The erosivity thing with inside the conventional soil loss equation (USLE) affords a powerful approach ofcomparing the erosive energy of rainfall. This study estimated the erosivity factorbased on monthly and yearly periodic rush rainfall data of Amritsar, Punjab State covering a period of 45 year from1971 to 2015using Lombardi system (EI = 1.03 Vd1.51). It was found that early arrived rainfall led high erosive index.The data of seasonal distribution of precipitation illustrated that the most ferocious erosion imminence inthe area can be anticipated in August, especially in the areas that aren’t defended by foliage cover, whichmight be due to climatic change. The average periodic erosivity indicator for the megacity during theperiod of study was 744.6 MJ mm/ hr. The correlation between periodic erosivity indicator andaverageperiodic rush was expressed as Y = 0.0002x + 0.8873. The R2 of 0.79 shows that rush alone contributed 79 %of the corrosion threat within the study period. The knowledge of impact of rainfall on erosivity is essentialin soil erosion threat assessment and for soil and water conservation planning. The information on effect ofprecipitation on erosivity is important in soil disintegration threat evaluation and for planning of waterand soil conservation.

Background: Nitrogenous fertilizers being very expensive, farmers can’t come up with the money for them in big and it cannot observe in wasteful way. At the identical time, crop should not suffer from the deficiency of nitrogen. Proper time of N utility is important to limit N loss and increases restoration; it’s far commonly determined that farmer tend to apply more nitrogen fertilizer than needed mainly because of its immediately seen effect on plant boom and leaf color. Methods: Field experiment was conducted to study the effect of methods of sowing and nutrient management on productivity of maize during season of 2018 and 2019 on sandy loam soil at Amritsar. The soil was neutral (pH- 7.6) and it was low in Nitrogen (154 kg N/ha) and medium in phosphorus (24 kg P2O5/ha) and high in potassium (328 kg K2O/ha). The experiment consisted three methods of sowing i.e. Zero, Flat and Ridge sowing of maize and five levels of fertilizer i.e., No = control, Nrec = recommendation (125:60:30), NLCC4 (90:60:30), NLCC5 (120:60:30) and NLCC6 (150:60:30) with split N application on basis of LCC, thus making fifteen treatment combinations, which were replicated thrice and was laid out in Split pot design (SPD). Result: Higher grain yield (39.43 q ha-1) was obtained from ridge planting followed by (36.27 q ha-1) in planting method Zero tillage.The result indicated that different schedules of fertilizer expressed significant effect on maize grain yield and quality. It was found that the application of split N on the basis of LCC gave highest yield. The application of nutrient nitrogen used more efficiently by the NLCC4 in maize it may be due to less losses of nitrogen by application through leaf color chart shade 4. The gross return, net return and Benefit: cost ratio indicated that the application of nutrients on basis of LCC5 proved economically more remunerative.

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Within season variability in temperature is a major bottleneck in wheat productivity. This simulation study aimed to evaluate the effects of temperature variability on grain yield of two cultivars of wheat (cv PBW 621 and HD 3086) sown under different dates (early, mid and late) using two dynamic crop simulation models (CERES-Wheat and INFOCROP model) for two locations (Amritsar and Ludhiana). The temperature was increased and decreased by 1.0 to 2.0oC for Amritsar and 1.0 to 3.0oC for Ludhiana from normal during three growing periods, i.e., the whole season, vegetative phase, and reproductive phase. In Amritsar the CERES-Wheat and INFOCROP model predicted that with the increase in temperature by 1.0 to 2.0oC from normal during the vegetative phase, the grain yield may decrease by 0.36-15.23 % and 3.61-19.54 % respectively, during the reproductive phase the grain yield may decrease by 0.67–8.64 % and 3.18-26.76 % respectively and during the whole season the grain yield may decrease by 1.52-27.10 % and 1.91-24.10 % respectively. Among the two cultivars of wheat, cv HD 3086 at both locations performed better under thermal stress environments as compared to cvPBW 621. However, the InfoCrop model predicted that cv PBW 621 performed well in comparison to cv HD 3086 at Ludhiana conditions with an increase in temperature up to 3°C. The simulation results showed that mid November sowing of wheat was better able to counteract the negative impacts of an increase in temperature on wheat as compared to early (October) or late (December) sowing dates.