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ThesisItem Restricted Formulation of crop weather calendar for sunflower (Helianthus annuus L.) under Punjab conditions(Punjab Agricultural University, Ludhiana, 2019) Jaspal Singh; Sidhu, Prabhjyot KaurThe field experiment was conducted during rabi 2018 at the Research farm, Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana to generate phenological field data for formulation of crop weather calendar under Punjab conditions. The sunflower was sown with four thermal environments; early sown (T1: 5th January), normal sown (T2: 15th January and T3: 25th January) and late sown (T4: 4th February) using two cultivars (PSH 996 and PSH 1962). The experiment was laid out in split plot design with four replications, keeping thermal environments in main plot and two cultivars in sub plot. The sunflower crop sown during 5th January took more days to complete their life cycle as compared to that sown during 15th January followed by 25th January and 4th February. Among the sowing dates, LAI, dry matter accumulation, PAR interception, radiation use efficiency (RUE) and plant height were higher and extinction coefficient was lower in early sown crop as compared to late sown crop which may be due to profuse vegetative growth. Among the yield attributes characters, early sown (T1: 5th January) and normal sown (T2: 15th January) sunflower crop produced higher number of seed / head, seed yield, stalk yield, biomass yield, but 1000 seed weight. Then for formulation of crop weather calendar the weekly and monthly normal of different historical metrological parameters was computed from daily weather data for Ludhiana, Ballowal Saunkhri and Amritsar, rabi 2003-2017. These climatic normals were used for comparing the actual data to study the effect of different meteorological parameters on yield of sunflower crop. The analysis of meteorological parameters at different locations revealed that temperature above normal in the months of March for continuous period is not favourable for sunflower productions. Heavy rainfall in the months of April and May at grain filling stage with below normal sunshine hour have directly reduced the sunflower productivity. The actual meteorological data of high yield crop years over the past 15 years were analysis for different growth stages of sunflower to work out the critical ranges of meteorological parameters. Crop weather calendar were formulated for sunflower crop by compiling the weekly climatic normals and critical limits of meteorological parameters for different growth stages and these can be used for agro-advisory service and for prediction of potential crop yield. Further weather based “Thumb Rule models” using the weekly meteorological data were formulated for predicting the yield of sunflower at 3 locations in Punjab state.ThesisItem Restricted Effects of microclimatic modifications on phenological development and seed cotton yield using CROPGRO-cotton model(Punjab Agricultural University, Ludhiana, 2019) Dhir, Abhishek; Raj Kumar PalThe Present study entitled “Effects of microclimatic modifications on phenological development and seed cotton yield using CROPGRO-cotton model” was carried out at two Locations viz., Punjab Agricultural University (PAU) Regional Research Station, Bathinda, (latitude 30°58‟N, 74°18‟E longitude and altitude 211m above mean sea level) and Faridkot (latitude 30°40‟ N, longitude 74°44‟ E and altitude 200m above mean sea level) during the kharif 2018. The soil of the both the experimental sites is sandy loam. The experiments were laid out with Bt cotton hybrid RCH 773 BGII and sown at three dates i.e. April 30, May 15 and May 30 with two row orientations (North-South: N-S and East-West: E-W) and three plant spacings (67.5cm×45.0cm, 67.5cm×60.0 cm and 67.5cm×75.0cm) in factorial split-plot design with three replications. The study indicated that the days taken to achieve various phenophases like emergence, first square, anthesis, boll opening and maturity (DAS) were found to be decreased with delayed sowing and recorded more days to attain various phenophases with 30th April sown crop. Delay in sowing from April 30 to May 15 reduced the cotton yield by 6.0% at Bathinda and by 13.1% at Faridkot and further delayed sowing from May 15 to