New and improved maize hybrids, developed by the CIMMYT Global Maize Program, are available for uptake by public and private sector partners, especially those interested in marketing or disseminating hybrid maize seed across rainfed lowland tropics of South Asia and similar agro-ecological zones. Following a rigorous trialing and a stage-gate advancement process culminating in Stage 5 trials, CIMMYT advanced a total of five new elite maize hybrids in South Asia in 2023. Phenotypic data collected in Stage 4 and Stage 5 trials for the selected hybrids as well as information about the trial sites are provided in this dataset. These trials were conducted through a network of partners, including NARES and private seed companies, in South Asia under various management and environmental conditions. Each hybrid is associated with one of the following product profiles for South Asia: Waterlogging and Drought-Tolerant (SAWLDT), Drought-Tolerant (SADT), or Heat- and Drought -Tolerant (SAHDT).
Disease resistance improvement efforts in plant breeding can help to reduce the negative impact of biotic stresses on crop production.Disease resistance can be assessed through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specially trained staff. Remote sensing (RS) tools can also be used to measure traits such as vegetation indices that can also be used to assess plant responses to diseases. This dataset contains phenotypic and genotypic data from a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH). Data from VS and RS methods for assessing common rust resistance were used in genome wide association study (GWAS) as well as genomic prediction (GP) analyses. A report on the comparison of the results of these analyses is provided in the accompanying article.
Gender equality is an indicator of sustainable development and also a means toward it. One of the Sustainable Development Goals (SDGs) set by United Nations is to achieve gender equality and empowerment of women and girls by 2030 (United Nations 2017). Empowerment of rural women is considered also as a necessary pre-requisite to attain food security and alleviate poverty in developing countries. While a number of studies address women empowerment as a developmental outcome, the deterministic role of rural women empowerment on agrarian development has not received sufficient research focus. On one hand, the quantitative empirical studies addressing technological change often limit the gender dimension to a binary variable on sex of the household head. The key roles and responsibilities of women members of the farm household, who are directly or indirectly involved in crop and livestock production, are overlooked by doing so. One the other hand, the in-depth qualitative case studies are not sufficiently broad (small sample size) to allow for generalization. Against this backdrop, the proposed study attempts to develop a mixed research methodology taking variables from quantitative household surveys and qualitative case studies for quickly and effectively capturing rural women involvement and empowerment and their ramifications on technological change and farmer livelihoods. The empirical analysis will be based on Focus Group Discussions (FGDs) and household survey data, conducted in the second half of 2018 in Madhya Pradesh (India), where wheat is one of the main crops. The study frame was built in close collaboration with the CGIAR Gender and Agriculture Research Network, and also be a ‘pilot’ study for identifying and integrating gender variables in the adoption-impact studies in CRP WHEAT and MAIZE Programmes. The overall objective of the project is to better understand the importance of women involvement in agriculture and women empowerment on technological change and rural livelihoods in India, focusing on impact heterogeneity and the role of different social institutions. There are two sets of research questions – first specific to the study area, and second more generic to the developing countries. The research questions specific to the study area are shown below. 1. Which of the individual / household / community characteristics are the key determinants of women empowerment in agriculture? 2. What role do gender plays on diffusion of varietal technologies and sustainable intensification practices in wheat? 3. What are the impacts of women empowerment and gender roles on household food insecurity? The more generic research questions are concerning (a) better technology targeting and (b) development of a variable set on gender to be used for quantitative data collection. The research questions framed in this connection are shown below. 4. How should the technology dissemination and targeting strategies change when the role, responsibilities, and preferences of women farmers are addressed? 5. Which are the easy to observe household attributes that could stand proxy for woman empowerment in quantitative studies on technological change? The empirical part of this study will be based on data collected from three districts of Madhya Pradesh, India – Jabalpur, Mandla and Damoh. Madhya Pradesh is one of the states with largest wheat growing area (19% of wheat area in India) but with lower wheat productivity (2.85 tons) compared to other major producers (4.29 tons in Punjab and 3.98 tons in Haryana in 2014-15 season). While Mandla and Damoh are lower productive districts within the state, Jabalpur farmers experience moderately high wheat productivity. Mandla and Damoh are also categorized as the disadvantaged districts by Government of India. The selected districts contain the three GENNOVATE case-study communities.
