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Replication Data for: Mix and manage: Cultivar mixtures can maintain yield under high wheat blast disease pressure
No full textWheat blast disease, which began in South America, has now spread to Asia and Africa, posing a serious threat to food security. The first outbreak in Bangladesh occurred in 2016, leading to the release of the blast-resistant wheat variety BARI Gom 33. Although seeds for this variety are limited, it remains crucial due to the ongoing threat of the disease. Our research assessed using BARI Gom 33 in mixture with other varieties under high disease pressure. Over three growing seasons, we tested combinations of BARI Gom 26, BARI Gom 30, and BARI Gom 33 to evaluate disease symptoms and crop yield.Results showed that while susceptible varieties suffered heavy yield losses, BARI Gom 33 maintained its yield even in mixtures. The mixture containing 67% BARI Gom 33 significantly reduced blast symptoms compared to single stand results. This study supports the use of BARI Gom 33 as the primary defense against wheat blast in Bangladesh. For farmers unable to access enough BARI Gom 33 seeds, mixing it with less resistant varieties is a recommended temporary solution
KASP and STS markers associated with the chromosome 3B short arm translocation (T3BL.3NsbS) from Leymus racemosus
No full textKASP markers were developed based on the genotyping results generated through DArTSeqTM and the AxiomTM Wheat Breeders Genotyping Array.
Genotyping results included Leymus racemosus, several T3BL.3NsbS translocation lines in CIMMYT elite spring bread wheat genetic backgrounds, and other CIMMYT elite parents.
KASP markers were designed based on the consensus sequence of DArTSeq and Axiom markers that could clearly distinguish the source and translocation lines from other CIMMYT elite parents.
The KASP markers were selected to be bi-allelic and are currently used at CIMMYT for transferring translocations of Leymus racemosus in additional elite spring bread wheat backgrounds
National-scale wheat area dataset of Ethiopia for the 2020/21 rainfed cropping seasons
No full text The national-scale wheat area dataset of Ethiopia for the 2020/21 rainfed cropping seasons shows the spatial distribution and density of rainfed wheat at 10 m spatial resolution for the 2020 Belg and 2020/21 Meher seasons at the national scale. The binary dataset depicts whether a pixel is classified as wheat or non-wheat (labeled as "1" and "0", respectively) during the period ranging from April to December 2020.
For datatset generation, state-of-the-art, efficient satellite data pre-processing, in-depth in situ data cleaning, and Random Forest classification methods have been applied to a multispectral, gap-filled Sentinel-2 satellite time series from April to December 2020 and the multi-source Ethiopian Crop Type 2020 (EthCT2020) ground reference dataset (Blasch et al., 2024).
This dataset with a classification overall accuracy of 80% was developed with the goal to complement the Ethiopian Wheat Rust Early Warning and Advisory System’s baseline data for the disease dispersal and environmentally suitability forecast models as well as building the basis for new information layers regarding the host susceptibility.
Overall, the dataset can serve as a baseline input parameter for crop models, climate models, crop disease and pest surveillance and forecasting, and agricultural monitoring in the smallholder cropping systems of Ethiopia.</p
Ethiopian crop type 2020 (EthCT2020) dataset
No full textThe observation of crop types is paramount for a myriad of agricultural and environmental remote sensing applications, including land use mapping, crop growth monitoring, and yield forecasting. Accurate georeferenced and quality-controlled crop type data is integral to the calibration of machine learning algorithms. However, publicly available field data remains scarce, particularly within the dynamic smallholder farming systems of sub-Saharan Africa.
To address this gap, the EthCT2020 dataset was established for the 2020/21 cropping season in Ethiopia. This dataset comprises 2,793 harmonized and quality-controlled in-situ samples of annual crops, representing seven crop groups and 22 classes across the diverse agricultural landscape of Ethiopia, with a specific emphasis on wheat-based farming systems. The data collection process involved a nationwide campaign implemented through a stratified sampling approach grounded in the wheat crop calendar, resulting in the acquisition of 1,263 samples from this campaign (GDCC; Source 1). Additionally, the dataset was enhanced by incorporating 1,530 wheat samples sourced from specialized databases dedicated to wheat disease surveillance, specifically the Wheat Rust Toolbox (WRTB; Source 2), which contributed 734 samples, and from an internal farm household survey database (FHSD; Source 3), which added 796 samples. The field data were meticulously labeled in accordance with the guidelines established by the Joint Experiment for Crop Assessment and Monitoring (JECAM) and the FAO Indicative Crop Classification for the definition and collection of agricultural land data.