May 30 reduced the seed cotton yield by 17.4% and 8.7% at Bathinda and Faridkot, respectively. Moreover, delay in sowing time of one month from April 30 to May 30, caused reduction in seed cotton yield by 22.36% and 20.37% at Bathinda and Faridkot, respectively. Besides, crop sown in East–West row orientation reported highest seed cotton yield than North–South direction at both the study regions. Similarly, among plant spacings, wider plant spacing of 67.5cm×75.0cm produced more seed cotton yield due to proper aeration and light distribution within the canopy. Among weather parameters, higher variation was observed between sowing dates, while lesser variation was recorded among row orientations and plant spacings. Furthermore, the model output in terms of seed cotton yield was found in good agreement over observed having higher value of R2 (0.75 for Bathinda and 0.83 for Faridkot) and lesser RMSE (253.8 kg ha-1 for Bathinda and 198.3 kg ha-1 for Faridkot). Similarly, simulated phenology of the crop was also shown close proximity over observed value having R2 value of 0.49, 0.51 and 0.61 at Bathinda and 0.59, 0.43 and 0.87 at Faridkot for emergence, anthesis and maturity. Hence, the CROPGRO-cotton model can be used as research tool for the prediction of cotton phenology and yield of the crop.ThesisItem Restricted Role of meteorological parameters in incidence of rice brown planthopper(Punjab Agricultural University, Ludhiana, 2019) Shubham Anand; Dhaliwal, L. K.A field experiment was conducted at the Research Farm, Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana during kharif 2018. Two rice varieties (PR 121 and PR 126) were transplanted on 20th June and 30th June. Variety Pusa Basmati 1121 was transplanted on 5th July and 15th July under three spacings (25 cm x 12 cm, 20 cm x 15 cm and 30 cm x 10 cm). The experiment was laid out in Split-Split Plot Design for rice crop and Factorial-RCBD Design for basmati rice in four replications. The micrometeorological data on photosynthetically active radiation (PAR) and relative humidity were recorded at different phenological stages. The periodic biometric observations on leaf area index were recorded. At harvesting, the yield and yield contributing characters (number of effective tillers, number of grains/panicle, 1000-grain weight, grain yield etc.) were recorded. The results indicate that PAR interception was higher under wider spacing (30 cm x 10 cm) followed by closer spacings (20 cm x 15 cm and 25 cm x 12 cm). The higher relative humidity was recorded in closer spacing (25 cm x 12 cm) than wider spacing (20 cm x 15 cm and 30 cm x 10 cm). Leaf area index was higher in 30 cm x 10 cm spacing followed by 20 cm x 15 cm and 25 cm x 12 cm spacing. The higher grain yield (58.65 q/ha) was recorded in rice varieties transplanted under wider spacing (30 cm x 10 cm) in comparison to closer spacings of 20 cm x 15 cm (55.70 q/ha) and 25 cm x 12 cm (53.40 q/ha). In Pusa Basmati 1121, grain yield was higher (30.57 q/ha) in 30 cm x 10 cm spacing followed by 20 cm x 15 cm (29.25 q/ha) and 25 cm x 12 cm (26.89 q/ha) spacings. The peak incidence of brown planthopper incidence was observed in 25 cm x 12 cm spacing (3.1 brown planthopper population/hill) as compared to 20 cm x 15 cm spacing (2.9 brown planthopper population/hill) and 30 cm x 10 cm (2.4 brown planthopper population/hill).The brown planthopper incidence data from 2014 to 2016 and 2018 indicated that the highest incidence at Ludhiana was observed in 2016 and the lowest in 2018. Correlation and regression analysis were carried out between different meteorological parameters and brown planthopper incidence. Using brown planthopper data (in field) of different years stepwise regression models were developed. Minimum temperature and relative humidity (morning and evening) were found to be important parameters in incidence of brown planthopper. Agroclimatic indices viz. growing degree days (GDD) and humid-thermal ratio (HTR) were calculated for brown planthopper. Agroclimatic indices based model was developed using brown planthopper incidence data from 2014 to 2016. RHe/Tmax and RHe/Tmin based model gave lowest error (%) when validated with 2018 brown planthopper data. According to present study, the favourable temperature for brown planthopper