Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India.

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70-85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast.

Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.

Resiliencies of soil phosphorus fractions after natural summer fire are governed by microbial activity and cation availability in a semi-arid Inceptisol.

The unintended impact of natural summer fire on soil is complicated and rather less studied than its above-ground impact. Recognising the impact of a fire on silvopastoral soils and their resilience can aid in improving the management of silvopastoral systems. We studied the immediate (after 1 week (W)) and short-term (after 3 months (M)) recovery of different soil biological and chemical properties after the natural fire, with specific emphasis on phosphorus (P) dynamics. Soil samples were collected from four different layers (0-15, 15-30, 30-45, and 45-60 cm) of Morus alba, Leucaena leucocephala, and Ficus infectoria based silvopastoral systems. In the 0-15 cm soil layer, soil organic carbon (SOC) declined by ∼37, 42, and 30% after the fire in Morus-, Leucaena-, and Ficus-based systems, respectively within 1W of fire. However, after 3M of fire, Morus and Leucaena regained ∼6 and 11.5% SOC as compared to their status after 1W in the 0-15 cm soil layer. After 1W of the fire, soil nitrogen (N), sulfur (S), and potassium availability declined significantly at 0-15 cm soil layer in all systems. Iron and manganese availability improved significantly after 1W of the fire. Saloid bound P and aluminium bound P declined significantly immediately after the fire, increasing availability in all systems. However, calcium bound P did not change significantly after the fire. Dehydrogenase and alkaline phosphatase activity declined significantly after the fire, however, phenol oxidase and peroxidase activity were unaltered. Resiliencies of these soil properties were significantly impacted by soil depth and time. Path analysis indicated microbial activity and cationic micronutrients majorly governed the resilience of soil P fractions and P availability. Pasture yield was not significantly improved after the fire, so natural summer fire must be prevented to avoid loss of SOC, N, and S.

Evaluation of machine learning approaches for prediction of pigeon pea yield based on weather parameters in India.

Pigeon pea is the second most important grain legume in India, primarily grown under rainfed conditions. Any changes in agro-climatic conditions will have a profound influence on the productivity of pigeon pea (Cajanus cajan) yield and, as a result, the total pulse production of the country. In this context, weather-based crop yield prediction will enable farmers, decision-makers, and administrators in dealing with hardships. The current study examines the application of the stepwise linear regression method, supervised machine learning algorithms (support vector machines (SVM) and random forest (RF)), shrinkage regression approaches (least absolute shrinkage and selection operator (LASSO) or elastic net (ENET)), and artificial neural network (ANN) model for pigeon pea yield prediction using long-term weather data. Among the approaches, ANN resulted in a higher coefficient of determination (R2 = 0.88-0.99), model efficiency (0.88-1.00) with subsequent lower normalised root mean square error (nRMSE) during calibration (1.13-12.55%), and validation (0.33-21.20%) over others. The temperature alone or its interaction with other weather parameters was identified as the most influencing variables in the study area. The Pearson correlation coefficients were also determined for the observed and predicted yield. Those values also showed ANN as the best model with correlation values ranging from 0.939 to 0.999 followed by RF (0.955-0.982) and LASSO (0.880-0.982). However, all the approaches adopted in the study were outperformed the statistical method, i.e. stepwise linear regression with lower error values and higher model efficiency. Thus, these approaches can be effectively used for precise yield prediction of pigeon pea over different districts of Karnataka in India.

Novel combination artificial neural network models could not outperform individual models for weather-based cashew yield prediction.

