REMATTOOL-R is a tool that helps breeders to visualize and assess the relationship between yield and several agronomic traits. Users can download the application, an abstract of the paper describing the application, and a sample dataset. The sample dataset includes maize field trial result means of an experiment from the Water Efficient Maize for Africa (WEMA) Project funded by the Bill and Melinda Gates Foundation (BMGF, Grant # OPP1019943) and the Howard Buffet Foundation. The tabulated means are from data obtained from five locations across Kenya.
Performance trials (N=52) in two zones (West Shewa and Jimma) in Ethiopia. Trials comprise four nutrient management treatments, namely control with zero fertilizer; and three fertilizer recommendations to achieve the same target yield based on regional fertilizer recommendation, a Nutrient Expert (IPNI software) based recommendation and a soil-test NE based recommendation. Trials were conducted on-farm with four plots per farm. Observations include biomass and grain yields, as well as pre-sowing pH, nitrogen and phosphorus levels. Some N & K data are missing.
The Fieldbook2020 software provides offline capabilities for managing pedigrees, phenotypic data, seed stocks, and fieldbooks for a maize breeding program. CIMMYT is transitioning toward the use of an online Enterprise Breeding System for managing breeding information and performing analyses that will replace the use of Fieldbook2020. Institutes interested in adopting online breeding management and analysis systems are encouraged to request more information from Liz Jones (ej245@cornell.edu) with the Excellence in Breeding (EiB) Platform Module 5. She can provide more information about available options such as the Enterprise Breeding System, the Breeding Management System of the IBP, and the Breedbase systems. Additional information is available in the EiB Toolbox: https://excellenceinbreeding.org/toolbox . The user takes full responsibility for all manipulations done with Fiedbook2020. NOTE: CIMMYT is operating Fieldbook2020 in a run and maintain mode currently and will not provide support for the maintenance, update, or use of Fieldbook2020 after 2020. Fieldbook2020 users are encouraged to plan to transition to another system by or during 2021.
This research study the genomic-enabled prediction accuracy of the composition of the following sparse testing allocation design: (1) all non-overlapping (0 overlapping) lines in environments, (2) all overlapping (0 non-overlapping) lines tested in all the environments, and (3) combinations of the two previous cases where certain numbers of non-overlapping (NO)/overlapping (O) lines were distributed in the environments. We also studied cases where the size of the testing population was decreased. The study used two large maize data sets (T1 and T2). Four different genomic-enabled prediction models were studied, two models (M1 and M2) that do not include the genomic × environment interaction (GE), whereas models M3 and M4 incorporate two forms of modeling GE. The results show that genome-based models including GE (M3 and M4) captured more genetic variability with the GE component than the other models for both data sets. Also, models M3 and M4 provide higher prediction accuracy than models M1 and M2 for the different allocation designs comprising different combinations of NO/O lines in environments. Results indicate that substantial savings of testing resources can be achieved by optimizing the allocation design using genome-based models including genomic × environment interaction.
An association-mapping panel (DTMA) and two DH populations (DH1 and DH2) were used in the current study, which in total includes 487 materials. The dataset includes three types of files. One is the genotype of 487 lines sequenced by GbS, named DTMA_DH2_DH3-955690.hmp.txt; one is the genotype of 487 lines sequenced by rAmpSeq named genotype-rAmpSeq.csv; and the third type of files are the phenotypic data files named DH1-phenotype.csv, DH2-phenotype.csv and DTMA-phenotype.csv.
Each year, CIMMYT’s Global Maize Program conducts regional hybrid maize trials through a network of NARS and private seed companies in Asia under various management and environmental conditions (site summary attached in Appendix 4). These entries are compared evaluated against the performance of relevant checks from private seed companies and National Agricultural Research Programs. The objectives of these regional trials are: 1. To identify improved maize hybrids with higher yield and agronomic performance suitable for target product profiles; 2. To help partners identify improved maize hybrids that can potentially replace outdated, less productive, less resilient and/or less profitable commercial varieties in the market; 3. To monitor the breeding progress and enhance regional genetic gains in maize breeding and product development; and 4. To enrich and widen the genetic base of improved maize germplasm through germplasm exchange. Details of Regional Trials conducted by CIMMYT in Asia in 2017 and 2018, are based on the below Target Product Profiles. 1. Product profile 1 (Tropical Rainfed (with erratic/excess rainfall) (Appendix 2): Basic traits: Intermediate maturing, yellow, high yielding, tolerant to drought and waterlogging and resistant to TLB Nice-to-have / emerging traits: FSR, BLSB, FAW CRT-3-8 – Intermediate maturing elite pre-commercial hybrids regional trial (including external and internal checks). 2. Product profile 2 (Tropical Rainfed Dry)(Appendix 3): Basic traits: Intermediate maturing, yellow, high yielding, tolerant to drought and heat and resistance to FSR Nice-to-have / emerging traits: FAW, Charcoal Rot CRT-1&2 - Intermediate maturing elite pre-commercial hybrids regional trial (including external and internal checks).