incidence was 30.2-33.4 oC (maximum temperature) and 20.6-22.5 oC (minimum temperature) while relative humidity was 86-90 per cent in the morning and 48-60 per cent in the evening. Hot, cloudy and humid conditions are conducive for brown planthopper multiplication. The crop-weather-brown planthopper-calendar was prepared on the basis of four-year brown planthopper data. This calendar can be used in agro-advisory for giving forewarning of brown planthopper to the farmers.ThesisItem Open Access Role of meteorological parameters in incidence of rice brown planthopper(Punjab Agricultural University, Ludhiana, 2019) Anand, Shubham; Dhaliwal, L. K.A field experiment was conducted at the Research Farm, Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana during kharif 2018. Two rice varieties (PR 121 and PR 126) were transplanted on 20th June and 30th June. Variety Pusa Basmati 1121 was transplanted on 5th July and 15th July under three spacings (25 cm x 12 cm, 20 cm x 15 cm and 30 cm x 10 cm). The experiment was laid out in Split-Split Plot Design for rice crop and Factorial-RCBD Design for basmati rice in four replications. The micrometeorological data on photosynthetically active radiation (PAR) and relative humidity were recorded at different phenological stages. The periodic biometric observations on leaf area index were recorded. At harvesting, the yield and yield contributing characters (number of effective tillers, number of grains/panicle, 1000-grain weight, grain yield etc.) were recorded. The results indicate that PAR interception was higher under wider spacing (30 cm x 10 cm) followed by closer spacings (20 cm x 15 cm and 25 cm x 12 cm). The higher relative humidity was recorded in closer spacing (25 cm x 12 cm) than wider spacing (20 cm x 15 cm and 30 cm x 10 cm). Leaf area index was higher in 30 cm x 10 cm spacing followed by 20 cm x 15 cm and 25 cm x 12 cm spacing. The higher grain yield (58.65 q/ha) was recorded in rice varieties transplanted under wider spacing (30 cm x 10 cm) in comparison to closer spacings of 20 cm x 15 cm (55.70 q/ha) and 25 cm x 12 cm (53.40 q/ha). In Pusa Basmati 1121, grain yield was higher (30.57 q/ha) in 30 cm x 10 cm spacing followed by 20 cm x 15 cm (29.25 q/ha) and 25 cm x 12 cm (26.89 q/ha) spacings. The peak incidence of brown planthopper incidence was observed in 25 cm x 12 cm spacing (3.1 brown planthopper population/hill) as compared to 20 cm x 15 cm spacing (2.9 brown planthopper population/hill) and 30 cm x 10 cm (2.4 brown planthopper population/hill).The brown planthopper incidence data from 2014 to 2016 and 2018 indicated that the highest incidence at Ludhiana was observed in 2016 and the lowest in 2018. Correlation and regression analysis were carried out between different meteorological parameters and brown planthopper incidence. Using brown planthopper data (in field) of different years stepwise regression models were developed. Minimum temperature and relative humidity (morning and evening) were found to be important parameters in incidence of brown planthopper. Agroclimatic indices viz. growing degree days (GDD) and humid-thermal ratio (HTR) were calculated for brown planthopper. Agroclimatic indices based model was developed using brown planthopper incidence data from 2014 to 2016. RHe/Tmax and RHe/Tmin based model gave lowest error (%) when validated with 2018 brown planthopper data. According to present study, the favourable temperature for brown planthopper incidence was 30.2-33.4 oC (maximum temperature) and 20.6-22.5 oC (minimum temperature) while relative humidity was 86-90 per cent in the morning and 48-60 per cent in the evening. Hot, cloudy and humid conditions are conducive for brown planthopper multiplication. The crop-weather-brown planthopper-calendar was prepared on the basis of four-year brown planthopper data. This calendar can be used in agro-advisory for giving forewarning of brown planthopper to the farmers.ThesisItem Restricted Prediction of Maize Yield using Artificial Neural Network under Punjab Conditions(Punjab Agricultural University, Ludhiana, 2019) Vikrant Jeet Singh; Biswas, BarunThe study entitled "Prediction of maize yield using artificial neural network under Punjab conditions" was primarily focused on estimating maize yield utilizing effect of