Increasing weather variability and corresponding increased threat to the sustainability of the system and to the food security of any nation raises the importance of weather analysis in a range of ...studies. Meteorological data, hence, is used as a key component while developing a weather-based risk assessment and impact assessment models. However, despite of the availability of global meteorological data in real time and several state-of the art dynamic prediction system, such models demand downscaling of these datasets to the regions of interest. The present scientific fraternity has been able to provide a range of datasets at needed spatial resolution, which are generated through interpolation, weather generation methods, satellite-based remote sensing methods, and others. Each of the datasets has their own advantages and limitations. They are not universal, because of which their robustness and reproducibility varies with location. Therefore, the present study is basically evaluation of the freely available data sources (Grid IMD, NASA POWER and MarkSim) to know which one fits best to the study area. Statistical techniques such as error statistics, correlation analysis, anomaly, and percent deviation have been used for weather dataset at three timescales (daily, weekly, and monthly). Results for maximum and minimum temperature indicated that NASA POWER datasets are more reliable than IMD data for Ranichauri (at all the three timescales) and Roorkee (only at daily and weekly timescale), unlike Udham Singh Nagar for which IMD gives better results for daily data; and MarkSim at weekly and monthly scale. It was also observed that for Udham Singh Nagar and Roorkee, MarkSim results are found to be better for RCP 2.6 as well as RCP 4.5 at higher timescales. Better performance of Tmax under RCP 4.5 indicates that the emission activities have increased in the districts, which can be attributed directly to the increased industrial establishments in the region.
Rice is one of the most important cereal foods not only for India but also for the world. The production of crop depends upon the favorable climatic conditions. Farmers’ access to more accurate data ...on crop yields in various climate conditions can aid in crucial agronomic and crop selection decisions. Taking this into account, the motive of the present research was to find the best method of predicting rice crop yield in seven important rice producing districts of Uttarakhand, namely Udham Singh Nagar, Nainital, Haridwar, Dehradun, Champawat, Tehri-Garhwal, and Pauri Garhwal. Data on the weather variables for the crop-growing season (27th to 44th SMW) for 19 years was gathered from the respective district and the NASA power website, while rice production data for the research period was gathered from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare. Stepwise multiple linear regression (SMLR), least absolute shrinkage and selection operator (LASSO), ridge regression, elastic net (ELNET), and artificial neural network (ANN) were employed for the model’s development. The 80% data of the total datasets was utilized to calibrate the models, while the remaining 20% data was allocated for the model validation. On examining these models, LASSO was found to be the finest performing model followed by ELNET, while SMLR was the worst performing model during calibration stage. During validation stage, ANN performed better for Champawat, Dehradun, Haridwar, Pauri Garhwal, and Udham Singh Nagar. The performance of ELENT and LASSO was found to be best for Nainital and Tehri Garhwal, respectively. The performance of ridge regression and SMLR were found to be poor as compared to the other models for the rice yield forecasting.
Accurate crop yield forecasting prior to harvest plays a vital role in formulating, implementing, and optimizing policies concerning food safety, as well as in the efficient management of ...agro-product storage and marketing. The growth and development of crops are inherently influenced by weather conditions, making models that utilize weather variables indispensable for providing reliable predictions of crop yields. However, selecting the most suitable crop production forecasting model can pose a challenging task. Therefore, in this study, three multivariate models were developed to predict soybean yield in eight major districts of Uttarakhand: Udham Singh Nagar, Almora, Uttarkashi, Dehradun, Pauri-Garhwal, Tehri-Garhwal, Rudraprayag, and Pithoragarh. The models used were Artificial Neural Networks (ANN), Principal Component Analysis—Artificial Neural Networks (PCA-ANN), and Least Absolute Shrinkage and Selection Operator (LASSO). To build and assess the models, historical time series data of soybean yields and weather indices were utilized. The dataset was divided into calibration (80% of the data) and validation sets (remaining data) to evaluate the model predictions. The models were trained to predict soybean yield based on average values of phenological stages derived from daily weather data. Both weighted and unweighted weather indices were employed in the computation. After evaluating the models, it was observed that the PCA-ANN model outperformed all others in predicting soybean yield. The overall ranking of model performances for all locations was as follows: PCA-ANN > ANN > LASSO. It was also noted that the PCA-ANN hybrid model was the most effective for forecasting soybean yields in the examined districts of Uttarakhand, providing valuable insights for agricultural planning and decision-making.
