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The productivity of wheat is highly vulnerable to climate change. Optimizing the sowing period of a crop may be one of the most important climate resilient strategies to optimize yield. First, the CERES-Wheat model was used to analyze effects of climate change on the optimum sowing window of wheat. Second, it was used to determine the optimum sowing window for different zones within Punjab state, India. The simulation results suggested that climate change has caused a shift in the optimum sowing window of wheat. The current (2006–2015 weather data) optimum sowing window is 22–28 October in north eastern Punjab, 24–30 October in central Punjab, and 21–27 October in south western Punjab. The rate of decrease in productivity with delay in sowing from the optimum sowing window by each day was lowest for north eastern Punjab (36.09 kg ha−1 day−1) and highest for south western Punjab (70.80 kg ha−1 day−1). The methodology followed in this study can be useful in determining the optimum sowing time of various crops.

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Heat stress is a major factor affecting growth and productivity of wheat. A study was conducted at Ludhiana, Punjab, India with an objective to find out the impact of periodic (15 days) heat stress in wheat grown under natural field conditions. The heat stress treatments consisted of control (no heat stress), heat stress during 0–15, 16–30, 31–45, 46–60, 61–75, 76–90, 91–105, 106–120, 121–135, 136 days after sowing (DAS) to harvest. Heat stress (seasonal average maximum temperature higher than optimum temperature by 4.3 and 2.7°C during 2016–2017 and 2017–2018, respectively) was imposed by using mini heat tents made of galvanized iron pipes covered with transparent polyethene sheets. The mini heat tents were installed for 15 days duration in the plots according to the treatments and were removed at the end of 15th day. On the next day, they were installed in plots of the next treatment level. Heat tents were never installed on control plots. Increase in temperature in the mini heat tents was not controlled and was allowed to increase naturally. However, the excessive heating in the tents was prevented by opening the southern side during day time. Heat stress resulted in a significant increase in the membrane injury index and a decrease in chlorophyll content. Heat stress reduced the activity of dehydrogenase in rhizosphere and bulk soil. Heat stress from 76–90 and 91–105 DAS significantly reduced the ear length and number of grains ear‐1. Heat stress during 106–120 and 121–135 DAS decreased thousand grain weight significantly. Application of heat stress between 61–75, 76–90, 91–105, 106–120 and 121–135 DAS decreased grain yield significantly by 16.0, 19.8, 22.1, 16.4 and 14.8% during 2016–2017 and 11.8, 15.3, 18.8, 13.9 and 12.6% during 2017–2018, respectively, as compared to that obtained under control conditions. Apart from effects of heat stress on crops, the present study also highlights the design and possibility of using cost‐effective, small size and portable mini heat tents for conducting heat stress studies in different field crops.

A lot of research work regarding irrigation scheduling in rice has been carried out at global level with the objective of increasing irrigation water productivity (IWP) and sustaining grain yield. Under natural conditions rain disturb the planned irrigation treatments. One way to overcome this problem is to use rain shelters which is a costly affair, crop growth simulation models offer a good scope to conduct such studies by excluding the effect of rain. Very limited studies are available where FAO's AquaCrop model has been used to develop irrigation schedule for crops. Therefore, a study was conducted using FAO AquaCrop model to develop irrigation schedule for rice having higher IWP. The model was calibrated and validated using the experimental data of field experiments conducting during 2009 and 2010, respectively. The model underestimated the above ground dry biomass at 30 days after transplanting (DAT) in the range of 21.60 to 24.85 %. At the time of harvest the model overestimated the above ground dry biomass within the range 11.58 to 14.34 %. At harvest the values of normalized root mean square error (15.54%) suggested a good fit for the above ground dry biomass and an excellent agreement (3.34%) between observed and model predicted grain yield. The model suggested to irrigate rice transplanted in puddled loamy sand soil on every 5 th day to get higher IWP coupled with statistically similar grain yield as obtained with daily irrigation schedule.

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