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Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])
Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES
MLN disease diagnostics, MLN disease-free seed production and MLN disease management
Suresh L.M. (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA DISEASES DISEASE MANAGEMENT SEED PRODUCTION MAIZE NECROSIS YIELD LOSSES ECONOMIC IMPACT SURVEILLANCE SYSTEMS TRAINING
Abdelfattah DABABAT fouad mokrini (2023, [Artículo])
Durum Wheat Bread Wheat Cereal Cyst Nematodes Nematode Reproduction Factors CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA HETERODERA AVENAE HARD WHEAT SOFT WHEAT NEMATODES RESISTANCE VARIETIES SUSTAINABLE PRODUCTION CEREALS
Balwinder-Singh Meha Jain (2023, [Artículo])
One way to meet growing food demand is to increase yields in regions that have large yield gaps, including smallholder systems. To do this, it is important to quantify yield gaps, their persistence, and their drivers at large spatio-temporal scales. Here we use microsatellite data to map field-level yields from 2014 to 2018 in Bihar, India and use these data to assess the magnitude, persistence, and drivers of yield gaps at the landscape scale. We find that overall yield gaps are large (33% of mean yields), but only 17% of yields are persistent across time. We find that sowing date, plot area, and weather are the factors that most explain variation in yield gaps across our study region, with earlier sowing associated with significantly higher yield values. Simulations suggest that if all farmers were able to adopt ideal management strategies, including earlier sowing and more irrigation use, yield gaps could be closed by up to 42%. These results highlight the ability of micro-satellite data to understand yield gaps and their drivers, and can be used to help identify ways to increase production in smallholder systems across the globe.
Yield Drivers Yield Mapping CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MICROSATELLITES YIELD GAP SMALLHOLDERS FOOD PRODUCTION YIELD INCREASES
Menas Wuta Isaiah Nyagumbo (2021, [Artículo])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE DRY SPELLS RAINWATER HARVESTING CROP PRODUCTION TECHNOLOGY
Agricultural lime value chain efficiency for reducing soil acidity in Ethiopia
Moti Jaleta (2023, [Artículo])
Soil acidity is challenging agricultural production in Ethiopia. Above 43% of the farmland is under soil acidity problem and it leads to low crop yields and production losses. Ag-lime is widely considered as an effective remedy for amending soil acidity. This study assesses the current structure of ag-lime value chain and its functionality focusing on central parts of Ethiopia where lime is produced and channeled to acidity affected areas. The study uses Ethiopia as a case study and applies qualitative methods such as key informant interviews and focus group discussions to collect data from different actors in the ag-lime value chain. Key findings indicate that both public and private ag-lime producing factories are operating below their capacity. Due to limited enabling environments, the engagement of private sector in ag-lime value chain is minimal. In addition, farmers have a good awareness of soil acidity problem on their farms, and its causes and mitigation strategies in all regions. However, the adoption of ag-lime by smallholders was minimal. Overall, the current structure of the ag-lime value chain appears fragmented and needs improvement. Addressing soil acidity challenge through efficient ag-lime value chain could narrow lime supply-demand mismatches and increase widespread adoption by farmers to enhance crop productivity and food security in acidity-prone areas of the country.
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LIMES PRODUCTION COSTS VALUE CHAINS SOIL PH
Wenfei Tian Maria Itria Ibba Govindan Velu Shuanghe Cao Zhonghu He (2024, [Artículo])
CIMMYT Germplasm CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GERMPLASM FERULIC ACID FUNCTIONAL FOODS PHYTOCHEMICALS YIELD POTENTIAL WHEAT FOOD PRODUCTION
Tek Sapkota Sieglinde Snapp (2022, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CEREAL PRODUCTS PRODUCTION SYSTEMS CEREALS NITROGEN RICE WHEAT MAIZE
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