The database consists of data collected over seven seasons to evaluate maize productivity among smallholder farmers in Malawi, focusing on the performance of various maize genotypes under distinct management practices. The key objectives of the study included assessing the interactions between genotype (G), environment (E), and management (M) to optimize maize production amid climatic variability and soil fertility challenges.
More than 60% of Zambia's population lives in rural areas and relies on rain-fed agriculture, with smallholder farmers playing a key role in food production. However, their dependence on the climate makes them vulnerable. It is essential to sustainably increase food production to feed a growing population and address climate change. Sustainable Intensification Practices (SIPs) are a solution that increases productivity without harming the environment. Examples of SIPs include Conservation Agriculture (CA), agroforestry, and the use of improved fertilizers. Although SIPs have benefits such as improving soil fertility and increasing productivity, their adoption is low due to obstacles like competition for crop residues, credit and labor constraints, and technical limitations. Additionally, farmers' risk and time preferences influence the adoption of these practices. To better understand these factors, data were collected from two household surveys conducted in nine districts of Zambia, located in the Eastern (Chipata, Lundazi and Sinda), Northern (Kasama, Mbala and Senga Hill), and Southern provinces (Choma, Mazabuka and Monze), where CIMMYT promotes various SIPs. The surveys were carried out in specific communities using the mother and baby trial approach of the SIFAZ project. Households of farmers hosting mother and baby trials, as well as other farmers in each camp, were randomly sampled. In total, 678 households were surveyed in 2021 and 646 in 2022, achieving a balanced panel of 1292 observations. Adoption refers to the use of CA practices on non-demonstration and non-trial plots that are not supported by external projects. Ethical approval was obtained for both surveys. This study in Zambia uses this data to analyze how risk and time preferences affect the adoption of CA-based practices, concluding that risk aversion and impatience reduce the likelihood of adoption.
Sustainable intensification practices, such as intercropping maize with grain legumes, are essential for smallholder farmers in southern Africa to address climate change and declining soil fertility. A study in Zambia compared different diversification strategies (single-row intercropping, strip cropping, and crop rotation) with sole maize. The results showed that while there were no significant differences in individual yields, intercropping systems provided important nutritional and economic benefits. However, these systems required more labor, which was not preferred by farmers. Soil organic carbon content and pH generally decreased without differences between systems. In summary, maize-legume intercropping can improve food security and agricultural benefits, although it requires more labor, which could be addressed with appropriate mechanization.
Agriculture is the most severely affected sector due to drought-like situations. Bihar is one of the most vulnerable states to drought due to its geographical location, and its high dependency on rainfed agriculture. The first survey, in Kharif season 2022 covered 518 farmers from 11 districts, 39 blocks and 79 villages of Bihar in October 2022. The second survey was the phone survey which was conducted to collect the data for the Rabi season 2022. We interviewed 339 farmers out of the 518 farmers in June 2023. To validate the first phone survey, we interviewed 229 farmers out of the 518 farmers. These drought impact surveys were designed in a way that data are collected from randomly selected farmers spread uniformly within a KVK domain/district. The survey questionnaire captures all production practices applied by farmers from land preparation to harvesting, including detailed sections on crop establishment, sources and methods of irrigation, fertilizer use, weed control and cost of irrigation. Data is captured through the electronically enabled Open Data Kit (ODK) tool on mobile phone or tablet. This contributes as a useful database which can be used by researchers, and policymakers for developing Bihar’s drought management strategy to protect the farmers from future drought-like situations.
In this data base, representative georeferenced farmer survey data from Zambia from 2011 are combined with climate data to estimate storage losses, analyze their relationship with climate, and estimate the effect of climate change on storage losses. The storage loss data include importance of different pests (maize weevils and larger grain borer), and farmers’ estimates of storage loss due to both pests, in grain and cobs. The climate data include temperature (from WorldClim) and relative humidity (from CHIRTS) over the storage season in 2011.
A standard quantitative genetic model was used to examine how genotype-environment interactions have changed over the past decades from four spring wheat trial data sets. The variability of cross interactions for yield from one year to another is explained in more than 70% by climatic factors.
Dataset of daily canopy temperature and meteorological data from the ECMWF’s AgERA5 product for the period 1979 though 2020, and for 785 points belonging to the International Wheat Improvement Network (IWIN). Wheat canopy temperature was estimated from a linear model using maximum air temperature, vapor pressure deficit, and solar radiation as inputs. The model was calibrated using multiple measurements of wheat canopy temperature.
Household survey was conducted by International Maize and Wheat Improvement Centre (CIMMYT) as part of CGIAR research program on Climate Change Agriculture and Food Security (CCAFS) in Karnal district of Haryana state and Vaishali district of Bihar state in India. The overall aim is to identify high-yield low-emission development pathways in cereal production systems. To achieve this, specific objectives are as follows: (i) to identify various technologies and farm management practices that influence GHG emissions and (ii) to explore household socio-economic factors that determine the adoption of low-emission technologies and management practices at the farm level. The information collected through questionnaire was crop production, socio-economic and demographic conditions, climate risks in agriculture and adaptation and mitigation measures. The data presented was tillage and crop establishment methods, water management, crop type, N rate (kg/ha), yield (Mg/ha), CO2 emission (kgCO2e/ha), N2O (kgCO2e/ha), CH4 (kgCO2e/ha) and total GWP (kgCO2/ha).
