1 tablespoon chili paste 2 tablespoons soy sauce Place grated pear between several paper towels and press to extract any extra liquid. In a large bowl, combine ground pork, pear, breadcrumbs, scallions, garlic, chili paste, soy sauce, egg, vinegar, pepper and salt. Use your hands to form meatballs about 11⁄2-inches in diameter (you should have about 30 meatballs). In a large sauté pan, heat oil over medium heat. Working in batches, brown meatballs on all sides, then set aside on a baking pan. Continue until all meatballs are browned. Bake meatballs at 350 degrees for 10 to 12 minutes, or until an internal temperature reaches 160 degrees. While meatballs are cooking, make the glaze. In a small saucepan, combine jam, chili paste, rice vinegar and soy sauce over medium heat for about 5 minutes until glossy. Place hot meatballs in a large bowl and pour glaze over, tossing gently to combine. Serve over brown rice, basmati rice or rice noodles. Ginna Parsons is the Daily Journal‘s food/home/garden editor.
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