Cashew is an important cash crop which is ecologically sensitive, making it vulnerable to climate change. So, the present study compares the performance of stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), elastic net, and artificial neural network (ANN) individually against the ANN model combined with SLR, LASSO, elastic net, and principal components analysis (PCA) for prediction of cashew yield based on weather parameters. The model performances were evaluated using three approaches: (1) Taylor plot; (2) statistical metrics like coefficient of determination (R2), root mean square error (RMSE), and normalized RMSE (nRMSE); and (3) ranking followed by Kruskal-Wallis and Dunn's post hoc test. The results revealed that during calibration, the R2 and RMSE ranged from 0.486 to 0.999 and 2.184 to 88.040 kg ha-1, respectively, while RMSE and nRMSE varied from 3.561 to 242.704 kg ha-1 and 0.799 to 89.949%, respectively, during validation. Kruskal-Wallis and Dunn's post hoc test revealed LASSO as the best model which was at par with ELNET, SLR, and ELNET-ANN. So, these models can be used for cashew yield prediction for the study area well in advance.

Energy and carbon budgeting of traditional land use change with groundnut based cropping system for environmental quality, resilient soil health and farmers income in eastern Indian Himalayas.

Energy intensive traditional cereals based monoculture often lead to high greenhouse gas emissions and degradation of land and environmental quality. Present study aimed at evaluating the energy and carbon budget of diversified groundnut (Arachis hypogea L) based cropping system with over existing traditional practice towards the development of a sustainable production technology through restoration of soil and environmental quality and enhancement of farming resiliency by stabilizing farmers' income. The trials comprised of three introduced groundnut based systems viz. groundnut- pea (Pisum sativum), groundnut-lentil (Lens esculenta) and groundnut-toria (Brasssica campestris var. Toria) replacing three existing systems viz. maize (Zea mays L) - fallow, maize - toria, and rice (Oryza sativa L)-fallow systems. Four years study revealed that adoption of groundnut based systems reduced non-renewable energy input use (fertilizers, chemical, machinery and fossil fuels) by 25.5%, consequently that reduced the cost of production. Repeated analysis of variance measurement also affirmed that groundnut based systems (groundnut-pea>groundnut-lentil> groundnut-toria) increased the energy use efficiency, energy productivity, carbon use efficiency, net returns and decreased the specific energy and energy intensiveness. Groundnut based systems increased the mean system productivity and water productivity in terms of groundnut equivalent yield by 3.7 and 3.1 folds over existing practice. The savings of fossil fuel reduced greenhouse gas emissions owing to reduced use of farm machinery and synthetic fertilizers. Groundnut based systems significantly (p < 0.05) enhanced the soil carbon concentration (8.7-18.1%) and enzymatic activities (27.1-51.8%) over existing practice. Consequently, estimated soil quality index values were 35.9-77.3% higher under groundnut based systems than existing practice. Thus, the study indicated the resilient nature of groundnut based systems as an environmentally safe and sustainable production technology for enhancing resource use efficiency, reduce carbon emission, energy intensiveness and cost of production in the Eastern Himalaya region of India and similar ecosystems.

Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India.

Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000-2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model's performance was evaluated using R2, root mean square error (RMSE) and absolute percentage error (APE). The R2 and RMSE of the models ranged between 0.45-0.99 and 18-3624 nuts ha-1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.

Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India.

Rice 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.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Vis-NIR spectroscopy based rapid and non-destructive method to quantitate microplastics: An emerging contaminant in farm soil.

Microplastics (MPs) is the second most important environmental issue and can potentially enter into food chain through farmland contamination and other means. There are no standardized extraction methods for quantification of MPs in soil. The embedded errors and biases generated serious problems regarding the comparability of different studies and leading to erroneous estimation. To address this gap, present study was formulated to develop an efficient method for MPs analysis suitable for a wide range of soil and organic matrices. A method based on Vis-NIR (Visible-Near Infra Red) spectroscopy is developed for four different soil belonging to Alfisol, Inceptisol, Mollisol and Vertisol and two organic matter matrices (FYM and Sludge). The developed method was found as rapid, reproducible, non-destructive and accurate method for estimation of all three-density groups of MPs (Low, Medium and High) with a prediction accuracy ranging from 1.9 g MPs/kg soil (Vertisol) to 3.7 g MPs/kg soil (Alfisol). Two different regression models [Partial Least Square Regression (PLSR) and Principal Component Regression (PCR)] were assessed and PLSR was found to provide better information in terms of prediction accuracy and minimum quantification limit (MQL). However, PCR performed better for organic matter matrices than PLSR. The method avoids any complicated sample preparation steps except drying and sieving thus saving time and acquisition of reflectance spectrum for single sample is possible within 18 s. Owing to have the minimum quantification limit ranging from 1.9-3.7 g/kg soil, the vis-NIR based method is perfectly suitable for estimation of MPs in soil samples collected from plastic pollution hotspots like landfill sites, regular based sludge amended farm soils. Additionally, the method can be adapted by small scale compost industries for assessing MPs load in product like city compost which are applied at agricultural fields and will be helpful in quantifying possible MPs at the sources itself.

