Phenotypic data collected in 2019 Stage 4 and 2020 Stage 5 trials for CIMMYT maize hybrid JH1104 as well as information about the trial sites are provided in this dataset. These trials were conducted through a network of partners, including NARS and private seed companies in Eastern Africa under various management and environmental conditions.
Dataset of daily fine particular matter (PM2.5) emissions for the period 2012 though 2020. Emissions were calculated from wheat and rice production statistics and active fires from the Visible Infrared Imaging Radiometer Suite (VIIRS) 375-m product. Rice harvest period was considered from October to November, and wheat harvest from April through May.
Data from two CIMMYT and KALRO household surveys representative of six maize production areas or agroecological zones in Kenya. The surveys were conducted in 2010 and 2013 collected data on farmer demographics, adoption of improved technologies and practices, marketing, access to agricultural information, and farmer adaptation to climate change.
The Stem Rust Resistance Screening Nursery is a single replicate nursery that contains diverse spring bread wheat (Triticum aestivum) germplasm adapted to all mega-environments with total 50-100 entries and white/red grain color.
CIMMYT develops and distributes elite tropical CIMMYT maize lines (CMLs) for use around the world. This dataset contains genotypic profiles for all 615 of the CML lines released by 2022 and fourteen temperate maize inbred lines. All lines were genotyped with 180 kompetitive allele-specific PCR (KASP) markers. These data were used to generate a reference fingerprinting dataset using 152 high-quality markers. These data were also used to perform analyses concerning the relationships between different CMLs and temperate materials in the accompanying journal article.
Wheat productivity in eastern Indo-Gangetic plain is sub-optimal that can largely be attributed to delayed wheat sowing and use of late sown varieties. Ideal time of sowing wheat in eastern India is first fortnight of November but in general, it gets delayed by 15-25 days. This delay puts farmers in a situation where they tend to prefer late sown varieties whose potential yields are low. By 2020, more than half wheat farmers in Bihar more than one third in eastern Uttar Pradesh were using late sown wheat varieties. To validate the effect of timely sowing and comparative performance of long and short duration varieties, multi-location on-farm trials were conducted continuously over five years starting from 2016-17. Krishi Vigyan Kendras, district-level extension centre of national agriculture research and extension system were involved in the process of evidence generation. Ten districts so ten centres were selected in a way that all agro-climatic zones of this area is covered. There were five treatments of sowing windows – 01 to 10 November, 11 – 20 November, 21 to 30 November, 01 – 15 December and 16 – 31 December. Varietal performance was compared in T3, T4 and T5 as short duration varieties can’t be sown before 20 November. There is asymmetry in distribution of samples within treatments and over years. That happened as trial was in farmer’s participatory mode and numbers were dependent completely on willingness of farmers to participate. Altogether, the trial was conducted at 3735 sites and we captured 60 variables including yield and yield attributing traits.
Empirical studies of early generation genomic selection strategies for parental selection or population improvement are still lacking in wheat and other major crops. We show the potential of rapid-cycle recurrent GS to increase genetic gain for grain yield in wheat. We show a consistent realized genetic gain for grain yield after three cycles of recombination of bi-parental F1’s, when summarized across two years of phenotyping.
This experiments were established with different rates of nitrogen in order to generate a wide range of values for NDVI and grain yield in order to develop a calibration model for the GreenSeeker in Sinaloa.
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 Latin America and similar agro-ecological zones. Following a rigorous trialing and a stage-gate advancement process culminating in the 2020 Stage 5 trials, CIMMYT advanced a total of four new elite maize hybrids in Latin America in 2022 for Product Profile LA-PP1. These are yellow and white maize hybrids adapted for the tropical lowlands. Phenotypic data collected in Stage 4 and Stage 5 trials for the four 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 NARS and private seed companies, in Latin America under various management and environmental conditions. Nuevos y mejorados híbridos desarrollados por el Programa Global de Maíz del CIMMYT se ponen a disposición de instituciones del sector público y privado, especialmente para aquellas instituciones colaboradoras interesadas en la comercialización y diseminación de semilla de maíz en Latinoamérica o en zonas agroecológicas similares. Después de un riguroso proceso de evaluación de germoplasma en distintas etapas que culminó en ensayos de evaluación de híbridos en etapa cinco, el CIMMYT avanzó cuatro nuevos híbridos élite en Latinoamérica en 2022 por el Perfíl de Producto LA-PP1. Estos son híbridos amarillos y blancos adaptados a las tierras bajas tropicales. Datos fenotípicos recopilados en los ensayos en etapa cuatro y cinco por los cuatro hibridos avanzados, además de información sobre los sitios están incluidos en este conjunto de datos. Estos ensayos fueron conducidos bajo diferentes condiciones de manejo y ambientes a través de redes colaborativas con instituciones de investigación pública y empresas semilleras de Latinoamérica.
In plant breeding, sparse testing methods have been suggested to improve the efficiency of the genomic selection methodology. The data provided in this dataset were used to evaluate four methods for allocating lines to environments for sparse testing in multi-environment trials. The analysis was conducted using a multi-trait and uni-trait framework. The accompanying article describes the results of the evaluation as well as a cost-benefit analysis to identify the benefits that can be obtained using sparse testing methods.
