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- Author or Editor: B. Srivastava x
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Abstract
A fungicide-spray scheduling scheme for tomatoes called TOM-CAST (tomato forecaster) was adapted for use with operational weather data in order to increase the number of users by eliminating the need for in-field measurements of hourly temperature and leaf wetness duration. Such schemes reduce cost, environmental risk, and the development of resistance to the fungicide. Duration of wetness was estimated as the length of time that the dewpoint depression (T−Td) remained between two specified limits, indicating the onset and offset of wetness. Several methods of obtaining the necessary temperature and dewpoint data were investigated. The preferred method, considering accuracy and simplicity, involved synthesis of hourly temperatures from locally observed daily maximum and minimum temperatures, and estimation of dewpoints from two Environment Canada hourly weather stations. With appropriate calibration, the scheme was able to match the number of sprays required by TOM-CAST exactly or within one spray.
Abstract
A fungicide-spray scheduling scheme for tomatoes called TOM-CAST (tomato forecaster) was adapted for use with operational weather data in order to increase the number of users by eliminating the need for in-field measurements of hourly temperature and leaf wetness duration. Such schemes reduce cost, environmental risk, and the development of resistance to the fungicide. Duration of wetness was estimated as the length of time that the dewpoint depression (T−Td) remained between two specified limits, indicating the onset and offset of wetness. Several methods of obtaining the necessary temperature and dewpoint data were investigated. The preferred method, considering accuracy and simplicity, involved synthesis of hourly temperatures from locally observed daily maximum and minimum temperatures, and estimation of dewpoints from two Environment Canada hourly weather stations. With appropriate calibration, the scheme was able to match the number of sprays required by TOM-CAST exactly or within one spray.
Abstract
Climate change is predicted to negatively impact wheat yields across northern India, primarily as a result of increased heat stress during grain filling at the end of the growing season. One way that farmers may adapt is by sowing their wheat earlier to avoid this terminal heat stress. However, many farmers in the eastern Indo-Gangetic Plains (IGP) sow their wheat later than is optimal, likely leading to yield reductions. There is limited documentation of why farmers sow their wheat late and the potential constraints to early sowing. Our study uses data from 256 farmers in Arrah, Bihar, a region in the eastern IGP with late wheat sowing, to identify the socioeconomic, biophysical, perceptional, and management factors influencing wheat-sowing-date decisions. Despite widespread awareness of climate change, we found that farmers did not adopt strategies to adapt to warming temperatures and that wheat-sowing dates were not influenced by perceptions of climate change. Instead, we found that the most important factors influencing wheat-sowing-date decisions were irrigation type and cropping decisions during the monsoon season prior to the winter wheat growing season. Specifically, we found that using canal irrigation instead of groundwater irrigation, planting rice in the monsoon season, transplanting rice, and transplanting rice later during the monsoon season were all associated with delayed wheat sowing. These results suggest that there are system constraints to sowing wheat on time, and these factors must be addressed if farmers are to adapt wheat-sowing-date decisions in the face of warming temperatures.
Abstract
Climate change is predicted to negatively impact wheat yields across northern India, primarily as a result of increased heat stress during grain filling at the end of the growing season. One way that farmers may adapt is by sowing their wheat earlier to avoid this terminal heat stress. However, many farmers in the eastern Indo-Gangetic Plains (IGP) sow their wheat later than is optimal, likely leading to yield reductions. There is limited documentation of why farmers sow their wheat late and the potential constraints to early sowing. Our study uses data from 256 farmers in Arrah, Bihar, a region in the eastern IGP with late wheat sowing, to identify the socioeconomic, biophysical, perceptional, and management factors influencing wheat-sowing-date decisions. Despite widespread awareness of climate change, we found that farmers did not adopt strategies to adapt to warming temperatures and that wheat-sowing dates were not influenced by perceptions of climate change. Instead, we found that the most important factors influencing wheat-sowing-date decisions were irrigation type and cropping decisions during the monsoon season prior to the winter wheat growing season. Specifically, we found that using canal irrigation instead of groundwater irrigation, planting rice in the monsoon season, transplanting rice, and transplanting rice later during the monsoon season were all associated with delayed wheat sowing. These results suggest that there are system constraints to sowing wheat on time, and these factors must be addressed if farmers are to adapt wheat-sowing-date decisions in the face of warming temperatures.
Abstract
In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.
Abstract
In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.