Predicting the invasion risk of rugose spiraling whitefly, Aleurodicus rugioperculatus, in India based on CMIP6 projections by MaxEnt.

BACKGROUND: Rugose spiraling whitefly (RSW), Aleurodicus rugioperculatus Martin, is a highly polyphagous invasive pest native to Central America. The occurrence of A. rugioperculatus in the Oriental region was first reported from Pollachi, Tamil Nadu, India in 2017. This pest is widely distributed in India, causing severe economic damage to coconut and other horticultural crops. It is a recent invasion in India and information on its potential distribution is lacking. Thus, in the present study we used the latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset through Maximum Entropy species distribution modelling (version 3.4.1, MaxEnt) to determine the potential distribution of RSW in present and future climate change scenarios in 2050 and 2070 under Shared Socioeconomic Pathway (SSP) 126 and SSP585 emission scenarios. The performance of the model was evaluated using the area under the curve (AUC), true skill statistics (TSS) and the continuous Boyce index (CBI). RESULTS: The MaxEnt model performed well and predicted the potential distribution of A. rugioperculatus with high-accuracy AUC values of 0.991 and 0.989, TSS values of 0.891 and 0.842, and CBI values of 0.972 and 0.934 for training and testing, respectively. Jackknife analysis revealed that A. rugioperculatus distribution was mostly influenced by temperature-based bioclimatic variables contributing 62.1% of the suitability, with precipitation variables contributing the remainder. The most important bioclimatic variables for RSW distribution were annual mean temperature (Bio 1, 28.9%) followed by mean diurnal range (Bio 2, 19.5%) and annual precipitation (Bio 12, 19.1). Potential suitable areas for RSW establishment were mostly found in all coastal and southern states of India. A. rugioperculatus prefers a warm and humid climate, indicating that the tropics, subtropics and temperate regions are ideal for its spread and invasion. Our results highlighted that the suitable habitat area for A. rugioperculatus is predicted to increase and highest probability of invasion and spread in 2050 and 2070 under future climate change scenarios of SSP126 and SSP585 compared to present climatic conditions. CONCLUSIONS: This is the first study to use the latest CMIP6 models and it predicts the potential distribution of RSW in India under present and future climate change scenarios. The implementation of strict domestic quarantine measures may prevent the spread and damage of RSW to noncoastal regions of India. The results of the current study should help in timely monitoring and surveillance of RSW and to formulate integrated pest management strategies at the national level to restrict its spread, invasion and damage to new areas. © 2022 Society of Chemical Industry.

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models.

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R2 values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R2 of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R2=0.99; validation R2=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options.

Land degradation vulnerability mapping in a west coast river basin of India using analytical hierarchy process combined machine learning models.

