Hacia una agricultura sustentable: Impacto de la red de colaboraciones

Jelle Van Loon (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRIFOOD SYSTEMS BIODIVERSITY CONSERVATION FOOD SECURITY CONSERVATION AGRICULTURE

Editorial: Sorghum and pearl millet as climate resilient crops for food and nutrition security

Palak Chaturvedi Mahalingam Govindaraj Govindan Velu Wolfram Weckwerth (2022, [Artículo])

Climate Smart Crops Foxtail Millet CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING FINGER MILLET FOOD SECURITY SETARIA ITALICA GENETIC RESOURCES PEARL MILLET SORGHUM

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics

Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023, [Artículo])

New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY

SNP discovery using BSR-Seq approach for spot blotch resistance in wheat (Triticum aestivum L.), an essential crop for food security

Vinod Mishra Ramesh Chand UTTAM KUMAR Apurba Chowdhury arun joshi (2022, [Artículo])

Spot Blotch Recombinant Inbred Lines Bulk Segregant Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT FOOD SECURITY SINGLE NUCLEOTIDE POLYMORPHISM DISEASE RESISTANCE

Potential impacts of Ukraine-Russia armed conflict on global wheat food security: A quantitative exploration

Khondoker Mottaleb Gideon Kruseman Sieglinde Snapp (2022, [Artículo])

Violent conflict is a major cause of acute food crises. In 2021, at least 155 million people in 10 countries were severely food insecure and eight of those countries were experiencing armed conflict. On February 24, 2022, an armed conflict between Russian Federation (Russia) and Ukraine escalated. As Russia and Ukraine are major wheat exporters, this will aggravate the already precarious food security situation in many developing countries by disrupting wheat production and export and by accelerating price hikes in import-dependent developing countries. This study examines the potential impacts of this ongoing armed conflict between Russia and Ukraine on wheat price, consumption, and calorie intake from wheat. In doing so, it applies the conditional mixed process estimation procedure using information collected from 163 countries and territories for the years 2016–2019 from online database of the Food and Agriculture Organization of the United Nations (FAO). The study shows that, on average, a 1% decrease in the global wheat trade could increase the producers' price of wheat by 1.1%, and a 1% increase in the producers' price could reduce the yearly per capita wheat consumption by 0.59%, daily calorie intake by 0.54% and protein intake by 0.64% in the sampled countries. Based on this, the study demonstrates that a 50% reduction in wheat exports by Russia and Ukraine could increase the producers’ price of wheat by 15%, which would induce a reduction in wheat consumption and dietary energy intake by at least 8%. Since wheat export has reduced from both Russia and Ukraine, to avoid a food crisis in developing countries, policies are suggested, including near term improvement of domestic wheat production by promoting improved agronomic practices to close yield gaps to meet a substantial portion of wheat self-sufficiency goals. In the long run, countries in Africa, East Asia and South America can explore expanding wheat into new land area. International donor agencies can play a key role in supporting the ongoing wheat research and development activities.

Export-Import CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ARMED CONFLICTS CALORIES CONSUMPTION ELASTICITY FOOD SECURITY PRICES PRODUCTION WHEAT

Multi-environment genomic prediction of plant traits using deep learners with dense architecture

Osval Antonio Montesinos-Lopez Jose Crossa (2018, [Artículo])

Shared Data Resources Deep Learning Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA ACCURACY GENOMICS NEURAL NETWORKS FORECASTING DATA MARKER-ASSISTED SELECTION

Trade-off in the design of sustainable cropping systems at a regional level: A case study on the North China plain

Jeroen Groot XiaoLin Yang (2022, [Artículo])

Holistic Analysis Model-Based Analysis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP ROTATION FOOD SECURITY WATER USE ENVIRONMENTAL PROTECTION ECONOMIC VIABILITY

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Martin van Ittersum (2023, [Artículo])

Context: Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective: The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods: A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results: Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion: Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance: The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Model Accuracy Model Precision Linear Mixed Models CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MACHINE LEARNING SUSTAINABLE INTENSIFICATION BIG DATA YIELDS MODELS AGRONOMY

The generation challenge programme platform: Semantic standards and workbench for crop science

Richard Bruskiewich Guy Davenport Mathieu Rouard Reinhard Simon Samart Wanchana Trushar Shah Victor Jun Ulat Andrew Farmer Pankaj Jaiswal Mark Wilkinson David Marshall Alyssa Collins (2008, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP IMPROVEMENT GENETIC RESOURCES PLANT BREEDING BIODIVERSITY COMPUTER APPLICATIONS DIGITAL TECHNOLOGY DATA PROCESSING

Review of Nationally Determined Contributions (NCD) of Vietnam from the perspective of food systems

Tek Sapkota (2023, [Documento de trabajo])

Over the past decades, Vietnam has significantly progressed and has transformed from being a food-insecure nation to one of the world’s leading exporters in food commodities, and from one of the world’s poorest countries to a low-middle-income country. The agriculture sector is dominated by rice and plays a vital role in food security, employment, and foreign exchange. Vietnam submitted its updated Nationally Determined Contributions (NDC) in 2022 based on the NDC 2020. There is a significant increase in greenhouse gas (GHG) emission reduction, towards the long-term goals identified in Vietnam’s National Climate Change Strategy to 2025, and efforts are being made to fulfil the commitments made at COP26. The Agriculture Sector is the second-largest contributor of GHG emissions in Vietnam, accounting for 89.75 MtCO2eq, which was about 31.6 percent of total emissions in 2014. Rice cultivation is the biggest source of emissions in the agriculture sector, accounting for 49.35% of emissions from agriculture. The total GHG removal from Land Use, Land Use Change and Forestry (LULUCF) in 2014 was -37.54 MtCO2eq, of which the largest part was from the forest land sub-sector (35.61 MtCO2eq), followed by removal from croplands (7.31 MtCO2eq) (MONRE 2019).

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE GREENHOUSE GAS EMISSIONS FOOD SYSTEMS LAND USE CHANGE AGRICULTURE POLICIES DATA ANALYSIS