Extensive processing of the EthCT2020 dataset was conducted to ensure its quality and accuracy, which included data harmonization and mixed pixel assessment through visual interpretation of satellite imagery, coupled with quality control measures based on Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is distinctive in its diversity and spatial precision, and it represents a valuable resource for further development of innovative remote sensing technologies and predictive models within the Ethiopian agricultural context. Additionally, it serves as a foundational input for climate models and forecasting systems for crop diseases and pests
Monitoreo y caracterización fenológica y climatológica sobre áreas cultivadas en la región Mixteca Oaxaqueña
No full textEl conjunto de datos describe un monitoreo integral de la fenología en la región Mixteca de Oaxaca, México, una zona predominantemente agrícola de temporal en la que los pequeños agricultores cultivan milpa (maíz, frijol y calabaza) de manera tradicional. Para evaluar el impacto del cambio climático en los ciclos agrícolas, se emplearon datos satelitales de MODIS y datos meteorológicos en cuadrícula provenientes de plataformas como Daymet y AgERA5, con una resolución de 1 km, que abarcan desde 1980 hasta 2024. Estos datos permiten determinar con precisión el inicio, pico y final de la temporada de crecimiento de los cultivos en la región, así como analizar tendencias climáticas y fenoclimáticas a lo largo de más de 40 años.
Los análisis revelan que, en promedio, la temporada de cultivo comienza alrededor del 3 de junio, con el pico vegetal entre el 12 de agosto y el 9 de septiembre, y la finalización en finales de noviembre. Además, se detectó un aumento en la frecuencia de eventos de sequía y una disminución significativa en las precipitaciones a partir de 2013, evidenciando un cambio en las condiciones climáticas que afecta directamente la producción agrícola. Los mapas geoespaciales generados con TIMESAT permiten visualizar estos cambios a diferentes escalas, facilitando la toma de decisiones para la gestión agrícola y la adaptación al cambio climático.
El conjunto de datos incluye archivos en diversos formatos (PDF, ZIP, NetCDF, CSV), con información sobre precipitaciones, temperaturas, radiación solar y estadísticas fenológicas, abarcando desde 1980 hasta 2023. Estos datos serán utilizados para desarrollar soluciones personalizadas y específicas para cada subregión, con el fin de mitigar el impacto del cambio climático en los medios de vida de los pequeños agricultores en la región Mixteca.
The dataset describes a comprehensive monitoring of phenology in the Mixteca region of Oaxaca, Mexico, an area predominantly engaged in rainfed agriculture where small farmers traditionally cultivate milpa (maize, beans, and squash). To evaluate the impact of climate change on agricultural cycles, satellite-based MODIS data and gridded meteorological data from platforms such as Daymet and AgERA5 were used, with a resolution of 1 km, covering the period from 1980 to 2024. These data allow for precise determination of the start, peak, and end of the crops’ growing season, as well as analysis of climatic and phenoclimatic trends over more than 40 years.
The analyses reveal that, on average, the planting season begins around June 3rd, with the vegetation peak between August 12th and September 9th, and the harvest ending in late November. Additionally, an increase in the frequency of drought events and a significant decrease in rainfall since 2013 have been observed, indicating a change in climatic conditions that directly affects agricultural production. The geospatial maps generated with TIMESAT enable visualization of these changes across different scales, facilitating decision-making for agricultural management and climate adaptation.
The dataset includes files in various formats (PDF, ZIP, NetCDF, CSV) containing information on precipitation, temperatures, solar radiation, and phenological statistics, covering the period from 1980 to 2023. These data will be used to develop tailored, subregion-specific solutions aimed at mitigating the impacts of climate change on the livelihoods of small farmers in the Mixteca region.</p
2019 Product Announcement and Results of the Maize Regional Trials for Eastern and Southern Africa for 2018
No full textNew 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 eastern and southern Africa and similar agro-ecological zones. Following a rigorous trialing and a stage-gate advancement process CIMMYT advanced new elite maize hybrids in eastern and southern Africa in 2019. Phenotypic data collected in 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 eastern and southern Africa under various management and environmental conditions.
The summary results of the Regional Trials for CIMMYT Maize Hybrids in Eastern Africa for 2018 specific to different product profiles. The trials include:
IHYB18 – Intermediate maturing elite pre-commercial hybrids regional trial: Product profile 1A (Eastern Africa) - Basic traits (Intermediate maturing, white, high yielding, drought tolerant, NUE, and resistant to GLS, TLB, Ear rots, and MSV); Nice to have/emerging traits (MLN, Striga, FAW).
