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Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola

Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola was established on 20th October, 1969 with its head-quarter at Akola. This Agricultural University was named after the illustrious son of Vidarbha Dr. Panjabrao (alias Bhausaheb) Deshmukh, who was the Minister for Agriculture,Govt. of India. The jurisdiction of this university is spread over the eleven districts of Vidarbha. According to the University Act 1983 (of the Government of Maharashtra), the University is entrusted with the responsibility of agricultural education, research and extension education alongwith breeder and foundation seed programme. The University has its main campus at Akola. The instructional programmes at main campus are spread over in 5 Colleges namely, College of Agriculture, College of Agricultural Engineering & Technology, College of Forestry, College of Horticulture and Post Graduate Institute. At this campus 4 degree programmes namely B.Sc.(Agri.) B.Sc. (Hort.), B.Sc. (Forestry) and B.Tech. (Ag. Engg.) , two Master’s Degree Programmes viz. M.Sc.(Agri.) and M.Tech. (Agri.Engg.) and Doctoral Degree Programmes in the faculties of Agriculture and Agril. Engineering are offered. The University has its sub-campus at Nagpur with constituent College, College of Agriculture which offers B.Sc.(Agri.) and M.Sc.(Agri.) degree programmes. The Nagpur Campus is accomplished with a garden, surrounded by its natural beauty and a well established Zoo which attract the general public and visitors to the city. A separate botanic Garden is being maintained on 22 hectares with a green house for the benefit of research workers. In addition there are 2 affiliated grant-in-aid colleges and 14 private non-grant-in-aid colleges under the umbrella of this University A Central Research Station is situated at the main Campus which caters to the need of research projects undertaken by Crop Scientists of the principle crops of the region are Cotton, Sorghum, Oilseeds and Pulses.

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Author: Authors: DOFE, SHASHWATI GANESHRAO. × End date: 2019 × Start date: 2019 × Thesis Type: M.Sc ×
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    ThesisItemOpen Access
    Title: SIMULATION OF INFOCROP WHEAT MODEL UNDER DIFFERENT SOWING WINDOWS AT AKOLA.
    (Publisher : Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra., 2019-08-21) Authors: DOFE, SHASHWATI GANESHRAO.; Advisor: Tupe, Dr. A. R.
    Abstract: An experiment entitled “Simulation of InfoCrop wheat model under different sowing windows at Akola” was carried out in rabi season 2017-2018 at the farm of Wheat Research Unit, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola (M.S.). The treatments consisted of three sowing dates (46thMW, 48thMW and 51stMW) and five wheat genotypes (AKAW-4627, AKAW-3722, AKW-1071, AKAW-3997, and AKAW-4210-6). The experiment was laid out in a factorial randomized block design with three replications. The soil was clayey with pH 7.64 containing 206.08, 15.32, 339.45 kg ha-1 available N, P, K respectively. The growth and yield attributes were by significant increased, when wheat crop was sown at D1 (46thMW) than D2 (48thMW) and D3 (51stMW). Grain yield obtained was significantly higher at D1 (46thMW) sowing. Out of five genotypes, (AKAW-4210-6) was superior over numbers of functional leaves plant-1, number of tillers plant-1, dry matter accumulation plant-1, spikelet plant-1, length of earhead, weight of earhead, number of earhead plant-1, number of spikelets earhead-1, number of grain earhead-1,weight of grains earhead-1, test weight(g), grain yield (kg ha-1), straw yield (kg ha-1), biological yield, harvest index. Genotype AKAW-4210-6 was proved better in obtaining highest B:C ratio. Wheat crop sown at 51stMW required significantly lower growing degree days (GDD) and heliothermal units for completion of reproductive phase than 46thMW and 48thMW sown crop. Growing degree days and heliothermal units were observed maximum with genotype AKAW-3997 over the other genotypes at all stages of crop growth. The highest thermal use efficiency with grain yield, straw yield and biological yield was observed in AKAW-4210-6, while lowest thermal use efficiency was recorded in genotype AKAW-3997. The results revealed that simulation of growth and yield parameters were compared with observed data and results indicated that the model underestimated all the growth parameters within the acceptable range (<15%) with significant accuracy. The model simulated the leaf area against observed value at different dates of sowing (0.81/0.89, 0.80/0.85, 0.80/0.81) and with genotypes (0.80/0.89, 0.78/0.83, 0.78/0.84, 0.79/0.81, 0.87/0.90), which was underestimated the per cent error below 10. The biomass yield and straw yield was slightly overestimated in all treatments and grain yields were well matched with different dates of sowing i.e. D1, D2, D3 and genotypes i.e. G1, G2, G3, G4, and G5. The grain yield was well simulated at all treatments. The predicted environmental factors play important role in growth of wheat, which were well matched with observed data. However the bright sunshine hours simulated by the model were more than the observed values. Thus, model predicted most of weather parameters correctly as compared to observed value of weather parameters.
