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

Genotype×Environment interaction and stability analysis using GGE biplot for grain yield of bread wheat (Triticum aestivum) genotypes under low moisture stress areas of Ethiopia

Ayele Badebo Huluka Bekele Abeyo (2023, [Artículo])

Moisture Stress Grain Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOTYPE ENVIRONMENT INTERACTION DROUGHT STRESS STABILITY TRITICUM AESTIVUM LATTICE DESIGN

High Throughput-Phenotyping at CIMMYT: Experiences and needs

Francisco Pinto (2021, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING PROGRAMMES GENETIC GAIN CROSS-BREEDING TECHNOLOGY YIELD POTENTIAL FIELD EXPERIMENTATION

A 'wiring diagram' for sink strength traits impacting wheat yield potential

Gustavo Slafer John Foulkes Matthew Paul Reynolds Erik Murchie A Elizabete Carmo-Silva Simon Griffiths (2023, [Artículo])

Grain Number Grain Weight Yield Physiology CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING GRAIN HARVEST INDEX SOURCE SINK RELATIONS YIELD COMPONENTS WHEAT

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

Genetic effects of Rht-B1b and Rht-D1b dwarfing alleles on plant height and agronomic traits of bread wheat (Triticum aestivum L.) breeding lines

Zine El Abidine Fellahi Abderrahmane Hannachi Susanne Dreisigacker deepmala sehgal Hamenna Bouzerzour (2023, [Artículo])

Pleiotropic Effects Reduced Height Genes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT HEIGHT TRITICUM AESTIVUM YIELD COMPONENTS ALLELES BREEDING LINES

Mapping QTL for phenological and grain-related traits in a mapping population derived from high-zinc-biofortified wheat

Gopalareddy Krishnappa Govindan Velu (2023, [Artículo])

DArT-Seq Gene Mapping Yield Component Traits CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT QUANTITATIVE TRAIT LOCI CANDIDATE GENES QUANTITATIVE TRAIT LOCI MAPPING YIELD COMPONENTS BIOFORTIFICATION

Catching-up with genetic progress: Simulation of potential production for modern wheat cultivars in the Netherlands

João Vasco Silva Frits K. Van Evert Pytrik Reidsma (2023, [Artículo])

Context: Wheat crop growth models from all over the world have been calibrated on the Groot and Verberne (1991) data set, collected between 1982 and 1984 in the Netherlands, in at least 28 published studies to date including various recent ones. However, the recent use of this data set for calibration of potential yield is questionable as actual Dutch winter wheat yields increased by 3.1 Mg ha-1 over the period 1984 – 2015. A new comprehensive set of winter wheat experiments, suitable for crop model calibration, was conducted in Wageningen during the growing seasons of 2013–2014 and of 2014–2015. Objective: The present study aimed to quantify the change of winter wheat variety traits between 1984 and 2015 and to examine which of the identified traits explained the increase in wheat yield most. Methods: PCSE-LINTUL3 was calibrated on the Groot and Verberne data (1991) set. Next, it was evaluated on the 2013–2015 data set. The model was further recalibrated on the 2013–2015 data set. Parameter values of both calibrations were compared. Sensitivity analysis was used to assess to what extent climate change, elevated CO2, changes in sowing dates, and changes in cultivar traits could explain yield increases. Results: The estimated reference light use efficiency and the temperature sum from anthesis to maturity were higher in 2013–2015 than in 1982–1984. PCSE-LINTUL3, calibrated on the 1982–1984 data set, underestimated the yield potential of 2013–2015. Sensitivity analyses showed that about half of the simulated winter wheat yield increase between 1984 and 2015 in the Netherlands was explained by elevated CO2 and climate change. The remaining part was explained by the increased temperature sum from anthesis to maturity and, to a smaller extent, by changes in the reference light use efficiency. Changes in sowing dates, biomass partitioning fractions, thermal requirements for anthesis, and biomass reallocation did not explain the yield increase. Conclusion: Recalibration of PCSE-LINTUL3 was necessary to reproduce the high wheat yields currently obtained in the Netherlands. About half of the reported winter wheat yield increase was attributed to climate change and elevated CO2. The remaining part of the increase was attributed to changes in the temperature sum from anthesis to maturity and, to a lesser extent, the reference light use efficiency. Significance: This study systematically addressed to what extent changes in various cultivar traits, climate change, and elevated CO2 can explain the winter wheat yield increase observed in the Netherlands between 1984 and 2015.

Light Use Efficiency Potential Yield CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING LIGHT PHENOLOGY MAXIMUM SUSTAINABLE YIELD TRITICUM AESTIVUM WINTER WHEAT

Genome-wide association analyses of agronomic traits and Striga hermonthica resistance in pearl millet

Hussein Shimelis Chris Ojiewo Abhishek Rathore (2023, [Artículo])

Pearl millet (Pennisetum glaucum [L.] R. Br.) is a nutrient-dense, relatively drought-tolerant cereal crop cultivated in dry regions worldwide. The crop is under-researched, and its grain yield is low (< 0.8 tons ha−1) and stagnant in the major production regions, including Burkina Faso. The low productivity of pearl millet is mainly attributable to a lack of improved varieties, Striga hermonthica [Sh] infestation, downy mildew infection, and recurrent heat and drought stress. Developing high-yielding and Striga-resistant pearl millet varieties that satisfy the farmers’ and market needs requires the identification of yield-promoting genes linked to economic traits to facilitate marker-assisted selection and gene pyramiding. The objective of this study was to undertake genome-wide association analyses of agronomic traits and Sh resistance among 150 pearl millet genotypes to identify genetic markers for marker-assisted breeding and trait introgression. The pearl millet genotypes were phenotyped in Sh hotspot fields and screen house conditions. Twenty-nine million single nucleotide polymorphisms (SNPs) initially generated from 345 pearl millet genotypes were filtered, and 256 K SNPs were selected and used in the present study. Phenotypic data were collected on days to flowering, plant height, number of tillers, panicle length, panicle weight, thousand-grain weight, grain weight, number of emerged Striga and area under the Striga number progress curve (ASNPC). Agronomic and Sh parameters were subjected to combined analysis of variance, while genome-wide association analysis was performed on phenotypic and SNPs data. Significant differences (P < 0.001) were detected among the assessed pearl millet genotypes for Sh parameters and agronomic traits. Further, there were significant genotype by Sh interaction for the number of Sh and ASNPC. Twenty-eight SNPs were significantly associated with a low number of emerged Sh located on chromosomes 1, 2, 3, 4, 6, and 7. Four SNPs were associated with days-to-50%-flowering on chromosomes 3, 5, 6, and 7, while five were associated with panicle length on chromosomes 2, 3, and 4. Seven SNPs were linked to thousand-grain weight on chromosomes 2, 3, and 6. The putative SNP markers associated with a low number of emerged Sh and agronomic traits in the assessed genotypes are valuable genomic resources for accelerated breeding and variety deployment of pearl millet with Sh resistance and farmer- and market-preferred agronomic traits.

High-Yielding Varieties Striga-Resistant Agronomic Parameters CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENOME-WIDE ASSOCIATION STUDIES STRIGA HERMONTHICA PEARL MILLET