Maize seed aid and seed systems development: Opportunities for synergies in Uganda

Jason Donovan Rachel Voss Pieter Rutsaert (2024, [Artículo])

In the name of food security, governments and NGOs purchase large volumes of maize seed in non-relief situations to provide at reduced or no cost to producers. At the same time, efforts to build formal maize seed systems have been frustrated by slow turnover rates – the dominance of older seed products in the market over newer, higher performing ones. Under certain conditions, governments and NGO seed aid purchases can support formal seed systems development in three ways: i) support increased producer awareness of new products, ii) support local private seed industry development, and iii) advance equity goals by targeting aid to the most vulnerable of producers who lack the capacity to purchase seeds. This study explores the objectives and activities of seed aid programmes in Uganda and their interactions with the maize seed sector. We draw insights from interviews with representatives of seed companies, NGOs and government agencies, as well as focus group discussions with producers. The findings indicated that seed aid programme objectives are largely disconnected from broader seed systems development goals. There is little evidence of public-private collaboration in design of these programmes. Better designed programs have the potential to align with varietal turnover objectives, commercial sector development and targeting of underserved markets could promote equity and ‘crowd in’ demand.

Seed Business Varietal Turnover Seed Aid CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SEED SYSTEMS SOCIAL INCLUSION MAIZE

Occurrence and Distribution of Physiological Races of Exserohilum turcicum in Maize Growing Regions of Mexico

Alexander Loladze Mateo Vargas Hernández amos Alakonya (2022, [Artículo])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SETOSPHAERIA TURCICA MAIZE FOLIAR DISEASES PHYSIOLOGICAL RACES

Stage-gate advancement and testing strategies for Product Development at CIMMYT

Berhanu Tadesse Ertiro (2022, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA STAGES BREEDING MAIZE TESTING

Weed management and tillage effect on rainfed maize production in three agro-ecologies in Mexico

Simon Fonteyne Abel Jaime Leal González Rausel Ovando Ravi Gopal Singh Nele Verhulst (2022, [Artículo])

Maize (Zea mays L.) is grown in a wide range of agro-ecological environments and production systems across Mexico. Weeds are a major constraint on maize grain yield, but knowledge regarding the best weed management methods is lacking. In many production systems, reducing tillage could lessen land degradation and production costs, but changes in tillage might require changes in weed management. This study evaluated weed dynamics and rainfed maize yield under five weed management treatments (pre-emergence herbicide, post-emergence herbicide, pre-emergence + post-emergence herbicide, manual weed control, and no control) and three tillage methods (conventional, minimum and zero tillage) in three agro-ecologically distinct regions of the state of Oaxaca, Mexico, in 2016 and 2017. In the temperate Mixteca region, weeds reduced maize grain yields by as much as 92% and the long-growing season required post-emergence weed control, which gave significantly higher yields. In the hot, humid Papaloapan region, weeds reduced maize yields up to 63% and pre-emergence weed control resulted in significantly higher yields than treatments with post-emergence control only. In the semi-arid Valles Centrales region, weeds reduced maize yields by as much as 65%, but weed management was not always effective in increasing maize yield or net profitability. The most effective weed management treatments tended to be similar for the three tillage systems at each site, although weed pressure and the potential yield reduction by weeds tended to be higher under zero tillage than minimum or conventional tillage. No single best option for weed management was found across sites or tillage systems. More research, in which non-chemical methods should not be overlooked, is thus needed to determine the most effective weed management methods for the diverse maize production systems across Mexico.

Corn Integrated Weed Management Manual Weed Control CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE WEED CONTROL MINIMUM TILLAGE ZERO TILLAGE

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

Gender analysis of household seed security : A case of maize and wheat seed systems in Nepal

Hom Nath Gartaula (2022, [Libro])

Seed Security Mountains CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SEED SYSTEMS MAIZE WHEAT ROLE OF WOMEN WOMEN'S PARTICIPATION

AGG-maize year 3 major achievements and next steps

Yoseph Beyene (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING PROGRAMMES INNOVATION HYBRIDS GERMPLASM

Introduction to line and hybrid development

Yoseph Beyene (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA INBRED LINES HYBRIDS MAIZE GERMPLASM

TELA Project: An update

Yoseph Beyene (2023, [Objeto de congreso])

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE HYBRIDS PEST INSECTS RESEARCH

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Kindie Tesfaye Vakhtang Shelia Pierre C. Sibiry Traore Dawit Solomon Gerrit Hoogenboom (2023, [Artículo])

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT's ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA CROP MODELLING DECISION SUPPORT SYSTEMS FORECASTING MAIZE