Perspective Chapter: Downscaling of Satellite Soil Moisture Estimates

Soil moisture is a key parameter in the hydrological cycle and plays a critical role in global climate. The capacity to forecast drought and floods, manage water resources, and make field-scale decisions depends on accurate and thorough information on soil moisture. In addition to the instrument-based field observation approaches, dynamic mapping of soil moisture has been made possible by satellite remote sensing technologies. Estimates of soil moisture at a global and regional scale from optical and thermal remote sensing have been explored, and considerable advancements have been made. However, these global soil moisture products have coarse spatial resolutions and are typically unsuitable for field-level hydrological and agricultural applications. In this regard, this chapter presents a comprehensive review of the latest downscaling methods to improve the coarse-spatial and temporal resolution of soil moisture products. The main approaches discussed in the chapter include active passive fusion, optical/thermal based, topography based, and data assimilation methods. The physical background, current status, advantages and limitations associated with each downscaling approach has been thoroughly examined. Each of these optical/thermal, microwave-based methods for soil moisture estimation involves intricate derivation at different spatiotemporal scales, which can be combined using recent advances in machine learning

A 19-SNP coronary heart disease gene score profile in subjects with type 2 diabetes: the coronary heart disease risk in type 2 diabetes (CoRDia study) study baseline characteristics

Background: The coronary risk in diabetes (CoRDia) trial (n = 211) compares the effectiveness of usual diabetes care with a self-management intervention (SMI), with and without personalised risk information (including genetics), on clinical and behavioural outcomes. Here we present an assessment of randomisation, the cardiac risk genotyping assay, and the genetic characteristics of the recruits. / Methods: Ten-year coronary heart disease (CHD) risk was calculated using the UKPDS score. Genetic CHD risk was determined by genotyping 19 single nucleotide polymorphisms (SNPs) using Randox’s Cardiac Risk Prediction Array and calculating a gene score (GS). Accuracy of the array was assessed by genotyping a subset of pre-genotyped samples (n = 185). / Results: Overall, 10-year CHD risk ranged from 2–72 % but did not differ between the randomisation groups (p = 0.13). The array results were 99.8 % concordant with the pre-determined genotypes. The GS did not differ between the Caucasian participants in the CoRDia SMI plus risk group (n = 66) (p = 0.80) and a sample of UK healthy men (n = 1360). The GS was also associated with LDL-cholesterol (p = 0.05) and family history (p = 0.03) in a sample of UK healthy men (n = 1360). / Conclusions: CHD risk is high in this group of T2D subjects. The risk array is an accurate genotyping assay, and is suitable for estimating an individual’s genetic CHD risk. / Trial registration: This study has been registered at ClinicalTrials.gov; registration identifier NCT0189178

An integrated organic farming system: innovations for farm diversification, sustainability, and livelihood improvement of hill farmers

IntroductionOrganic farming is a promising solution for mitigating environmental burdens related to input-intensive agricultural practices. The major challenge in organic agriculture is the non-availability of large quantities of organic inputs required for crop nutrition and sustaining soil health, which can be resolved by efficient recycling of the available on- and off-farm resources and the integration of the components as per the specific locations.MethodsAn integrated organic farming system (IOFS) model comprising agricultural and horticultural crops, rainwater harvesting units, livestock components, and provisions for nutrient recycling was developed and disseminated in the adopted organic villages Mynsain, Pynthor, and Umden Umbathiang in the Ri-Bhoi District, Meghalaya, India, to improve the income and livelihood of farmers. Harvested rainwater in farm ponds and Jalkunds was used for live-saving irrigation in the winter months and diversified homestead farming activities, such as growing high-value crops and rearing cattle, pigs, and poultry.ResultsMaize, french bean, potato, ginger, tomato, carrot, and chili yields in the IOFS model increased by 20%−30%, 40%−45%, 25%−30%, 33%−40%, 45%−50%, 37%−50%, and 27%−30%, respectively, compared with traditional practices. Some farmers produced vermicompost in vermibeds (made of high-density polyethylene) and cement brick chambers, generating 0.4−1.25 tons per annum. Two individual farmers, Mr. Jrill Makroh and Mrs. Skola Kurbah obtained net returns (without premium price) of Rs. 46,695 ± 418 and Rs. 31,102 ± 501 from their respective 0.27- and 0.21-ha IOFS models, which is equivalent to Rs. 172,944 ± 1,548/ha/year and Rs. 148,105 ± 2,385/ha/year, respectively. The net returns obtained from the IOFS models were significantly higher than those obtained from the farmers' practice of maize-fallow or cultivation of maize followed by vegetable (~30% of the areas). It is expected that, with the certification of organic products, the income and livelihood of the farmers will improve further over the years. While Mr. Jrill Makroh's model supplied 95.1%, 82.0%, and 96.0% of the total N, P2O5, and K2O, respectively, needed by the system, Mrs. Skola Kurbah's model supplied 76.0%, 68.6%, and 85.5% of the total N, P2O5, and K2O, respectively.DiscussionThus, IOFS models should be promoted among hill farmers so that they can efficiently recycle farm resources and increase their productivity, net returns, and livelihood while reducing their dependence on external farm inputs