The Zinc association mapping (ZAM) panel is a set of 923 elite inbred lines from the International Maize and Wheat Improvement Center (CIMMYT) biofortification breeding program. The panel represented wide genetic diversity for kernel Zn and is comprised of several lines with tolerance/resistance to an array of abiotic and biotic stresses commonly affecting maize production in the tropics, improved nitrogen use efficiency, and grain nutritional quality. The ZAM panel_923_LINES_GENO and Zinc association mapping (ZAM) panel_phenotypic data are two files with GBS and phenotypic data for zinc (Zn) from this population. From the ZAM panel, four inbred lines (two with high-Zn and two with low-Zn) were selected and used to form the bi-parental populations, namely DH population1 and DH population2. Genotypic and phenotypic data corresponding to these populations are DH populations1&2_255_LINES_GENO and DH population1_phenotypic data and DH population2_phenotypic data
This study projected the consumption demand of a few major food items of Nepal. On average, 62% of the total daily dietary energy of a Nepalese comes solely from cereals. As the economy of Nepal is rapidly emerging, and given that 25% of the total population of Nepal is still poor, this study projects the aggregate consumption demand for rice, wheat, maize and other cereals, pulses, vegetables, and fish and meat in Nepal by 2025 and 2030. Findings indicate that the consumption demand of the sampled commodities would increase with the GDP and population growth of the country. For example, by 2030, the demand for wheat, maize, and rice will increase by 63%, 46%, and 8%, respectively, from the consumption level in 2018/19. This study also demonstrates that the consumption demand for noncereal food items such as pulses, vegetables, meat and fish would also increase by 2030. Based on the findings, this study strongly suggests policymakers to invest both in cereal and noncereal subsectors of agriculture in Nepal to enhance the domestic production capacity. Investment in agricultural capacity enhancement can also reduce extreme poverty in Nepal, as more than 66% of the employed labor force in Nepal is currently engaged in agriculture.
The Global Popcorn Project began in 2014, by asking the basic question: How many popcorn accessions do we have in the Maize Collection of the CIMMYT Maize and Wheat Germplasm Bank? Six years later, we are closing in on answering that basic question and have also moved on to more complicated ones, with the help of an international team of graduate students and their supervising professors, from Mexico, Brazil and China. We are starting with the phenotyping guides (in English and Spanish) and the first data set compiled in 2014, using the methodology described in the manuals, and we will be continually updating this site with publications and their supporting data sets. Don’t miss the video of our phenotyping team in action! This project was carried out as a collection-related activity of the Maize Collection in the CIMMYT Germplasm Bank, supported by the CGIAR Genebank Platform and the Global Crop Diversity Trust. The graduate students involved in the project were supported during their internships in the germplasm bank as follows: Braulio Torres: Programa de Formación y Desarrollo de Científicos y Tecnólogos, Consejo Nacional de Ciencia y Tecnología (CONACYT-Mexico); Natalia Almeida: Programa Ciência sem Fronteiras - Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - Brazil) and Jing Li: Chinese Scholarship Council (CSC).
An experiment initiated in 1996 in the highlands of the state of San Luis Potosí, Mexico, evaluated different tillage methods and levels of soil cover under permanent raised beds for their effects on yield, profitability, and soil quality in an irrigated, summer maize- winter oats rotation. The experiment was located at the Experimental Station “San Luis” of the Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP) in Soledad de Graciano Sánchez, San Luis Potosí, Mexico (22° 13' 35.30" N 100° 50' 56.67"W, 1,838 m above sea level). The experiment evaluated four tillage methods; permanent, raised beds and the following three conventional methods: 1) Inversion tillage with a disc plow and disk harrowing to a depth of 30 cm (P+D), 2) disk harrowing to a depth of 30 cm (D), and 3) non-inversion tillage with a “multiplow” and disk harrowing to a depth of 30 cm (M+D). In all three conventional tillage treatments, 1.65 m wide beds were shaped after tillage. In treatments P+D, D, and M+D, soil preparation took place at the beginning of each maize and oats cycle. Four levels of soil cover were tested on the raised beds: zero (PB0%), one third (PB33%), two thirds (PB66%) and full soil cover (PB100%). The amount of residue to cover one-third, two-thirds and all of the soil surface, corresponds to 1.3, 2.6, and 4.0 t ha-1, respectively, and it was applied once a year after maize harvest. All residue was removed after harvest and the amount of residue for each treatment was returned every year. The permanent raised beds (PB) were created in 1996 and are 1.65 m wide. Furrows in the raised beds have been reshaped every five years. Plots were 8.25 m wide and 30.0 m long. Treatments were evaluated in a randomized complete block design with two replications. The data set contains daily weather data for the weather station closest to the experimental site for 1995-2018 (precipitation, minimum and maximum temperature), yield data (grain yield for maize and biomass yield for oats), maize height data for three seasons and soil quality data (texture, saturation point, pH, electric conductivity, macronutrients, micronutrients, penetration resistance, time to pond, bulk density, mean weight diameter after dry and wet sieving).