MLN 3WAY HYB18 - Intermediate maturing elite pre-commercial MLN 3way hybrids regional trial: Product profile 1B (Eastern Africa): Basic traits (Intermediate-maturing, white, high-yielding, drought tolerant, NUE, resistant to MLN, MSV, TLB); Nice to have/ emerging traits (FAW, Striga).
ILHYB18 – Intermediate-Late maturing elite pre-released and released hybrids regional trials: Product profile 2 (Eastern Africa) - Basic traits (Late-maturing, white, high-yielding, Drought tolerance, NUE, and resistant to GLS, TLB, MSV Ear Rots, and rust); Nice to have/ Emerging traits (FAW, MLN, Striga).
EHYB18 – Early/extra-early maturing elite pre-commercial hybrids regional trials: Product profile 3 (Eastern Africa) - Basic Traits (Early-maturing, white, high-yielding, Drought tolerant, and resistant to MSV and MLN, NUE); Nice to have traits (Heat tolerant, TLB, Rust, Striga).
Pro-A HYBSI7; Pro-A HYBSI8- Intermediate maturing elite pre-commercial Provitamin A hybrids regional trial: Product profile 3 (Southern Africa)- Basic traits (Nutritious maize, early-late maturity, orange and/ white, high yield, high pvA, high Zn, high Lysine and tryp, tolerance to drought, low N, heat and low soil pH stresses and tolerance to MSV, GLS, TLB and cob rot);Nice to have traits (MLN, FAW, Downy mildew, aflatoxin, rust, organoleptic test, bioavailability, suitability for value-added products).
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Using a Video-based Product Ranking Tool (VPRT) as a basis for market segmentation in Tanzania (Sorghum)
No full textThe sorghum data was collected from small-scale sorghum farmers in the Tanzanian regions of Dodoma and Shinyanga in November and December 2023. In the Dodoma region, two districts were selected: Kondoa and Kongwa, while in the Shinyanga region, the districts selected were Kishapu and Shinyanga. The sample size consists of 1,100 small-scale farmers. The farmer profiling questions covered the following topics: socio-demographic information, with variables such as gender, age, family size, and education; farm information variables, including farm size, crops grown, animals kept, input use, and key income sources; and information on sorghum farming variables, such as seed purchase, cropping system, seed volume and plot size, harvest volume, and usage questions. The data also includes the identification and selection of preferred sorghum seed variety concepts presented in video format. For the video data collection, eight product descriptions were conceptualized into videos, and the presentations were made by a seed multiplier. Each farmer saw only three of the eight videos at a given time (incomplete block design). The videos viewed by each farmer were selected at random, resulting in a total of 3,300 videos presented to 1,100 small-scale farmers overall
Gender Intentional Wheat Seed Delivery Pathways
No full textThis was a survey data collected from Ethiopia to assess the preference of men and women farmers for wheat traits and the sources of seeds in wheat production systems
Seed Dealer Survey 2022 in Bihar, India
No full textIn the eastern Indian state of Bihar, most wheat farmers access seeds from the private dealers. In spite of the high seed replacement rate (>85%), the farmers still use the age-old varieties (mean age >20 years). Since most farmers consider seed dealers as a valuable source of information, a study was designed to examine the willingness of private dealers to act as knowledge dissemination agents – by participating in workshops, demonstration trails, web-based training, etc. – and on different ways to incentivize them to ensure delivery of accurate information and quality seeds of recent varieties with desired traits.A trader survey was conducted in December 2021.
The survey covered 40 villages, where household surveys are already completed as part of the AGG project in 2021. As part of another CIMMYT project (Climate Resilient Agriculture for Bihar, CRA), detailed village surveys with key informants were already conducted in the same villages. A telephone interview with key informants from these villages revealed that farmers get wheat seeds from about 210 private dealers (5-6 dealers per village). These dealers formed the respondents of the dealer survey. The interviews were conducted in December 2021, using a semi-structured questionnaire
Gender-segregated focus group discussion (FGD) data on women's role in wheat cultivation, Madhya Pradesh, India
No full textThe dataset is generated as part of an empirical study to develop a mixed research methodology, taking variables from both 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. Focus Group Discussions (FGDs) were conducted in the second half of 2018 in Madhya Pradesh (India), where wheat is one of the main crops.
The empirical part of this study is 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).
From each of the three districts, 5 villages were selected for data collection. We restricted our study to those villages where wheat is one of the main crops. Of the total 15 villages, three included the GENNOVATE case study communities (non-random selection). The other 12 will be selected based on the remoteness to the state road highways (6 close, 6 remote). From each village, two wards (sub-units of village) will be selected based on the income poverty (one where most of the poor households reside, and other with the non-poor). Two FGDs were conducted (one for men, one for women) in the selected wards