CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 2015.
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 1973 to 2017.
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 1969 to 2017.
CIMMYT has Meteorological Records for each station office in Mexico. Many variables of weather are measured per day (rawdata) from which daily, monthly and yearly sumaries are generated. These climate variables are measured by meteorological station and some of them are shown daily or hourly. Some of those variables are: Temperature, Rainfall, Solar Radiation, Relative Humidity, Wind Speed, Evapotranspiration. These files include climate data from 1975 to 2016.
The emergence of wheat-blast disease in Bangladesh in the 2015-16 wheat (Triticum aestivum) crop threatens the food security of South Asia. As wheat is the second most important staple and India has been emerging as a net wheat exporter, a potential spread of the disease from Bangladesh to India could have devastating impacts on India’s overall food security. West Bengal state in eastern India shares a 2,217 km-long border with Bangladesh and has a similar agro-ecology in its nine border districts, enhancing the possibility that disease may enter India via West Bengal. The present study explores the possibility of a ‘wheat holiday’ policy in the nine border districts of West Bengal, India. Under the policy, farmers in these districts would stop wheat cultivation for a few years. The present study attempts to find economically feasible alternative crops to wheat by applying an ex ante assessment framework. Of the ten crops considered, only maize, lentils, gram (chick pea), urad (black gram), khesari (grass pea), rapeseed, mustard and potatoes are found to be feasible alternatives. Such substitution would need support to ease the transition including addressing the challenges related to the management of the alternative crops, ensuring adequate crop combinations and value chain development. Still, as wheat is a major staple, there is some urgency also to support further research on disease epidemiology and forecasting, as well as the development and dissemination of blast-resistant wheat varieties across South Asia
The survey was conducted in 2015 immediately after lentil harvest in six western Nepal districts: Surkhet, Dadeldhura (mid and far west hills) and Banke, Bardiya, Kailali, Kanchanpur from terai. These six districts were selected purposively for the survey and sub districts selection was based on lentil acreage. 600 samples were selected randomly.
Machine learning (ML) is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) from data, without being explicitly programmed to do this. ML is closely related to (and often overlaps with) computational statistics, which also focuses on making predictions through the use of computers. In general, ML explores algorithms that can learn from current data and make predictions on new data, through building a model from sample inputs. The field of statistics and ML had a root in common and will continue to come closer together in the future. In this paper we explore the novel deep learning (DL) methodology in the context of genomic selection. DL models with densely connected network architecture were compared with one of the most often used genome-enabled prediction models genomic best linear unbiased prediction (GBLUP). We used nine published real genomic data sets to compare the models and obtain a “meta picture” of the performance of DL models with a densely connected network architecture.
Phenotypic and climate/environment data collected for IWYP39 field and glasshouse trials conducted in 2017 by the Unversity of Adelaide, South Australia. Rainfall data for the field trial was collected at Glenthorne Farm, O'Halloran Hill, South Australia and reported by Martin Carson. Climate data provided for the field trial was collected at the Adelaide Airport, South Australia, and reported by the Australian Government, Bureau of Meteorology. Climate and growth environment data for the glasshouse study was collected and reported by The Plant Accelerator, Waite Campus, Urrbrae, South Australia. Greenhouse data consisted of various phenotypic measurements taken periodically for AVP1, NAS2 and PSTOL1 single genes and pyramided lines (461 lines). The Field data consisted of phenotypic measurements for AVP1 and NAS2 single genes (280 lines).
Household survey was conducted by International Maize and Wheat Improvement Centre (CIMMYT) as part of CGIAR research program on Climate Change Agriculture and Food Security (CCAFS) in Karnal district of Haryana state and Vaishali district of Bihar state in India. The overall aim is to identify high-yield low-emission development pathways in cereal production systems. To achieve this, specific objectives are as follows: (i) to identify various technologies and farm management practices that influence GHG emissions and (ii) to explore household socio-economic factors that determine the adoption of low-emission technologies and management practices at the farm level. The information collected through questionnaire was crop production, socio-economic and demographic conditions, climate risks in agriculture and adaptation and mitigation measures. The data presented was tillage and crop establishment methods, water management, crop type, N rate (kg/ha), yield (Mg/ha), CO2 emission (kgCO2e/ha), N2O (kgCO2e/ha), CH4 (kgCO2e/ha) and total GWP (kgCO2/ha).
Farmers’ participatory researchers managed long-term trails aimed to improve the productivity, profitability, and sustainability of smallholder agriculture in the EGP by activities carried out to address the objectives: 1. Understand farmer circumstances with respect to cropping systems, natural and economic resources base, livelihood strategies, and capacity to bear risk and undertake technological innovation. 2. Develop with farmers more productive and sustainable technologies that are resilient to climate risks and profitable for small holders. 3. Facilitate widespread adoption of sustainable, resilient, and more profitable farming systems.
