First detection of virulence in Puccinia striiformis f. Sp. tritici to wheat resistance genes Yr10 and Yr24 (=Yr26) in Mexico

JULIO HUERTA_ESPINO Ravi Singh (2017, [Artículo])

Near Isogenic Lines Virulence Spectrum Yellow Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA VIRULENCE PUCCINIA STRIIFORMIS WHEAT RESISTANCE GENES

Identification and characterisation of stripe rust resistance genes Yr66 and Yr67 in wheat cultivar VL gehun 892

Harbans Bariana Lakshmi Kant Naeela Qureshi Urmil Bansal (2022, [Artículo])

Kompetitive Allele Specific PCR Stripe Rust Yr Genes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC MARKERS MARKER-ASSISTED SELECTION RUSTS WHEAT GENES

Indicadores de integridad ecológica y salud ambiental para las cuencas de los ríos Yautepec y Cuautla, Morelos: primera etapa

Perla Alonso_EguíaLis JORGE LUIS IZURIETA DAVILA REBECA GONZALEZ VILLELA (2016, [Documento de trabajo])

Los objetivos del proyecto son: 1) Síntesis de la investigación que se ha realizado sobre monitoreo y biomonitoreo; 2) Análisis del cambio climático y del régimen de caudal en las cuencas; 3) Localización física y análisis de los factores de estrés al sistema, como descargas, calidad del agua y alteraciones hidrológicas generadas por modificaciones hidráulicas; 4) Localización de sitios de referencia para bioindicadores; y 5) Elaboración de un sistema de información geográfica.

Contaminación ambiental Ambiente acuático Indicadores ambientales Sistemas de información geográfica Río Yautepec Río Cuautla BIOLOGÍA Y QUÍMICA

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

Identification and validation of stable quantitative trait loci for yield component traits in wheat

Yong Zhang Zhonghu He xianchun xia Yan Zhang Shuanghe Cao (2023, [Artículo])

Grain Number Per Spike KASP Markers QTL Mapping Thousand Grain Weight CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA QUANTITATIVE TRAIT LOCI MAPPING TRITICUM AESTIVUM GRAIN YIELD COMPONENTS MARKER-ASSISTED SELECTION

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

Distribution of Puccinia striiformis f. sp. tritici races and virulence in wheat growing regions of Kenya from 1970 to 2014

Lesley Boyd sridhar bhavani Cristobal Uauy Annemarie Fejer Justesen Mogens Hovmoller (2022, [Artículo])

Cereals and Grains Pathogen Diversity Puccinia f. sp. tritici Stripe Rust Yellow Rust CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CEREALS FIELD CROPS FUNGI PATHOGENICITY RUSTS TRITICUM AESTIVUM

The input reduction principle of agroecology is wrong when it comes to mineral fertilizer use in sub-Saharan Africa

Gatien Falconnier Marc Corbeels Frédéric Baudron Antoine Couëdel leonard rusinamhodzi bernard vanlauwe Ken Giller (2023, [Artículo])

Can farmers in sub-Saharan Africa (SSA) boost crop yields and improve food availability without using more mineral fertilizer? This question has been at the center of lively debates among the civil society, policy-makers, and in academic editorials. Proponents of the “yes” answer have put forward the “input reduction” principle of agroecology, i.e. by relying on agrobiodiversity, recycling and better efficiency, agroecological practices such as the use of legumes and manure can increase crop productivity without the need for more mineral fertilizer. We reviewed decades of scientific literature on nutrient balances in SSA, biological nitrogen fixation of tropical legumes, manure production and use in smallholder farming systems, and the environmental impact of mineral fertilizer. Our analyses show that more mineral fertilizer is needed in SSA for five reasons: (i) the starting point in SSA is that agricultural production is “agroecological” by default, that is, very low mineral fertilizer use, widespread mixed crop-livestock systems and large crop diversity including legumes, but leading to poor soil fertility as a result of widespread soil nutrient mining, (ii) the nitrogen needs of crops cannot be adequately met solely through biological nitrogen fixation by legumes and recycling of animal manure, (iii) other nutrients like phosphorus and potassium need to be replaced continuously, (iv) mineral fertilizers, if used appropriately, cause little harm to the environment, and (v) reducing the use of mineral fertilizers would hamper productivity gains and contribute indirectly to agricultural expansion and to deforestation. Yet, the agroecological principles directly related to soil fertility—recycling, efficiency, diversity—remain key in improving soil health and nutrient-use efficiency, and are critical to sustaining crop productivity in the long run. We argue for a nuanced position that acknowledges the critical need for more mineral fertilizers in SSA, in combination with the use of agroecological practices and adequate policy support.

Manure Crop Yields Smallholder Farming Systems Environmental Hazards CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOLOGICAL NITROGEN FIXATION LEGUMES NUTRIENT BALANCE SOIL FERTILITY AGROECOLOGY YIELD INCREASES LITERATURE REVIEWS

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

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery – Retrieving maize LAI to access biomass and grain yield

Ahmed Kayad Francelino Rodrigues Marco Sozzi Francesco Pirotti Francesco Marinello Urs Schulthess Bruno Gerard Marie Weiss (2022, [Artículo])

PROSAIL Vegetation Indices Field Variability Digital Farming CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PRECISION AGRICULTURE MAIZE GRAIN YIELD BIOMASS VEGETATION VEGETATION INDEX