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Not AvailableAbiotic stresses are one of the major factors affecting crop production in many parts of the globe. The need of the hour is to reduce the yield losses due to these abiotic stresses. In this connection, early detection and corrective measures can help to reduce the impact of stresses on crop growth and yield. The recent developments in remote sensing particularly hyperspectral remote sensing hold a major key in early detection of abiotic stress over a larger area with less involvement of cost, time and labour. The works relevant to abiotic stress characterization particularly water and nutrient stress based on plant spectral reflectance are dealt in this chapter. The research work done previously elucidates that the water and nutrient monitoring through remote sensing is possible. The remote sensing-based techniques can lead to the development of real-time management of water and nutrient stress, thereby reducing the yield losses due to these stresses.Not Availabl

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Not AvailableRice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.Not Availabl

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Not AvailableRice is generally grown under completely flooded condition and providing food for more than half of the world’s population. Any changes in weather parameters might affect the rice productivity thereby impacting the food security of burgeoning population. So, the crop yield forecasting based on weather parameters will help farmers, policy makers and administrators to manage adversities. The present investigation examines the application of stepwise multiple linear regression (SMLR), artificial neural network (ANN) solely and in combination with principal components analysis (PCA) and penalised regression models (e.g. least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)) for rice yield prediction using long-term weather data. The R2 and root mean square error (RMSE) of the models varied between 0.22–0.98 and 24.02–607.29 kg ha−1, respectively during calibration. During validation with independent dataset, the RMSE and normalised root mean square error (nRMSE) ranged between 21.35–981.89 kg ha−1 and 0.98–36.7%, respectively. For evaluation of multiple models for multiple locations statistically, overall average ranks on the basis of R2 and RMSE of calibration; RMSE and nRMSE of validation were calculated and non-parametric Friedman test was applied to check the significant difference among the models. The ranking of the models revealed that LASSO (2.63) was the best performing model followed by ENET (3.07) while PCA-ANN (4.19) was the worst model which was found significant at p < 0.001. The reason behind good performance of LASSO and ENET is that these models prevent overfitting and reduce model complexity by penalising the magnitude of coefficients. Then, pairwise multiple comparison test was performed which indicated LASSO as the best model which was found similar to SMLR and ENET. So, for prediction of rice yield, these models can very well be utilised for west coast of India.Not Availabl

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Not AvailableManganese (Mn) deficiency limits wheat productivity on sandy loam, calcareous and alkaline soils cropped with rice. Variation of wheat genotypes to sustain production and Mn use from Mn deficient condition was investigated to screen efficient genotypes. Forty-seven diverse wheat genotypes were evaluated on Mn sufficient (0.195 mM) and Mn deficient (0 mM) nutrient solution to elucidate physiological basis of Mn deficiency tolerance and to develop manganese deficiency tolerance index (MDTI). Shoot dry weight and mean Mn accumulation was 136.7% and 76.5% enhanced when Mn nutrition was improved, respectively. Efficient genotypes under limited Mn had lower root length/shoot weight ratio but higher relative shoot growth rate with higher shoot demand on root which reflected higher Mn influx. Genotypes were classified as tolerant (>0.66), semi-tolerant (0.33–0.66) and sensitive (<0.33) on the basis of MDTI (0–1 scale). Manganese efficient genotypes are most desirable for sustainable production of wheat under low Mn.Not Availabl

Comparison of weather-based wheat yield forecasting models for different districts of Uttarakhand using statistical and machine learning Techniques

The prediction of crop yield before harvest is crucial for facilitating the formulation and implementation of policies about food safety, transportation cost, and import-export, storage and marketing of agro-products. The weather plays a crucial role in crop growth and development. Therefore, models using weather variables can provide reliable forecasts for crop yield and choosing the right model for crop production forecasts can be difficult. Therefore in the present study, an attempt was made to find the best model for wheat yield forecast by using five different techniques viz. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ELNET) and Ridge regression. Historical wheat yield data (taken from the Directorate of Economics and Statistics, Ministry of Agriculture and Farmers Welfare) and weather data of past 18-20 years were collected for seven different districts of Uttarakhand. Analysis was carried out by fixing 80% of the data for calibration and remaining dataset for validation. The present study concluded that the performance of ANN was good for crop yield forecasting as compared to the other models based on the value of RMSE (0.005 - 0.474) and nRMSE (0.166 - 26.171)

Novel ensemble machine learning models in flood susceptibility mapping

The research aims to propose the new ensemble models by combining the machine learning techniques, such as rotation forest (RF), nearest shrunken centroids (NSC), k-nearest neighbour (KNN), boosted regression tree (BRT), and logitboost (LB) with the base classifier adabag (AB) for flood susceptibility mapping (FSM). The proposed models were implemented in the central west coast of India, which is vulnerable to flood events. For flood inventory mapping, a total of 210 flood localities were identified. Twelve effective factors were selected using the boruta algorithm for FSM. The area under the receiver operating characteristics (AUROC) curve and other statistical measures (sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and mean absolute error (MAE)) were employed to estimate and compare the success rate of the approaches. The validation results of the individual models in terms of AUC value were AB (92.74%) >RF (91.50%) >BRT (90.75%) >LB (89.07%) >NSC (88.97%) >KNN (83.88%), whereas the ensemble models showed that the AB-RF (94%) was of the highest prediction efficiency followed by, AB-KNN (93.33%), AB-NSC (93.02%), AB-LB (92.83%), and AB-BRT (92.64%). The outcomes of the ensemble models established that the AB is more appropriate to increase the accuracy of different single models. Therefore, this study can be useful for proper planning and management of the study area and flood hazard mapping in alike geographic environment

Innovative trend analysis of rainfall in relation to soybean productivity over western Maharashtra

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra