Repositorio Nacional

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

Arbustos y pastos para restablecer la cobertura vegetal en zonas áridas del Sur de Bolivia

Santiago Lopez-Ridaura Ravi Gopal Singh (2022, [Libro])

Pastos CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA AGRICULTURA DE CONSERVACIÓN SUELO COBERTURA DE SUELOS FERTILIDAD DEL SUELO CAMBIO CLIMÁTICO GANADERÍA VEGETACIÓN ARBUSTOS CONSERVATION AGRICULTURE SOIL LAND COVER CLIMATE CHANGE ANIMAL HUSBANDRY VEGETATION SHRUBS

Biofortification to avoid malnutrition in humans in a changing climate: Enhancing micronutrient bioavailability in seed, tuber, and storage roots

Ana Luisa Garcia-Oliveira Mahalingam Govindaraj Rodomiro Ortiz (2023, [Artículo])

Bioaccessibility and Absorption Biofortified Crop Cultivars Genes and Genetic Markers Nutrient Acquisition Transport and Storage CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOAVAILABILITY ABSORPTION CLIMATE CHANGE GENETIC MARKERS GENETIC ENGINEERING NUTRIENTS TRANSPORT STORAGE

Mining alleles for tar spot complex resistance from CIMMYT's maize Germplasm Bank

Martha Willcox Juan Burgueño Daniel Jeffers Zakaria Kehel Rosemary Shrestha Kelly Swarts Edward Buckler Sarah Hearne Charles Chen (2022, [Artículo])

Maize Landraces Maize Genetic Resources Allelic Diversity Rare Alleles Phenotypic Characterization Tropical Maize Phyllachora maydis CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE LANDRACES GENETIC RESOURCES ALLELES FOLIAR DISEASES CLIMATE CHANGE

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Mustafa Kamal Timothy Joseph Krupnik (2024, [Artículo])

High-resolution mapping of rice fields is crucial for understanding and managing rice cultivation in countries like Bangladesh, particularly in the face of climate change. Rice is a vital crop, cultivated in small scale farms that contributes significantly to the economy and food security in Bangladesh. Accurate mapping can facilitate improved rice production, the development of sustainable agricultural management policies, and formulation of strategies for adapting to climatic risks. To address the need for timely and accurate rice mapping, we developed a framework specifically designed for the diverse environmental conditions in Bangladesh. We utilized Sentinel-1 and Sentinel-2 time-series data to identify transplantation and peak seasons and employed the multi-Otsu automatic thresholding approach to map rice during the peak season (April–May). We also compared the performance of a random forest (RF) classifier with the multi-Otsu approach using two different data combinations: D1, which utilizes data from the transplantation and peak seasons (D1 RF) and D2, which utilizes data from the transplantation to the harvest seasons (D2 RF). Our results demonstrated that the multi-Otsu approach achieved an overall classification accuracy (OCA) ranging from 61.18% to 94.43% across all crop zones. The D2 RF showed the highest mean OCA (92.15%) among the fourteen crop zones, followed by D1 RF (89.47%) and multi-Otsu (85.27%). Although the multi-Otsu approach had relatively lower OCA, it proved effective in accurately mapping rice areas prior to harvest, eliminating the need for training samples that can be challenging to obtain during the growing season. In-season rice area maps generated through this framework are crucial for timely decision-making regarding adaptive management in response to climatic stresses and forecasting area-wide productivity. The scalability of our framework across space and time makes it particularly suitable for addressing field data scarcity challenges in countries like Bangladesh and offers the potential for future operationalization.

Synthetic Aperture Radar Random Forest Boro Rice In-Season Maps CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SAR (RADAR) RICE FLOODING CLIMATE CHANGE