  • Thumbnail Image
    ThesisItemOpen Access
    Title: SIMULATION OF INFOCROP WHEAT MODEL UNDER DIFFERENT SOWING WINDOWS AT AKOLA.
    (Publisher : Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola, Maharashtra., 2019-08-21) Authors: DOFE, SHASHWATI GANESHRAO.; Advisor: Tupe, Dr. A. R.
    Abstract: An experiment entitled “Simulation of InfoCrop wheat model under different sowing windows at Akola” was carried out in rabi season 2017-2018 at the farm of Wheat Research Unit, Dr. Panjabrao Deshmukh Krishi Vidyapeeth, Akola (M.S.). The treatments consisted of three sowing dates (46thMW, 48thMW and 51stMW) and five wheat genotypes (AKAW-4627, AKAW-3722, AKW-1071, AKAW-3997, and AKAW-4210-6). The experiment was laid out in a factorial randomized block design with three replications. The soil was clayey with pH 7.64 containing 206.08, 15.32, 339.45 kg ha-1 available N, P, K respectively. The growth and yield attributes were by significant increased, when wheat crop was sown at D1 (46thMW) than D2 (48thMW) and D3 (51stMW). Grain yield obtained was significantly higher at D1 (46thMW) sowing. Out of five genotypes, (AKAW-4210-6) was superior over numbers of functional leaves plant-1, number of tillers plant-1, dry matter accumulation plant-1, spikelet plant-1, length of earhead, weight of earhead, number of earhead plant-1, number of spikelets earhead-1, number of grain earhead-1,weight of grains earhead-1, test weight(g), grain yield (kg ha-1), straw yield (kg ha-1), biological yield, harvest index. Genotype AKAW-4210-6 was proved better in obtaining highest B:C ratio. Wheat crop sown at 51stMW required significantly lower growing degree days (GDD) and heliothermal units for completion of reproductive phase than 46thMW and 48thMW sown crop. Growing degree days and heliothermal units were observed maximum with genotype AKAW-3997 over the other genotypes at all stages of crop growth. The highest thermal use efficiency with grain yield, straw yield and biological yield was observed in AKAW-4210-6, while lowest thermal use efficiency was recorded in genotype AKAW-3997. The results revealed that simulation of growth and yield parameters were compared with observed data and results indicated that the model underestimated all the growth parameters within the acceptable range (<15%) with significant accuracy. The model simulated the leaf area against observed value at different dates of sowing (0.81/0.89, 0.80/0.85, 0.80/0.81) and with genotypes (0.80/0.89, 0.78/0.83, 0.78/0.84, 0.79/0.81, 0.87/0.90), which was underestimated the per cent error below 10. The biomass yield and straw yield was slightly overestimated in all treatments and grain yields were well matched with different dates of sowing i.e. D1, D2, D3 and genotypes i.e. G1, G2, G3, G4, and G5. The grain yield was well simulated at all treatments. The predicted environmental factors play important role in growth of wheat, which were well matched with observed data. However the bright sunshine hours simulated by the model were more than the observed values. Thus, model predicted most of weather parameters correctly as compared to observed value of weather parameters.