climatic conditions on maize yield over 1990-2017 period in Punjab. The two major kharif maize growing regions of Punjab selected for the current study were north Punjab comprising Gurdaspur, Amritsar, Hoshiarpur districts and central Punjab i.e. Ludhiana, Jalandhar and Kapurthala districts. As per the study objectives, the required meteorological data were acquired from available meteorological observatories established at different districts. The high-resolution gridded data was acquired from different sources and bias corrected to fill the missing observational data. The results revealed an increasing trend in maximum temperature over the period of 1990-2017 during kharif maize growing period in both the regions of Punjab except Gurdaspur. The minimum temperature during crop season registered a decreasing trend in all the districts except Ludhiana and Kapurthala. The rate of increase in maximum temperature was observed in the magnitude of 0.9°C – 3.2°C/100 year in Punjab. The seasonal total rainfall showed a positive trend in both the region except Ludhiana district. The maize yield over the years was predicted by developing multiple linear regression (MLR) and artificial neural networks (ANN). The performance of models was compared to select best fit model for maize yield prediction. In this study the R programing packages viz. ‘MASS’, ‘tidyr’ and ‘neuralnet’ were used for developing and testing above models. The ANN model with two hidden layers (ANN5.3) exhibited best result with 42.8, 40.3, 0.9, 0.99 and 0.991 for RMSE, MAE, MB, D-index and coefficient of determination (R2) for north Punjab and 64.3, 53.2, 0.9, 0.99 and 0.978 for RMSE, MAE, MB, D-index and coefficient of determination (R2) for central Punjab, respectively. This study can help in studying future climate effect on kharif maize and changing crop practices accordingly for sustainable grain yield.ThesisItem Open Access Studying the impact of climate and technology variables on maize productivity in Punjab using GIS(Punjab Agricultural University, Ludhiana, 2019) Gill, Gurpreet Kaur; Som Pal SinghClimate change has resulted in the variation of temperature and precipitation, affecting the crop production and productivity. A study therefore has been planned to examine the impact of climate and technology variables on maize productivity in Punjab using GIS. Long-term data (1971-2017) on maize yield, fertilizer consumption and maize gross irrigated area was collected from statistical abstracts of Punjab and long-term climatic data on maximum temperature, minimum temperature and rainfall during the maize growing period was collected from Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana, different research stations of PAU, India Meteorological Department, New Delhi and different websites. The maize yield increased in all the districts and the rate of increase was highest at Patiala (74.3 kg year-1). Significant increase has been observed in minimum temperature during maize growing period in all the maize growing districts, whereas no significant trend has been observed in maximum temperature and rainfall, although large year to year variations have been observed. Significant increase in fertilizer consumption in all the districts of the state was observed except Amritsar, where no trend was observed. Significant decrease has been observed in maize gross irrigated area in all the maize growing districts except Hoshiarpur and Roopnagar where the trend increased. The Mann-Kendall test results revealed that long term (1971-2017) seasonal variability showed the increase in minimum temperature in Gurdaspur, Hoshiarpur, Kapurthala and Roopnagar @ 0.04°C year-1 and increase in maximum temperature in Hoshiarpur, Kapurthala, Patiala and Roopnagar @0.03°C year-1. Temporal seasonal variability showed the decrease in maximum temperature in Jalandhar @ -0.14°C year-1 and increased in Patiala @ 0.10°C year-1 during 2001-2010. Increase in minimum temperature in Gurdaspur @ 0.37°C year-1 was observed during 2001-2010. Stepwise regression showed that 95.3 per cent of the variance in maize yield was explained by minimum temperature and 2.8 per cent by fertilizers. Inverse distance weighted method in Arc GIS 10.4 was used to show the spatial variability in maize productivity, technology