Adapting cropping systems to future climate change scenario in three agro-climatic zones of Punjab, India

The present study focuses on (1) projections of future climate data (for the years of 2020, 2050 and 2080) from three general circulation models (HadCM3, CCCMA-CGCM2 and CSIRO-MK2) for two scenarios (A2 and B2) for three agro-climatic zones of the Indian Punjab (ii) assessment of climate change impact on productivity of maize-wheat cropping system in moist to dry sub-humid, rice-wheat in hot dry semiarid and cotton-wheat in hot arid zones and (iii) evaluation of shifting planting dates as an adaptation measure to sustain crop yields. The results indicate that in future the magnitude of climate change and variability would vary with agro-climatic zone, model and scenario. Maximum temperature, minimum temperature and rainfall would be higher in moist to dry sub-humid zone than hot arid. Simulations with cropping system model anticipated reduction in yields of all the three cropping systems for future years; however, cotton crop was more vulnerable than maize and rice. Delaying trans/planting of maize by 7 days in sub humid zone, rice by 7-15 days in semi arid and cotton by 21 days in arid zone in future emerged as doable adaptation measure to minimize yield reduction in future

Impact of temperature variability and management interventions on productivity of wheat

Field experiments for six seasons (2008–09 to 2013–2014) and simulations for mid–century (2021-2050) were carried out to (i) understand impact of inter– and intra– seasonal temperature variability on wheat yield, and (ii) identify best management intervention in relation to temperature variability. In the field experiments, treatments were three dates of planting, two inbred varieties and two irrigation schedules replicated thrice. Simulation with CERES–Wheat model pointed that variability of 5.5 percent in Tmax and 3.8 percent in Tmin would cause 11.2 percent variability in yield. The variation in yield would also vary with date of planting. It was relatively less in Nov 05 sown wheat than other dates,showing that in mid–century yield can be sustained by planting wheat at this date. However, at present growing of longer duration varieties in last week of October with adequate irrigation, medium to longer duration in 1st week of November is the practical adaptive measure to minimize impact of temperature variability on wheat yield

Evaluation of statistical corrective methods to minimize bias at different time scales in a regional climate model driven data

The regional climate models provide sufficient information of the climate data, which can be used for simulating the impact of expected climate change on crop growth and hydrological processes. But future climate data derived from such models often suffers from bias and is not ready to use per se in crop growth/hydrological models, wherein reasonable and consistent meteorological daily input data is a crucial factor. The present study concerns the assessment and minimization of the bias in the PRECIS modeled data of maximum and minimum temperatures and rainfall for Ludhiana station, representing central Punjab of India. The correction functions for three corrective methods i.e. difference, modified difference and statistical bias correction at daily, monthly and annual time scales were developed and validated to minimize the bias. Amongst these, correction functions derived using modified difference method at daily time scale for rainfall and at monthly time scale for Tmax and Tmin were found to be the superseding

Evaluation of climgen model to generate weather parameters under different climatic situations in Punjab

In the present study, ClimGen (weather generator) generated data was compared to the observed  weather data of Ballowal, Ludhiana and Bathinda weather stations representing different type of climatic situations in Punjab. Several years of daily data of solar radiation, maximum and minimum temperature, morning and evening relative humidity, rainfall and wind speed were used as input and five years data were used for validation purpose. Evaluation was done on the basis of coefficient of determination (R2), Residual Mean Square Error (RMSE), General Standard Deviation (GSD) and Wilmott’s index (d) of agreement between generated and observed data. The ClimGen generated data for maximum and minimum temperature showed good performance (GSD d” 0.10 and d e” 0.95) and the data generated for morning relative humidity was acceptable (GSD > 0.10 but d” 0.20 and d < 0.95 but e” 0.90) while evening relative humidity and wind speed were poor except for Ludhiana station. However, the generated rainfall data was poor for all the stations and hence, cannot be accepted. Overall, results indicated ClimGen a good performer as a weather generator for certain parameters