Does access to improved grain storage technology increase farmers' welfare? Experimental evidence from maize farming in Ethiopia

Hugo De Groote Bart Minten (2024)

Seasonal price variability for cereals is two to three times higher in Africa than on the international reference market. Seasonality is even more pronounced when access to appropriate storage and opportunities for price arbitrage are limited. As smallholder farmers typically sell their production after harvest, when prices are low, this leads to lower incomes as well as higher food insecurity during the lean season, when prices are high. One solution to reduce seasonal stress is the use of improved storage technologies. Using data from a randomised controlled trial, in a major maize-growing region of Western Ethiopia, we study the impact of hermetic bags, a technology that protects stored grain against insect pests, so that the grain can be stored longer. Despite considerable price seasonality—maize prices in the lean season are 36% higher than after harvesting—we find no evidence that hermetic bags improve welfare, except that access to these bags allowed for a marginally longer storage period of maize intended for sale by 2 weeks. But this did not translate into measurable welfare gains as we found no changes in any of our welfare outcome indicators. This ‘near-null’ effect is due to the fact that maize storage losses in our study region are relatively lower than previous studies suggested—around 10% of the quantity stored—likely because of the widespread use of an alternative to protect maize during storage, for example a cheap but highly toxic fumigant. These findings are important for policies that seek to promote improved storage technologies in these settings.

Article

Hermetic Storage Randomised Controlled Trial CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA STORAGE PILOT FARMS SEASONALITY WELFARE MAIZE

Transpiration of a tropical dry deciduous forest in Yucatan, Mexico

EVELYN RAQUEL SALAS ACOSTA José Luis Andrade Torres Jorge Perera ROBERTH ARMANDO US SANTAMARIA bernardo figueroa-espinoza Jorge M. Uuh-Sonda EDUARDO CEJUDO ESPINOSA (2022)

The study of forest hydrology and its relationships with climate requires accurate estimates of water inputs, outputs, and changes in reservoirs. Evapotranspiration is frequently the least studied component when addressing the water cycle; thus, it is important to obtain direct measurements of evaporation and transpiration. This study measured transpiration in a tropical dry deciduous forest in Yucatán (Mexico) using the thermal dissipation method (Granier-type sensors) in representative species of this vegetation type. We estimated stand transpiration and its relationship with allometry, diameter-at-breast-height categories, and previously published equations. We found that transpiration changes over time, being higher in the rainy season. Estimated daily transpiration ranged from 0.562 to 0.690 kg m–2 d–1 in the late dry season (April–May) and from 0.686 to 1.29 kg m–2 d–1 in the late rainy season (September–October), accounting for up to 51% of total evapotranspiration in the rainy season. These daily estimates are consistent with previous reports for tropical dry forests and other vegetation types. We found that transpiration was not species-specific; diameter at breast height (DBH) was a reliable way of estimating transpiration because water use was directly related to allometry. Direct measurement of transpiration would increase our ability to accurately estimate water availability and assess the responses of vegetation to climate change. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Article

SAP FLUX SEASONALITY STAND TRANSPIRATION EVAPOTRANSPIRATION DRY DECIDUOUS FOREST BIOLOGÍA Y QUÍMICA CIENCIAS DE LA VIDA BIOLOGÍA VEGETAL (BOTÁNICA) ECOLOGÍA VEGETAL ECOLOGÍA VEGETAL

Calibrated multi-model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

Nachiketa Acharya Carlo Montes Timothy Joseph Krupnik (2023)

Bangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate-risk-prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi-model ensemble (CMME) of seven state-of-the-art general circulation models (GCMs) from the North American Multi-Model Ensemble project. The lead-1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA-based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real-time seasonal rainfall forecasts using this new forecast system.

Article

Multi-Model Ensemble Seasonal Forecasting CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CLIMATE SERVICES FORECASTING MONSOONS

First report of the dagger nematodes xiphinema index and xiphinema pachtaicum on grapevine in Algeria

fouad mokrini Rachid Lahlali Abdelfattah DABABAT (2023)

Article

Xiphinema pachtaicum CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA XIPHINEMA INDEX DIAGNOSIS GRAPEVINES

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)

Article

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

Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize

Alexander Loladze Francelino Rodrigues Cesar Petroli Felix San Vicente Garcia Bruno Gerard Osval Antonio Montesinos-Lopez Jose Crossa Johannes Martini (2024)

Article

Common Rust Rp1 Locus CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA RUSTS REMOTE SENSING VEGETATION INDEX MAIZE CHROMOSOME MAPPING

Associations between endogenous spike cytokinins and grain-number traits in spring wheat genotypes

Gemma Molero Carolina Rivera-Amado Matthew Paul Reynolds John Foulkes (2024)

Article

Spike Cytokinins Grain Number Fruiting Efficiency Wheat Breeding CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SPIKES CYTOKININS GRAIN FRUITING HARVEST INDEX WHEAT PLANT BREEDING

Molecular characterization of divergent isolates of Citrus bent leaf viroid (CBLVd) from citrus cultivars of Punjab, Pakistan

Abdelfattah DABABAT Yong Suk Chung (2023)

Article

Biological Indexing Sequencing Analysis Citrus Orchards CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PHYLOGENETIC ANALYSIS CITRUS RNA DNA

Morphological characterization of native maize populations of the ratón race from Coahuila, Mexico

Froylan Rincon Cesar Petroli (2023)

Article

Repeatability Index Representative Subset of Diversity Maize Raton Race CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA GENETIC DIVERSITY (AS RESOURCE) ZEA MAYS MORPHOLOGY PHENOTYPING VARIETIES

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)

Article

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