Assessment and modelling of land degradation are crucial for the management of natural resources and sustainable development. The current study aims to evaluate land degradation by integrating various parameters derived from remote sensing and legacy data with analytical hierarchy process (AHP) combined machine learning models for the Mandovi river basin of western India. Various land degradation conditioning factors comprising of topographical, vegetation, pedological, and climatic variables were considered. Integration of the factors was performed through weighted overlay analysis to generate the AHP-based land degradation map. The output of AHP was then used with land degradation conditioning factors to build AHP combined gradient boosting machine (AHP-GBM), random forest (AHP-RF), and support vector machine (AHP-SVM) model. The model performances were assessed through an area under the receiver operating characteristic (AUC). The AHP-RF model recorded the highest AUC (0.996) followed by AHP-SVM (0.987), AHP (0.977), and AHP-GBM (0.975). The study revealed that AHP combined with RF could significantly improve the model performance over solo AHP. High rainfall with high slopes and improper land use were the major causes of land degradation in the study area. The findings of the current study will aid the policymakers to formulate land degradation action plans through implementing appropriate soil and water conservation measures.

Predicting the potential geographical distribution of onion thrips, Thrips tabaci in India based on climate change projections using MaxEnt.

Onion thrips, Thrips tabaci Lindeman, an economically important onion pest in India, poses a severe threat to the domestic and export supply of onions. Therefore, it is important to study the distribution of this pest in order to assess the possible crop loss, which it may inflict if not managed in time. In this study, MaxEnt was used to analyze the potential distribution of T. tabaci in India and predict the changes in the suitable areas for onion thrips under two scenarios, SSP126 and SSP585. The area under the receiver operating characteristic curve values of 0.993 and 0.989 for training and testing demonstrated excellent model accuracy. The true skill statistic value of 0.944 and 0.921, and the continuous Boyce index of 0.964 and 0.889 for training and testing, also showed higher model accuracy. Annual Mean Temperature (bio1), Annual Precipitation (bio12) and Precipitation Seasonality (bio15) are the main variables that determined the potential distribution of T. tabaci, with the suitable range of 22-28 °C; 300-1000 mm and 70-160, respectively. T. tabaci is distributed mainly in India's central and southern states, with 1.17 × 106 km2, covering 36.4% of land area under the current scenario. Multimodal ensembles show that under a low emission scenario (SSP126), low, moderate and optimum suitable areas of T. tabaci is likely to increase, while highly suitable areas would decrease by 17.4% in 2050 20.9% in 2070. Whereas, under the high emission scenario (SSP585), the high suitability is likely to contract by 24.2% and 51.7% for 2050 and 2070, respectively. According to the prediction of the BCC-CSM2-MR, CanESM5, CNRM-CM6-1 and MIROC6 model, the highly suitable area for T. tabaci would likely contract under both SSP126 and SSP585. This study detailed the potential future habitable area for T. tabaci in India, which could help monitor and devise efficient management strategies for this destructive pest.

Investigations on annual spreading of viruses infecting cucurbit crops in Uttar Pradesh State, India.

During 2018 an intensive study was conducted to determine the viruses associated with cucurbitaceous crops in nine agroclimatic zones of the state of Uttar Pradesh, India. Total of 563 samples collected and analysed across 14 different cucurbitaceous crops. The results showed the dominance of Begomovirus (93%) followed by Potyvirus (46%), cucumber green mottle mosaic virus (CGMMV-39%), Polerovirus (9%), cucumber mosaic virus (CMV-2%) and Orthotospovirus (2%). Nearly 65% of samples were co-infected with more than one virus. Additionally, host range expansion of CMV, CGMMV and polerovirus was also observed on cucurbit crops. A new potyvirus species, zucchini tigre mosaic virus, earlier not documented from India has also been identified on five crops during the study. Risk map generated using ArcGIS for virus disease incidence predicted the virus severity in unexplored areas. The distribution pattern of different cucurbit viruses throughout Uttar Pradesh will help identify the hot spots for viruses and will facilitate to devise efficient and eco-friendly integrated management strategies for the mitigation of viruses infecting cucurbit crops. Molecular diversity and evolutionary relationship of the virus isolates infecting cucurbits in Uttar Pradesh with previously reported strains were understood from the phylogenetic analysis. Diverse virus infections observed in the Eastern Plain zone, Central zone and North-Eastern Plain zone indicate an alarming situation for the cultivation of cucurbits in the foreseeable future.

