This study includes two manuals that describe the use of diverse phenotyping techniques for applied crop research, with an emphasis on the methods commonly used at the International Maize and Wheat Improvement Center (CIMMYT). Volume 1 (Interdisciplinary Approaches to Improve Crop Adaptation) outlines the basic theory behind physiological traits and Volume 2 (A Field Guide to Wheat Phenotyping) provides guidance on the accurate and reliable measurement of these traits throughout the wheat crop cycle. Additionally, other protocols not included in the manuals are provided in the present study.
The IWYP Hub based in Ciudad Obregon, Mexico, offers an array of services, technology, and germplasm resources to IWYP Partner and Aligned projects. This study provides general information about the Hub, as well as descriptions of several germplasm populations being utilized within the IWYP project. For more information about the IWYP Hub, please contact the IWYP Hub Coordinator, Dr. Jacinto Gimeno: J.Gimeno@cgiar.org .
This dataset includes experimental data collected from a long-term field experiment conducted over 20 years (1999-2019) in a semiarid region of central Mexico. The study focused on evaluating the effects of various soil management practices—including the use of permanent beds, crop residue management, and tied ridges—on the yield and profitability of maize (Zea mays L.) and wheat (Triticum aestivum L.) under rainfed conditions. The dataset includes: Grain yield data for maize and wheat, Biomass production measurements, Information on soil management practices applied during the experiment.
Weed development is one of the major constraints to cereal cropping systems in Southern Africa with potential severe crop losses. Understanding weed community responses to different conservation agriculture (CA) components (i.e., no-tillage, NT; crop rotation, R; and mulching, M) and/or their combinations is crucial in Southern Africa where farmers apply different combinations depending on local context. Here, for the first time, we assessed how weed density, community diversity and structure respond to different combinations of CA components [conventional tillage (CT), CT+M, CT+R, CT+M+R, NT, NT+M, NT+R, NT+M+R]. The study was carried out over three seasons at two locations with contrasting soil textures i.e., clayish, and sandy. At the sandy location, across seasons, weed density (number of individuals per unit area) and community diversity (distribution of individuals within the species) were significantly and positively affected by precipitation and not by cropping system. Weed richness (number of species) was affected by the interaction of season and cropping system, with the highest number of weed species being recorded in the NT+M+R system in the seasons with medium to high precipitation. At the clayish location, an opposite pattern was observed, and weed density was lower in seasons with medium-high precipitation than under low precipitation. Weed community diversity was 50 % higher under NT+M than under CT+R, whereas weed species richness decreased with the increase of precipitation. At both locations, the implementation of rotation and mulching either in NT or CT systems resulted in the modification of the structure of weed community with respect to CT and NT alone, and these CA combinations were associated with highest maize grain yield. Overall, eight weed species common to both locations were responsible for most of the community structure differences among cropping systems. Structural equation modelling showed that at the sandy location precipitation did not affect grain yield, but positively affected weed density, diversity, evenness, and richness. By contrast, at the clay location, precipitation positively affected grain yield, but did not modify weed density and evenness, and reduced weed community diversity and richness. At this location, weed density negatively affected grain yield. The differential weed-crop relationship supports the need to find a site-specific equilibrium between the control of weeds and the maintenance of their diversity.
The project aim is to phenotype photosynthetic traits from diverse lines of wheat and, combined with next generation genetic approaches, identify markers and genes associated with each trait. This dataset includes phenotypic data for 97 lines of the Watkins Core Collection. Next generation genetic approaches will enable the identification of markers and genes associated with each trait.
This dataset was generated as part of the Guiding Acid Soil Management Investments in Africa (GAIA) project, which aims to support evidence-based investments in acid soil management across sub-Saharan Africa. Within this project, researcher-managed on-farm trials were established to quantify crop yield responses to increasing lime application rates. The experimental design followed a randomized complete block design (RCBD) within each participating farm. Sixteen farms were selected to host the trial, and seven lime application rates were tested within each farm. Lime was applied at the onset of the trial (Year 1). There were three blocks per farm. Blocks were relabelled when the former could not be identified in subsequent season or year. Incomplete, isolated or missing blocks in a farm result either from data missingness or farm misidentification. Crop yields measured during the first year capture the direct response to lime application, while yields measured in the second year allow evaluation of the residual effects of the initial lime treatment. This dataset contains plot-level yield measurements for maize, common beans, and potatoes collected during the first and second years after trial establishment in Rwanda. Predicted soil properties associated with the experimental sites are available from external datasets and can be linked through the soil sample unique identifier (SSN). These soil datasets are available at: Predicted Soil Properties Dataset: hdl:11529/10549138 Predicted Soil Properties Dataset: doi:10.71682/10549329 To facilitate interoperability and harmonization of agronomic terminology, a lookup table is provided that maps variable names and terms used in this dataset to the terminology defined in the terminag GitHub repository . Data from the first year are available in the related dataset: https://doi.org/10.71682/10549434 .
