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

Estandarización de nombres comunes de plantas yucatanenses, parte 1: mayas y yucatecos

Jorge Carlos Trejo Torres (2023, [Artículo])

Nombrar plantas es una necesidad para ocuparnos de ellas. En casos afortunados, una especie tiene un solo nombre común, aunque frecuentemente las especies tienen más de un nombre. En la península de Yucatán, los nombres en lengua maya son complejos en escritura y pronunciación. Aunque la estandarización de su escritura se ha trabajado, hoy día es común ver nombres mayas de plantas escritos sin reglas. Entonces, proponemos criterios para estandarizar la escritura o selección de nombres comunes de las plantas regionales. También planteamos que dicha estandarización se integre en medios confiables como bases de datos botánicas regionales.

BASES DE DATOS BOTANICAS CRITERIOS ESPAÑOL YUCATECO NOMENCLATURA FOLCLORICA LENGUA MAYA BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

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

Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize (Zea mays. L) under optimum and low nitrogen conditions

Noel Ndlovu Vijay Chaikam Berhanu Tadesse Ertiro Biswanath Das Yoseph Beyene Charles Spillane Prasanna Boddupalli Manje Gowda (2023, [Artículo])

Grain Yield Low Soil Nitrogen CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GRAIN NITROGEN SOIL CHEMICOPHYSICAL PROPERTIES MAIZE QUANTITATIVE TRAIT LOCI

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

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

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