Abstract
Three shrinkage regression and two machine‐learning approaches were evaluated to derive models for the prediction of epidemiological characteristics of white rust of mustard, using data from ...112 epidemics in the field. Four epidemiological characteristics were considered: (a) crop age at first appearance of disease, (b) crop age at highest disease severity, (c) highest disease severity in a growing season and (d) area under disease progress curve (AUDPC), along with (e) crop yield to measure the effects of disease on crop performance. We developed models using weather indices to predict these variables using five different approaches: ANN, Elastic Net, LASSO, random forest and ridge regression. One model was developed for each sowing date corresponding to each dependent variable. Two hundred different models were developed. All models performed well at the calibration stage for most of the five variables at all sowing dates. However, at the validation stage, ANN‐derived models outperformed (
R
2
val ~ 1.00, nRMSEV ~0.00 and MBEV ~0.00 in most cases) the three shrinkage regression‐derived models in predicting all five variables. Predictions by random forest‐ and LASSO‐derived models were acceptable for AUDPC and crop yield. Evaluation metrics (including
R
2
val, nRMSEV and MBEV) suggested that ENET‐ and ridge‐derived models do not perform satisfactorily, whereas ANN‐derived models yielded reliable results and thus generate robust predictions. The present work constitutes a systematic effort to compare modelling methods for disease and yield prediction and illustrates the relevance of weather variables in predicting multiple epidemiological variables, and of multiple disease variables as predictors of actual crop yield.
Crop yield forecasting before harvesting is critical for the creation, implementation, and optimization of policies related to food safety as well as for agro-product storage and marketing. Crop ...growth and development are influenced by the weather. Therefore, models using weather variables can provide reliable predictions of crop yields. It can be tough to select the best crop production forecasting model. Therefore, in this study, five alternative models, viz., stepwise multiple linear regression (SMLR), an artificial neural network (ANN), the least absolute shrinkage and selection operator (LASSO), an elastic net (ELNET), and ridge regression, were compared in order to discover the best model for rice yield prediction. The outputs from individual models were used to build ensemble models using the generalized linear model (GLM), random forest (RF), cubist and ELNET methods. For the previous 21 years, historical rice yield statistics and meteorological data were collected for three districts under three separate agro-climatic zones of Chhattisgarh, viz., Raipur in the Chhattisgarh plains, Surguja in the northern hills, and Bastar in the southern plateau. The models were calibrated using 80% of these datasets, and the remaining 20% was used for the validation of models. The present study concluded that for rice crop yield forecasting, the performance of the ANN was good for the Raipur (Rcal2 = 1, Rval2= 1 and RMSEcal = 0.002, RMSEval = 0.003) and Surguja (Rcal2 = 1, Rval2= 0.99 and RMSEcal = 0.004, RMSEval = 0.214) districts as compared to the other models, whereas for Bastar, ELNET (Rcal2 = 90, Rval2= 0.48) and LASSO (Rcal2 = 93, Rval2= 0.568) performed better. The performance of the ensemble model was better compared to the individual models. For Raipur and Surguja, the performance of all the ensemble methods was comparable, whereas for Bastar, random forest (RF) performed better, with R2 = 0.85 and 0.81 for calibration and validation, respectively, as compared to the GLM, cubist, and ELNET approach.
Background: Climate change has become a major challenge in cultivation of chickpea and productivity. Negative impacts of climate change are likely to result from the effects of high temperature, low ...temperature, drought and excessive moisture and these factors affect crop yield ultimately.
Methods: Keeping in view for quantifying the effects an experiment was laid out in split plot design. The experiment with three dates of sowing i.e. 12th December 2018 (D1), 22nd December 2019 (D2) and 2nd January 2019 (D3) as main plot treatments and the four microclimatic regimes viz. open field (T1), Open roof (T2), perforated roof (T3) and closed or packed (T4) by 100 GSM plastic film as sub plot treatments was laid to analyse the impact of temperature variation.