This dataset was generated as part of the Guiding Acid Soil Management Investments in Africa (GAIA) project, which aims to support evidence-based investments in acid soil management across sub-Saharan Africa. Within this project, researcher-managed on-station trials were established to quantify crop yield responses to increasing lime application rates. The experimental design followed a randomized complete block design (RCBD) within each participating farm. Two farms were selected to host the trial, and seven lime application rates were tested within each farm. There were three blocks per farm. Crop yields measured during the first year capture the direct response to lime application, while yields measured in the second year allow evaluation of the residual effects of the initial lime treatment. This dataset contains plot-level yield measurements for maize, wheat, faba bean, and soybean collected during the first and second years after trial establishment in Ethiopia. Predicted soil properties associated with the experimental sites are available from external datasets and can be linked through the soil sample unique identifier (SSN). These soil datasets are available at: Predicted Soil Properties Dataset: hdl:11529/10549136 Predicted Soil Properties Dataset: doi:10.71682/10549328 To facilitate interoperability and harmonization of agronomic terminology, a lookup table is provided that maps variable names and terms used in this dataset to the terminology defined in the terminag GitHub repository . Data from the first year are available in the related dataset: https://doi.org/10.71682/10549433
This dataset was generated as part of the Guiding Acid Soil Management Investments in Africa (GAIA) project, which aims to support evidence-based investments in acid soil management across sub-Saharan Africa. Within this project, researcher-managed on-farm trials were established to quantify crop yield responses to increasing lime application rates. The experimental design followed a randomized complete block design (RCBD) within each participating farm. Nine farms were selected to host the trial, and seven lime application rates were tested within each farm. Lime was applied at the onset of the trial (Year 1). There were three blocks per farm. Blocks were relabelled when the former could not be identified in subsequent season or year. Incomplete, isolated or missing blocks in a farm result either from data missingness or farm misidentification. Crop yields measured during the first year capture the direct response to lime application. This dataset contains plot-level yield measurements for maize, common beans, and potatoes collected during the first year after trial establishment in Rwanda. Predicted soil properties associated with the experimental sites are available from external datasets and can be linked through the soil sample unique identifier (SSN). These soil datasets are available at: Predicted Soil Properties Dataset: hdl:11529/10549138 Predicted Soil Properties Dataset: doi:10.71682/10549329 To facilitate interoperability and harmonization of agronomic terminology, a lookup table is provided that maps variable names and terms used in this dataset to the terminology defined in the terminag GitHub repository . Data from the second year are available in the related dataset: https://doi.org/10.71682/10549436 .
This dataset was generated as part of the Guiding Acid Soil Management Investments in Africa (GAIA) project, which aims to support evidence-based investments in acid soil management across sub-Saharan Africa. Within this project, researcher-managed on-station trials were established to quantify crop yield responses to increasing lime application rates. The experimental design followed a randomized complete block design (RCBD) within each participating farm. Two farms were selected to host the trial, and seven lime application rates were tested within each farm. There were three blocks per farm. Crop yields measured during the first year capture the direct response to lime application. This dataset contains plot-level yield measurements for maize, wheat, faba bean, and soybean collected during the first year after trial establishment in Ethiopia. Predicted soil properties associated with the experimental sites are available from external datasets and can be linked through the soil sample unique identifier (SSN). These soil datasets are available at: Predicted Soil Properties Dataset: hdl:11529/10549136 Predicted Soil Properties Dataset: doi:10.71682/10549328 To facilitate interoperability and harmonization of agronomic terminology, a lookup table is provided that maps variable names and terms used in this dataset to the terminology defined in the terminag GitHub repository . Data from the second year are available in the related dataset: https://doi.org/10.71682/10549435 .
The dataset comprises information from 5,689 wheat fields, capturing crop management from sowing to harvesting, including detailed field-level inputs and production practices. Collected over six years (2016–17 to 2021–22) across Bihar and the eastern part of Uttar Pradesh, it includes yield measurements obtained through physical crop-cutting experiments. Complementing this, field-specific weather data such as temperature and solar radiation were accessed from secondary source (global gridded meteorological product AgERA5 v2.0) to align with the wheat-growing season, providing a comprehensive view of yield performance under varying management and weather conditions.
