Results from rapid-cycle recurrent genomic selection in spring bread wheat

Susanne Dreisigacker Paulino Pérez-Rodríguez Leonardo Abdiel Crespo Herrera Alison Bentley Jose Crossa (2023, [Artículo])

Genomic-Assisted Breeding Molecular Markers Pedigree Information Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOMICS GENETIC MARKERS WHEAT BREEDING PROGRAMMES

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

Smallholder maize yield estimation using satellite data and machine learning in Ethiopia

Zhe Guo Jordan Chamberlin Liangzhi You (2023, [Artículo])

The lack of timely, high-resolution data on agricultural production is a major challenge in developing countries where such information can guide the allocation of scarce resources for food security, agricultural investment, and other objectives. While much research has suggested that remote sensing can potentially help address these gaps, few studies have indicated the immediate potential for large-scale estimations over both time and space. In this study we described a machine learning approach to estimate smallholder maize yield in Ethiopia, using well-measured and broadly distributed ground truth data and freely available spatiotemporal covariates from remote sensing. A neural networks model outperformed other algorithms in our study. Importantly, our work indicates that a model developed and calibrated on a previous year's data could be used to reasonably estimate maize yield in the subsequent year. Our study suggests the feasibility of developing national programs for the routine generation of broad-scale and high-resolution estimates of smallholder maize yield, including seasonal forecasts, on the basis of machine learning algorithms, well-measured ground control data, and currently existing time series satellite data.

Sentinel-2 Smallholder Agriculture Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INTENSIFICATION SMALLHOLDERS AGRICULTURE YIELD FORECASTING

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

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

Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat

Jose Crossa Osval Antonio Montesinos-Lopez Morten Lillemo (2024, [Artículo])

Multispectral Imaging Grain Yield Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN YIELDS HIGH-THROUGHPUT PHENOTYPING SPRING WHEAT

Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

Manish Kakraliya madhu choudhary Mahesh Gathala Parbodh Chander Sharma ML JAT (2024, [Artículo])

The future of South Asia’s major production system (rice–wheat rotation) is at stake due to continuously aggravating pressure on groundwater aquifers and other natural resources which will further intensify with climate change. Traditional practices, conventional tillage (CT) residue burning, and indiscriminate use of groundwater with flood irrigation are the major drivers of the non-sustainability of rice–wheat (RW) system in northwest (NW) India. For designing sustainable practices in intensive cereal systems, we conducted a study on bundled practices (zero tillage, residue mulch, precise irrigation, and mung bean integration) based on multi-indicator (system productivity, profitability, and efficiency of water, nitrogen, and energy) analysis in RW system. The study showed that bundling conservation agriculture (CA) practices with subsurface drip irrigation (SDI) saved ~70 and 45% (3-year mean) of irrigation water in rice and wheat, respectively, compared to farmers’ practice/CT practice (pooled data of Sc1 and Sc2; 1,035 and 318 mm ha−1). On a 3-year system basis, CA with SDI scenarios (mean of Sc5–Sc8) saved 35.4% irrigation water under RW systems compared to their respective CA with flood irrigation (FI) scenarios (mean of Sc3 and Sc4) during the investigation irrespective of residue management. CA with FI system increased the water productivity (WPi) and its use efficiency (WUE) by ~52 and 12.3% (3-year mean), whereas SDI improved by 221.2 and 39.2% compared to farmers practice (Sc1; 0.69 kg grain m−3 and 21.39 kg grain ha−1 cm−1), respectively. Based on the 3-year mean, CA with SDI (mean of Sc5–Sc8) recorded −2.5% rice yield, whereas wheat yield was +25% compared to farmers practice (Sc1; 5.44 and 3.79 Mg ha−1) and rice and wheat yield under CA with flood irrigation were increased by +7 and + 11%, compared to their respective CT practices. Mung bean integration in Sc7 and Sc8 contributed to ~26% in crop productivity and profitability compared to farmers’ practice (Sc1) as SDI facilitated advancing the sowing time by 1 week. On a system basis, CA with SDI improved energy use efficiency (EUE) by ~70% and partial factor productivity of N by 18.4% compared to CT practices. In the RW system of NW India, CA with SDI for precise water and N management proved to be a profitable solution to address the problems of groundwater, residue burning, sustainable intensification, and input (water and energy) use with the potential for replication in large areas in NW India.

Direct Seeded Rice Subsurface Drip Irrigation Economic Profitability Energy and Nitrogen Efficiency CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CONSERVATION AGRICULTURE RICE SUBSURFACE IRRIGATION IRRIGATION SYSTEMS WATER PRODUCTIVITY ECONOMIC VIABILITY ENERGY EFFICIENCY NITROGEN-USE EFFICIENCY

Conservation agriculture based sustainable intensification: India updates

ML JAT (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CONSERVATION AGRICULTURE SUSTAINABLE INTENSIFICATION LAND MANAGEMENT TILLAGE PLANT ESTABLISHMENT BIOMASS WATER MANAGEMENT

Calibración de los modelos de pérdidas de suelo USLE y MUSLE en una cuenca forestal de México: caso El Malacate

Calibration of the USLE and MUSLE soil loss models in a Mexican forest watershed: El Malacate case study

JORGE VICTOR PRADO HERNANDEZ PEDRO RIVERA RUIZ FELIPE BENJAMIN DE LEON MOJARRO MAURICIO CARRILLO GARCIA ANTONIO MARTINEZ RUIZ (2016, [Artículo])

La cuantificación de la erosión hídrica de los suelos en cuencas hidrográficas sirve para conocer el grado de su deterioro y para implementar medidas de conservación que minimicen la pérdida del suelo. Dada la carencia de la información para cuantificar con precisión aceptable la erosión en México, es necesario estudiar su estimación con la información disponible mediante metodologías validadas con información experimental. Por tal motivo, el objetivo de este estudio fue calibrar los modelos USLE (EUPS o Ecuación Universal de la Pérdida del Suelo, por sus siglas en inglés) y MUSLE (EUPS modificada) en la microcuenca El Malacate, perteneciente a la cuenca del Lago de Pátzcuaro en Michoacán, México con información experimental de 2013.

Erosión del suelo Conservación del suelo Modelación hidrológica INGENIERÍA Y TECNOLOGÍA

Establishment of heterotic groups for hybrid wheat breeding

Yunbi Xu (2022, [Artículo])

Genomic Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE CHANGE CROPS FORECASTING PLANTS COMBINING ABILITY HETEROSIS HETEROTIC GROUPS MALE INFERTILITY PLANT HEIGHT WHEAT