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20202
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Subject: Agricultural Meteorology × End date: 2020 × Start date: 2020 × Type: Thesis × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Calibration of CERES-Maize model and study of thermal requirements of Sweet corn under Tarai region of Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-12) Rawat, Sushmita; Singh, R.K.
    The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B Pant University of Agriculture and Technology, Pantnagar during kharif 2019 to analyze the effect of different sowing dates and nitrogen levels on the growth and development of Sweet corn as well as to study the thermal requirements of the crop in Tarai region of Uttarakhand. The experiment with two factors; sowing dates (8th August, 23rd August and 7th September) and nitrogen levels (150 kg N ha-1, 120 kg N ha-1, 90 kg N ha-1 and 60 kg N ha-1) was laid out in Split Plot Design with three replications keeping the sowing dates in main plot and nitrogen levels in sub-plot. The variety taken for the experiment was Sweet corn hybrid Sweet 77. All recommended cultural practices were followed during the crop growth period. The various growth parameters under observations were periodically recorded to evaluate the treatment effect on the growth and yield of crop. The heat unit requirement study shows that the various thermal indices i.e., GDD, HTU, PTU and HUE reduced with delay in planting dates. Higher values of thermal units were positively correlated with good crop growth parameters and higher grain production. Experimental analysis suggests that the crop sown on 8th August with 120 kg N ha-1 performed better among all the treatments. The growth parameters such as crop height, leaf area index and dry matter accumulation were at par in N1 (150 kg N ha-1) and N2 (120 kg N ha-1) treatments. The yield contributing parameters and grain yield were found to be reducing with delay in sowing date and lowering nitrogen doses. The CERES-Maize model was satisfactorily calibrated against the emergence (DAS), anthesis (DAS) and dry matter grain yield. The simulated dry matter grain yield was comparable with the actual observation with R2 of 0.96 and % RMSE of 9.14. Maize crop is grown throughout the country according to the climatic suitability. Weather variables play an important role in deciding the crop growth and productivity. The sensitivity analysis of the CERES-Maize model was performed to see the effect of the changing weather variables i.e., mean air temperature (°C), CO2 concentration (ppm) and solar radiation (MJ m-2 day-1) on the dry matter grain yield production of the crop. The results showed that the crop was highly sensitive to the changes in mean air temperature and solar radiation and responded differently under different growing environment. The simulated grain yield increased with increase in CO2 concentration and vice-versa.
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    ThesisItemOpen Access
    Studying the impact of modified microclimate on growth & development of chickpea (Cicer arietinum L.) in tarai region of Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-09) Satpathi, Anurag; Nain, A.S.
    Chickpea, an important rabi legume crop of India mostly cultivated in Madhya Pradesh region of the country. A field experiment was conducted in rabi season of 2018-19 at N.E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar with three dates of sowing and four modified microclimatic conditions (Degree of perforation on covered plastic material). The experiment was laid out in strip plot design. The experiment with three dates of sowing: D1-12 December 2018, D2-22 December 2018 and D3-02 January 2019 as main plot treatments and the four microclimatic regimes: T1-Open field, T2-Open roof, T3-Perforated roof and T4-Closed or packed typeas sub plot treatments was laid to analyse the impact of microclimatic modification on growth and development of chickpea. The experimental data-set of the field was also used to calibrate and validate the CROPGRO (DSSAT v4.7) model for chickpea (Cicer arietinum L.) for variety Pusa-362 under Tarai region. The major finding of the study is that sowing on 12 Dec (D1) resulted into highest biological as well as seed yield i.e. 1200 kg ha-1followed by 22 Dec (D2)i.e. 845 kg ha-1and 02 Jan (D3)i.e. 638 kg ha-1. This may be mainly attributed to congenial weather conditions i.e. Tmin ranged from 2.7 to 17.8°C while Tmax ranged from 22.2 to 35.7°C as compared to the weather that prevailed during the sowing on 22 Dec2018 and 02 Jan 2019.By studying the role of weather variables on chickpea in terms of seed yield, it is noticed that best performance of Packed subset (T4)i.e. 1044 kg ha-1was observed in all dates of sowing followed by Perforated (T3)i.e. 955 kg ha-1, Open roof (T2)i.e. 813 kg ha-1 and Open field (T1)i.e.765 kg ha-1. However, crops under packed and perforated conditions experienced high temperature stress during flowering and maturity stage but crop experienced congenial temperature range during germination and vegetative stage, which leads to higher dry matter production and ultimately higher seed yield. Tmin ranged from 8.7 to 23.5°C and 5.9 to 21.5°C for packed (T4) and perforated (T3) conditions respectively. Similarly, Tmax ranged from 25.5 to 40.4°C and 23.4 to 38.9°C for packed (T4) and perforated (T3), respectively. The overall performance of the model based on the test criterion to evaluate the CROPGROChickpea model for phenology and yield attributes of three dates of sowing 12 Dec (D1), 22 Dec (D2) and 02 Jan (D3) and four microclimatic conditions open field (T1), open roof (T2), perforated (T3) and closed (T4) type clearly indicated that simulation for seed yield was better with reasonable error (Seed yield RMSE 5.93%).The decrease in seed yield with delayed sowing as observed in experiment was well simulated by the model. Under different microclimatic conditions, for the packed condition (T4), the model simulated higher simulated seed yield as compared to observed yield of experiment. The model output showed that the simulated values of phenology, growth parameters and yield of chickpea were quite close to the corresponding observed values. Hence, the model can be used to predict the yield accurately at different scales.