A novel weather parameters prediction scheme and their effects on crops

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

Agro-meteorology of Indian Brassicas

Field experiment was conducted for two crop seasons (1996-97 & 1997-98) at CCS, HAU, Hisar research farm to study the effect of weather parameters on growth and yield of mustard. The results indicated that an increase in maximum temperature and duration of sunshine hours resulted in increased leaf area index (LAI). The increase in daytime temperature resulted in higher biomass accumulation during vegetative phase, but the trend was reversed during physiological maturity. The biomass accumulation in brassicas increased with increase in evaporation rate during the grand growth period. However, latter on during the physiological maturity, increase in evaporation rate resulted in decline of biomass accumulation. Further, it was noted that the magnitudes of some important weather parameters (maximum and minimum temperatures, pan evaporation and morning relative humidity) during the vegetative phase of crop played decisive role in deciding the quantum of seed yield which is a resultant of various yield attributes. The rainfall during the crop growing season either have no association or had a negative relationship with yield and yield attributes because crop never experienced water stress as abundant moisture was made available through irrigation.

Water management in wheat using non-traditional techniques

Water is one of the most limiting resources for agricultural production. Due to uneven distribution of rainfall, supplemental irrigation is often required to produce sustainable yield level. Timing and frequency of irrigation is one of the most important tactical decisions, which a farmer has to make to maximize profit from limited water availability. Computer based dynamic simulation models have the capability to assess management options under different environments to help in decision making. In this study, CRESS-Wheat Model V-3.5 has been utilized to quantify the optimum utilization of limited water for popular wheat genotypes of NW India for operational use in Agrometeorological Advisory services with routinely measured weather parameters.

In-orchard population dynamics of Erwinia amylovora on apple flower stigmas

Populations of the fire blight pathogen Erwinia amylovora Ea110 on apple flower stigmas were tracked over the course of apple bloom in field studies conducted between 2016 and 2019. In 18 of 23 experiments, flower stigmas inoculated on the 1st day of opening were found to harbor large (106-107 cells / flower) populations of E. amylovora when assessed three to five days post-inoculation. However, populations inoculated on stigmas of flowers that were already open for three days did not reach 106 cells / flower, and populations inoculated on stigmas of flowers that were already open for five days never exceeded 104 cells / flower. During this study, >10-fold increases in E. amylovora stigma populations in a 24-hr time period (termed population surges) were observed on 34.8%, 20.0%, and 4.0% of possible days on 1-day, 3-day, and 5-day open flowers, respectively. Population surges occurred on days with average temperatures as high as 24.5°C and as low as 6.1°C. Experiments incorporating more frequent sampling during days and overnight revealed that many population surges occurred between 10:00 PM and 2:00 AM. A Pearson’s correlation analysis of weather parameters occurring during surge events indicated that population surges were significantly associated with situations where overnight temperatures either increased or remained constant, where wind speed decreased, and where relative humidity increased. This study refines our knowledge of E. amylovora population dynamics and further indicates that E. amylovora is able to infect flowers during exposure to colder field temperatures than previously reported.

Evaluation of dynamic simulation model for wheat genotypes under diverse environments in India

Performance of dynamic crop growth simulation model (CERES -Wheat v3.5) has been evaluated for various wheat genotypes in wheat growing regions of India. The genetic coefficients were developed and sensitivity analysis was carried out for the genotypes under study. The simulated phenology and yield were found in agreement with observed ones suggesting that calibrated model may be operationally used with routinely observed soil, crop and weather parameters.

Wheat yield prediction based on weather parameters using multiple linear, neural network and penalised regression models

Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques. Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.

Effect of weather parameters, host resistance and sowing date on disease severity and temporal dynamics of Fusarium wilt in chickpea (Cicer arietinum L.)

Fusarium wilt incited by Fusarium oxysporum f. sp. ciceris is an economically damaging disease of chickpea (Cicer arietinum L.). Field experiments on epidemiological studies revealed that sowing during second fortnight of November curtailed the disease severity index (22.50 and 20.83% during 2018-19 and 2019-20, respectively) whereas, sowing during first fortnight of October enhanced the disease severity index (34.86 and 30.41%). The area under disease progress curve was significantly higher in susceptible variety JG 62 and was the least in resistant variety HC 1. The correlation analysis exhibited positive correlation of disease severity index with maximum and minimum temperature while negative correlation with relative humidity morning and evening, irrespective of date of sowing. The principal component analysis depicted resistance index, sowing time and weather parameters as positional factors in determining Fusarium wilt progression. In susceptible variety, Gompertz model was the best fitted model for simulating the Fusarium wilt epidemic over time.

The estimation of cotton yield based on weather parameters in Maharashtra

ABSTRACT. The cotton yield of 12 years (1975-1987), for five districts in Vidarbha region of Maharashtra, was taken for statistical-regression study. It is found that the higher temperature during first fortnight of September, which is period of budding and flowering is favourable for better yield. The cooler nights during second fortnight of October, when the crop is generally in fruiting stage, also help in good increases in final cotton yield. Higher rainfall, dufing last week of June to first week of July, when the crop is in the germination period, causing logging, reduces the seedling and more number of rainy days in second fortnight of December hamper the bolll bursting and thus al1ecting the cotton yield adversely.