Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu

Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat.  Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution

Studies on the relationship of weather on Fall armyworm damage in maize (Zea mays L.) under different growing environments

Fall armyworm is a recently occurring invasive pest in India, the most important defoliator causing drastic damage to maize production. Hence, the present study aimed to understand the temporal infestation level of Fall armyworms on maize (Zea mays L.) with weather patterns. Field experiments were conducted during Summer (February-May) and Rainy seasons, 2022 (August-December) at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore. Three different growing environments (GE1, GE2 and GE3) were created by providing staggered sowing. Regression models were developed for per cent leaf damage against three-days lagged (LT3) and seven-day lagged (LT7) weather variables. Results showed that irrespective of growing environments, weather variables showed negative correlation (Tmax: r = -0.57, -0.81*, -0.31; SSH: -0.30, -0.48, -0.39; Tmean: -0.49, -0.23, -0.30; and SR: -0.48, -0.94*, -0.40) during summer season whereas same variables (i.e Tmax =0.62*, 0.41, 0.33; SSH = 0.09, 0.68*, 0.24; Tmean = 0.29, 0.32, 0.44; and SR=0.13, 0 .67*, 0.26 ) showed a positive correlation with PLD. Rainfall exhibits positive relation (0.06, 0.54, 0.53) and negative correlation (-0.64*, -0.10, -0.02) during summer and rainy season, respectively. Among the regression models, LT7 model had higher R2 (0.65 and 0.76) than LT3 (0.57 and 0.68) during summer and rainy seasons, respectively. These models had good regression values of 0.56 and 0.70 during Rainy and Summer, respectively. It was concluded that Tmax (32.9 °C), Tmin (23.7 °C), Tmean (28.3 °C), RH-I (85.6%), RH-II (56.4%), SSH (4.1), SR (274.6 cal cm-2 m-2), afternoon cloud cover (4.8 okta) and weekly total rainfall (10.2 mm) were very conducive for the greater leaf damage

Implementation of K-Means Clustering Technique in Banana Production of Tamil Nadu, India

Aim: The main objectives of this study are to make use of the K-Means clustering approach to cluster the Banana data and to assist with crop yield prediction. Study Design: One of the methods of Big Data Analytics K-Means clustering is usedto cluster the data set. Place and Duration of Study: So far, the period 2010-2020, time series data were collected from the season and crop report, Directorate of Economics and Statistics, Chennai. Methodology: The horticulture industry has a significant impact on India's economic development. In the globe, after China, India ranks second in terms of fruit and vegetable production. Compare to the various fruits Mango and banana are one of the most abundant fruits in India. So, the Banana dataset were collected and dataset were clustered using the K-Means clustering technique and the optimum number of clusters were identify using the elbow approach. Results: According to these results from this study, there is positive relationship between the Area, Soil moisture, Maximum Temperature, Relative Humidity and negative relationship between Rainfall, Wind Speed and Minimum temperature related Banana production. Using K-Means clustering it divides the given dataset into three clusters in which cluster 3 contains high Banana production afterwards two and one. Conclusion: The selection of the most productive clusters is going to tell farmers on where to focus their efforts while planting crops in order to enhance productivity and crop production

Analysis of Trends in Climate Variables and the Adaptation Strategies Used by Cardamom Growers in Idukki District of Kerala, India

Aim: The study's main goal is to find any significant variations in the climatic variables and to analyze the preferences about adaptation strategies by the farmers to lessen the effects of the same. Study Area and Design: An ex-post-facto study was conducted at randomly selected panchayaths in Nedumkandam, Idukki, Kerala. Methodology: The climate data for 30 years (1991-2021) was analyzed using Mann-Kendall test and sen slope estimator. A sample size of 120 farmers was surveyed for identifying their preferences for adaptation measures. Adaptation strategies proposed by various institutions and experts were ranked using the Response Priority Index. Results: Throughout the july month every year, the maximum temperature rise by 0.06◦C, and this increase is significant at 1% level. The minimum temperature increased considerably by 0.06◦C at 5% level in December and by 0.04 ◦C at 10% level in January. For the month of June, there was a 6.15 mm significant decrease at the 0.01 level of significance. March had a rise in precipitation of 0.753 mm, which is noteworthy at the 0.05 level. The increase in rainfall during summer may increase panicle initiation whereas reduction in rainfall during June affects flowering. At the 0.1 criterion, the increase in May was 2.028 mm, which is considerable. And at the 0.1 level of significance, the relative humidity rises by 0.19% and 0.15%, respectively, in March and May. Fluctuation in these parameters resulted in increased pest and disease incidence. 86.66 % of farmers found it important to adopt adaptation measures. The first listed adaptation measure was maintaining a good level of shade. The least effective of the suggested solutions was growing disease and pest-resistant cultivars. Conclusion: The tests confirmed a shift in climate variables, and it is evident that this change affects cardamom production

Performance of Regional Climate Model (WRF 4.3) in Medium Range Rainfall Forecast (MRRF) for Tamil Nadu, India

The weather events are highly dynamic and fluctuating for the next few days due to enormous processes carried out by nature and physics and it is even more highly variable in tropics. The Medium Range Weather Forecast is incredibly helpful and trustworthy for agricultural purposes and rainfall is one of the most imminent events determining productivity. The Medium Range Rainfall Forecast (MRRF) given by Weather Research and Forecast model (WRF v 4.3) is verified using forecast verification scores including Ratio of Root Mean Square Error (RMSE) to the standard deviation of the observations (RSR), Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), Kling-Gupta Efficiency (KGE), and Root Mean Square Error (RMSE). Scores were computed by comparing forecast generated using two microphysics options viz., WRF Single Moment scheme (WSM-3) and Kessler scheme during South West Monsoon (SWM) and North East Monsoon (NEM) of the year 2021 for five different physiographic regions of Tamil Nadu. WSM-3 microphysics scheme outperformed in predicting MRRF for all the five regions and during both the monsoons

Standardized Precipitation Index Based Drought Assessment over the North Western Zone of Tamil Nadu, India

Drought is a natural disaster that tremendously affect the agriculture production and livelihood. Though the Tamil Nadu state is located at peninsular region of India and contributed from both the monsoons, the frequency of drought is high due to vagaries of monsoonal pattern. A study was conducted at Tamil Nadu Agricultural University to assess the drought characteristics across the north western Agro Climatic Zone (ACZ) of Tamil Nadu using Standardized Precipitation Index (SPI) during the past 30 years (1991-2020). The study clearly indicated that the Salem district had high vulnerability to drought followed by Dharmapuri and Namakkal districts during the South West Monsoon (SWM), whereas the Namakkal had high vulnerability followed by Salem and Dharmapuri during North East Monsoon (NEM)