variables and weather parameters during its growing period.ThesisItem Restricted Spatio-temporal variability in evapotranspiration and moisture availability in different agroclimatic regions of Punjab(Punjab Agricultural University, Ludhiana, 2019) Aatralarasi S; Kingra, P. K.Water scarcity is faced by the state of Punjab due to depletion of groundwater resources. This situation urges more detailed study, in terms of region-wise water availability over long-term period. A number of studies have been conducted in the past examining the supply side of hydrological cycle i.e precipitation. But the current study deals with demand side i.e reference evapotranspiration (ETO), a parameter directly relating climate change in terms of water requirements. In addition to this, two other parameters viz. Aridity Index (AI) and Moisture Index (MI) were also studied. Computation of ETO was done by HS method, MI by Krishnan and Singh method and AI by UNESCO method. Trend analysis was studied with Mann- Kendall test and classical statistical procedures. Spatial interpolation was done in Arc GIS 10.4 software. The results concluded that annual ETO increased in north-east region and south-west @ 1.46 mm year-1 and 0.97 mm year-1 respectively. Rabi season ETO increased in north-east region @ 1.27 mm year-1. Annual MI was observed to be dry during first decade (1971-80), there was increase in water availability during the next two decades and again dry from 2000 onwards. Annual and seasonal AI values were observed to be highest in north-east followed by Central and lowest in south-west. A critical analysis of the study indicated decreased ETO till 2000 contributed to higher AI & MI values denoting more moisture availability till 2000 and decreased thereafter annually as well as seasonally due to higher ETO. The results also indicated spatial variability in moisture availability in the state from north-east to south-west. There was prominent difference in moisture ranges during earlier decades, but this difference was reduced in recent decades which might be due to huge impact of climate change in the north-east region. To conclude, south-west region faced severe water crisis even from earlier decades, but under recent conditions of climate change north-east region is also coming under the stress situation. This alarms the need for judicious water management in all regions of state.ThesisItem Restricted Impact of El Nino and La Nina events on climatic conditions and rice-wheat productivity in Punjab(Punjab Agricultural University, Ludhiana, 2019) Chand, Shivani; Dhaliwal, L.K.Agriculture in India is mostly rainfed and is greatly influenced by Indian Summer Monsoon Rainfall (ISMR). It contributes to about 75 per cent of the total annual rainfall. El Nino, a phenomenon during which unusually warm water appears in the eastern Pacific Ocean has inverse relation with Indian summer monsoon rainfall. The historical data of different meteorological parameters (maximum temperature, minimum temperature, relative humidity, sunshine hours and rainfall) for different locations of Punjab viz. Amritsar (1971-2017), Ballowal Saunkhri (1984-2018), Bathinda (2001-2018), Ludhiana (1971-2018) and Patiala (1971-2018) were collected from the Department of Climate Change and Agricultural Meteorology, PAU, Ludhiana and Met Centre, India Meteorological Department. The rice-wheat productivity data were collected from indiastat.com and Statistical Abstract of Punjab. The data related to groundwater table (1998-2017) were collected from the Central Ground Water Board, Punjab. The El Nino/ La Nina events data were retrieved from Golden Gate Weather Services.The decade wise monthly analysis of maximum temperature showed increasing trend at Ballowal Saunkhri, Ludhiana and Patiala during all months except January, June and September whereas at Amritsar and Bathinda decreasing trend was observed in most of the months. The minimum temperature was found to be increasing at all the locations. The mean relative humidity showed increasing trend at Amritsar, Ballowal Saunkhri, Bathinda and Patiala whereas at Ludhiana few months showed negative mean relative humidity. The increasing trend in rainfall was observed at Amritsar and Ludhiana during all months except May, July, November and December whereas