Result: The major finding of the study is that the chickpea crop sown on 12th Dec. (D1) found highest grain yield (1200 kg ha-1) as compared to 22nd Dec. (845 kg ha-1) and 2nd Jan. (638 kg ha-1). This may be mainly attributed to congenial weather during the entire growing period. 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-1 was 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.
Rheumatoid arthritis (RA) is a chronic autoimmune of an unknown etiology. Air pollution has been proposed as one of the possible risk factors associated with disease activity, although has not been ...extensively studied. In this study, we measured the relationship between exposure to air pollutants and RA activity. Data on RA patients were extracted from the Kuwait Registry for Rheumatic Diseases (KRRD). Disease activity was measured using disease activity score with 28 examined joints (DAS-28) and the Clinical Disease Activity Index (CDAI) during their hospital visits from 2013 to 2017. Air pollution was assessed using air pollution components (PM 10 , NO 2 , SO 2 , O 3 , and CO). Air pollution data were obtained from Kuwait Environmental Public Authority (K-EPA) from six different air quality-monitoring stations during the same period. Multiple imputations by the chained equations (MICE) algorithm were applied to estimate missing air pollution data. Patients data were linked with air pollution data according to date and patient governorate address. Descriptive statistics, correlation analysis, and linear regression techniques were employed using STATA software. In total, 1651 RA patients with 9875 follow-up visits were studied. We detected an increased risk of RA using DAS-28 in participants exposed to SO 2 and NO 2 with β = 0 . 003 (95% CI: 0.0004-0.005, p < 0 . 01 ) and β = 0 . 003 (95% CI: 0.002-0.005, p < 0 . 01 ), respectively, but not to PM 10 , O 3 , and CO concentrations. Conclusively, we observed a strong association between air pollution with RA disease activity. This study suggests air pollution as a risk factor for RA and recommends further measures to be taken by the authorities to control this health problem.
Abstract
Background
This study aims to estimate the influence of air pollution ambients using the Air Quality Index (AQI) to Rheumatoid Arthritis (RA) disease activity. Disease Activity Score with 28 ...examined joints (DAS-28) and Clinical Disease Activity Index (CDAI) considered as disease activity indices for RA patients in the state of Kuwait.
Methods
Data for patients with RA disease were collected from Kuwait Registry for Rheumatic Diseases (KRRD) from 2013 to 2017. Moreover, data on air pollution obtained from The Kuwait Environmental Public Authority (K-EPA) during the same period. Statistical analysis was conducted using STATA to highlight the significant association between study variables. Descriptive statistics, correlation analysis and linear regression model techniques were employed to estimate the significant associations between RA disease activity represented by DAS-28 and CDAI; with air pollution components (PM10, NO2, SO2, O3, and CO). Multiple Imputation by Chained Equations (MICE) algorithm was also employed to tackle the value of the missing data for air pollution data.
Results
Total of 9,875 patients visits included in the analysis that matching with air pollution information from K-EPA database according to date and patient living address governorate. The study found SO2 and NO2 were significantly associated with RA disease activity using DAS-28 index, also, for CDAI index as well. For the score of RA disease activity using DAS-28 index, the correlation results show a positive significant correlation with exposure of SO2 using AQI (rp = 0.07), also the same results with the with the exposure of NO2 using AQI (rp = 0.07). The final model is demonstrating the effect from air-pollutants gaseous with RA factors (Swollen, RF, anti-CCP, ESR, CRP) on RA disease activity. The AQI of NO2 and SO2 still showed positive associations with disease activity performance of RA. The linear regression model shows a positive effects of NO2 (beta = 0.003, 95% CI: 0.002-0.005) and (beta = 0.048, 95\% CI: 0.030-0.066) for DAS-28 and CDAI respectively, where for SO2, the results shows positive significant effect with (beta = 0.003, 95% CI: 0.0004-0.005) and (beta = 0.044, 95% CI: 0.018-0.070) for DAS-28 and CDAI respectively.
Conclusion
In conclusion, our study showed that air ambients were significantly correlated to RA disease activity scores and should be considered as a possible risk factor for RA activity.
Disclosures
A.R. Alsaber None. J. Pan None. A. Al-Herz None. D.S. Alkandary None. A. Al-Hurban None. P. Setiya None.
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