Evaluation of different water absorption bands, indices and multivariate models for water-deficit stress monitoring in rice using visible-near infrared spectroscopy.

Accurate estimation of plant water status is a major factor in the decision-making process regarding general land use, crop water management and drought assessment. Visible-near infrared (VNIR) spectroscopy can provide an effective means for real-time and non-invasive monitoring of leaf water content (LWC) in crop plants. The current study aims to identify water absorption bands, indices and multivariate models for development of non-destructive water-deficit stress phenotyping protocols using VNIR spectroscopy and LWC estimated from 10 different rice genotypes. Existing spectral indices and band depths at water absorption regions were evaluated for LWC estimation. The developed models were found efficient in predicting LWC of the samples kept in the same environment with the ratio of performance to deviation (RPD) values varying from 1.49 to 3.05 and 1.66 to 2.63 for indices and band depths, respectively during validation. For identification of novel indices, ratio spectral indices (RSI) and normalised difference spectral indices (NDSI) were calculated in every possible band combination and correlated with LWC. The best spectral indices for estimating LWC of rice were RSI (R1830, R1834) and NDSI (R1830, R1834) with R2 greater than 0.90 during training and validation, respectively. Among the multivariate models, partial least squares regression (PLSR) provided the best results for prediction of LWC (RPD = 6.33 and 4.06 for training and validation, respectively). The approach developed in this study will also be helpful for high-throughput water-deficit stress phenotyping of other crops.

Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.

Identification and development of salinity tolerant genotypes and varieties are one of the promising ways to improve productivity of salt-affected soils. Alternate methods to achieve this are required as the conventional methods are time-consuming and often difficult to handle large population of genotypes. In this context, hyperspectral remote sensing could be one of the rapid, repeatable and reliable methods. The aim of the present study is to develop non-invasive high-throughput phenotyping techniques for salinity stress monitoring in rice. Spectral signature of leaf samples from 56 salinity stress tolerant and sensitive rice genotypes were collected at maximum tillering and flowering stage in visible and near-infrared (VNIR) domain. The spectral reflectance data and rice leaf potassium, sodium, calcium, magnesium, iron, manganese, zinc and copper concentration were analyzed for optimum index identification and multivariate model development. Novel hyperspectral indices sensitive to leaf nutrient status as affected by salinity stress were identified. The correlation coefficient during calibration and validation of the optimized indices varied between 0.34-0.63 and 0.36-0.66, respectively. To develop multivariate model, solo partial least square regression (PLSR), PLSR- and principal component analysis (PCA)-combined machine learning models were tested. The results revealed that the performance of PLSR-combined models was the best followed by PCA-based model while indices based model was found to be least accurate. The results obtained in the present study showed potential of hyperspectral remote sensing for non-destructive phenotyping of salinity stress.

Soil quality assessment of coastal salt-affected acid soils of India.

Soil salinity and acidity are some of the major causes of land degradation and have a negative impact on agricultural productivity. Assessing soil quality (SQ) of soils affected by soil salinity and acidity is required for their sustainable utilization for agricultural production. The aim of the present study was to evaluate the SQ of the salt-affected acid soils of the Indian West Coastal region using the additive and weighted soil quality indices (SQIs). The SQIs were developed using a total dataset (TDS) and a minimum dataset (MDS). The TDS comprised of 15 different soil properties as electrical conductivity (EC), pH, bulk density, soil available nitrogen (N), phosphorus (P), potassium (K), sulfur (S), boron (B), iron (Fe), manganese (Mn), copper (Cu), zinc (Zn) and exchangeable calcium (Ca), magnesium (Mg), and sodium (Na) measured on 300 soil samples (depth 0-0.15 m). Based on principal component analysis and correlation analysis, an MDS with soil properties like soil pH, EC, Na, Cu, