Using microsatellite data to estimate the persistence of field-level yield gaps and their drivers in smallholder systems

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

Dead level contour technical design parameters required for sustainable crop production in semiarid areas of Zimbabwe

Menas Wuta Isaiah Nyagumbo (2021, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE DRY SPELLS RAINWATER HARVESTING CROP PRODUCTION TECHNOLOGY

Avances en Agricultura Sustentable : Resultados de plataformas de investigación Hub Pacífico Norte 2010-2021

Simon Fonteyne Nele Verhulst (2022, [Libro])

Esta edición presenta los resultados de la red de plataformas en el Hub Pacífico Norte, misma que resulta de la colaboración entre el CIMMYT; el Patronato para la Investigación y Experimentación Agrícola del Estado de Sonora A.C. (PIEAES); el Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias (INIFAP); la Asociación de Agricultores del Río Sinaloa Poniente (AARSP); la Asociación de Agricultores del Río Fuerte Sur (AARFS); la Asociación de Agricultores del Río Culiacán (AARC); la Universidad Autónoma de Sinaloa (UAS); Servicios Agrofinancieros del Norte S.A. de C.V. (SAFINSA); el Club de Labranza de Conservación del Valle del Évora; Granera del Noroeste S.A. de C.V; y el Instituto de Ciencias Agrícolas de la Universidad Autónoma de Baja California (ICA-UABC). Los lectores podrán encontrar en este libro los resultados de las plataformas con más tiempo de operación, en donde ya se han podido generar suficientes datos para sacar conclusiones basadas en evidencias sólidas. Esperamos que el libro pueda servir de inspiración a los productores para que busquen que sus actividades en el campo sean más productivas, rentables y sustentables.

Plataformas de Investigación Maíz Amarillo Pulgón Áreas de extensión Módulos demostrativos Autosuficiencia Alimentaria Uso de Insumos Ganancias para el Productor Nodos de Innovación CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURA DE CONSERVACIÓN COSTOS DE PRODUCCIÓN EUTROFIZACIÓN MONOCULTIVO DEGRADACIÓN DEL SUELO CONTAMINACIÓN PLAGUICIDAS CAMBIO CLIMÁTICO PLATAFORMAS DE INNOVACIÓN EXTENSIÓN AGRÍCOLA AUTOSUFICIENCIA INSUMOS AGRÍCOLAS CONSERVATION AGRICULTURE PRODUCTION COSTS EUTROPHICATION MONOCULTURE SOIL DEGRADATION CONTAMINATION PESTICIDES CLIMATE CHANGE INNOVATION PLATFORMS AGRICULTURAL EXTENSION SELF-SUFFICIENCY FARM INPUTS

Sistemas de producción sostenibles y redes de innovación

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

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PRODUCTION SYSTEMS INNOVATION PRODUCTION SYSTEMS AGRIFOOD SYSTEMS

Nitrogen deficiency tolerance and responsiveness of durum wheat genotypes in Ethiopia

Tesfaye Geleta Aga Kebebew Assefa Bekele Abeyo (2022, [Artículo])

Low-Nitrogen Tolerance Yield Reduction Tolerant Genotypes Parental Donors CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA NITROGEN HARD WHEAT GENOTYPES

What the world eats: wheat

sridhar bhavani (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT VARIETY TRIALS PRODUCTION DATA

MLN disease diagnostics, surveillance, 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

Appropriate surveillance and diagnostics tools in preventing the spread of Maize Lethal Necrosis (MNL) to Southern Africa

Suresh L.M. (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE DISEASES SEED PRODUCTION MONITORING SYSTEMS TRAINING

Population and food in the early twenty-first century: meeting future food demand of an increasing population

Nurul Islam (1995, [Libro])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA FOOD POLICIES FOOD PRODUCTION FOOD SUPPLY FORECASTING

Remote sensing of quality traits in cereal and arable production systems: A review

Zhenhai  Li xiuliang jin Gerald Blasch James Taylor (2024, [Artículo])

Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi-platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Quality Traits Grain Protein CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA REMOTE SENSING QUALITY GRAIN PROTEINS CEREALS PRODUCTION SYSTEMS