High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three different emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models in the historical evaluation) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs. These downscaled data can serve as high-quality inputs for impact models, including agro-ecological models. Overall, the results of this study are expected to facilitate climate change impact assessment and model comparison research in Ethiopia.
The Green Revolution has led to intensive, irrigated rice-wheat systems in the Indian subcontinent that has helped the nations reduce poverty and food insecurity. The limited turn-around time between rice harvest and sowing of the winter crop (wheat) and mechanized harvesting of rice pose a critical challenge for farmers to sustainably handle the surplus rice residues. Because only a few economically viable residue-management alternatives are readily available, a majority of the 2.5 million rice-wheat farmers of the Indo Gangetic Plains (IGP) burn an estimated 23 million metric tons of rice stubble in October and November. Residue burning causes an emission of short-lived climate pollutants, such as methane and black carbon. More importantly, the decrease in air quality due to rice stubble burning has a significant adverse effect on human pulmonary functions. In the recent past, rice residue burning and the resultant increase in air pollution have received significant media attention. However, the residue burning of rabi (winter) crops that occurs during the March-May period – when wheat crop residue is burnt – goes rather unnoticed. The preliminary analysis of remote sensing data indicated that wheat residue burning is prevalent in the Central Indian states, especially Madhya Pradesh. The potential technologies to manage wheat residue (e.g., zero tillage direct-seeded rice) are not yet popular in Central India. Against this background, the objective of the study can be stated as follows – 1. Groundtruthing of satellite data on wheat residue burning and connecting farming system characteristics to farm-level residue burning 2. Estimating the economic and environmental impacts (e.g., GHG emission) of wheat residue burning in the Madhya Pradesh state of India 3. Designing interventions to incentivize farmers to adopt farming technologies that avoid residue burning while preparing land for Kharif crops. A farm household survey and village survey were conducted in three districts of Madhya Pradesh, where residue burning poses a serious environmental concern, such as Rajgarh, Vidisha, Hoshangabad.This dataset contains the village survey data collected through key informant interviews in 21 randomly selected villages with rice-wheat production systems.
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.
Development of a methodology for mechanical maize meter evaluation that compares diverse meters in terms of seed singulation, seed damage, and spatial distribution
In plant breeding research, several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes. To increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype × environment interaction (GE), deep learning (DL) neural networks have been developed.These analyses can potentially include phenomics data obtained through imaging. The two datasets included in this study contain phenomic, phenotypic, and genotypic data for a set of wheat materials. They have been used to compare a novel DL method with conventional GP models.The results of these analyses are reported in the accompanying journal article.
TDa0000194 is a female flowering and white tuber fleshed water yam clone ( Dioscorea alata ) with less tendency to oxidize often used as trait progenitor in IITA breeding program. It is a released variety in Nigeria. TDa0200012 is a male flowering and anthracnose tolerant water yam ( Dioscorea alata ) parental clone. TDr9902789 is male flowering white Guinea yam ( Dioscorea rotundata ) breeding line tolerant to yam mosaic virus often used as a trait progenitor in IITA white Guinea yam breeding program. Gwagwa is a female flowering white Guinea yam ( Dioscorea rotundata ) landrace variety in Nigeria. It is a short vine length and less tendency to climb landrace clone producing multiple shoots and tubers.
“Benjamin” (CIP105085.2) is a modern variety first launched in Peru in 2010. It is tracing back to a cross of SR02.177 (CIP-102025.3) x INIA-100 (CIP-102033.5). Storage root flesh color is deep orange and taste type is moist and sweet. This variety is widely adapted within temperate dry lands of the tropics and subtropics and suitable for early harvest (90 days). The number of commercial roots per plant is very high with an excellent form and size for fresh markets. Yields are up to 60 t/ha.