Ballowal Saunkhri and Patiala showed decreasing trend in most of the months. Inter season rainfall variability during kharif season mostly occurred in El Nino years at all locations except at Ballowal Saunkhri. The heavy rainfall events occurred more frequently compared to very heavy and extremely heavy rainfall events at different locations. Intra season rainfall variability between different seasons was highest during post monsoon season followed by pre monsoon season. Drought indices standardized precipitation index (SPI) and aridity index (AI) were calculated. SPI showed that El Nino year was more related with annual and kharif deficit years. Aridity index showed that El Nino years were warm and dry compared to La Nina and neutral years and in recent years Ballowal Saunkhri showed shift towards warm and dry conditions. The ground water table level showed significant decrease at different locations of Punjab. The relationships between groundwater table and rainfall indicated that continuous deficit rainfall years led to decline in groundwater table. The correlation between wheat grain yield and diurnal range of temperature was negative at all the locations except at Ballowal saunkhri.ThesisItem Open Access Comparative testing of CERES-Wheat and InfoCrop models to predict and optimize wheat yields in Punjab(Punjab Agricultural University, Ludhiana, 2019) Sarabjit Singh; Sidhu, Prabhjyot KaurA simulation study was conducted for predicting and optimizing wheat yield in Punjab under climate change scenarios using CERES-Wheat and InfoCrop-Wheat models. The actual data on phenology and yield of three wheat cultivars (WH 1105, HD 2967 and PBW 621) sown under five dates of sowing (28 October, 4 November, 11 November, 18 November and 25 November) during rabi 2014-15 were used for calibration and during rabi 2015-16 were used for validation of the two models. The anthesis and physiological maturity of wheat cultivars was predicted within -9 to +3 days and -7 to +1 days, respectively by CERES-Wheat and -3 to +11 and -10 to +1 days, respectively by InfoCrop-Wheat model. The calibration and validation of the CERES-Wheat and InfoCrop-Wheat model showed good agreement between the observed and simulated values with RMSE value 493.8 kg/ha and 434.4 kg/ha for grain yield respectively. Keeping in view the observed trends in climate variability, phenology and yield of wheat were simulated under climatic scenarios of changes in temperature (0.5, 1.0, 1.5, 2.0, 2.5, 3.0 oC from normal), solar radiation (2.5, 5.0, 10.0, 12.5, 15.0 20.0 % from normal) and their combined interactive effects during whole season, vegetative phase, grain filling phase, 0-30 days after sowing (DAS), 30-60 DAS and 60-90 DAS. In general, with an increase in temperature above normal, both the CERES-Wheat and InfoCrop-Wheat model predicted advancement in phenological development in wheat and vice versa. With the imposition of increase in temperature and decrease in solar radiation the CERES-Wheat model predicted decrease in grain yield. The maximum reduction in grain yield of wheat was observed in whole season followed by grain filling phase, vegetative phase, 60-90 DAS, 30-60 DAS and 0-30 DAS in decreasing order. On the other hand, InfoCrop-Wheat model did not respond to the change in phenology or yield with increase or decrease in solar radiation. The CERES-Wheat model predictions showed that wheat cv WH 1105 was more tolerant to heat and radiative stress than cv HD 2967 and cv PBW 621 and hence may be recommended for cultivation due to its tolerant traits towards maintaining its yield as well as harvest index. The InfoCrop-Wheat model predicted the sowing on 26 November with nitrogen application of 135 to150 kg/ha is best option for optimizing wheat yield but the InfoCrop-Wheat model did not respond under change in plant population. Hence as is indicative from several unexpected results simulated by the InfoCrop-Wheat model, the present version 2.1 of the model needs further scrutiny and refinement. The CERES-Wheat model predicted that amongst the growing windows, sowing of wheat on November 19 with plant population of 100 m-2 and nitrogen application of 135 to 150 kg/ha is the best for optimizing wheat yield in the state.
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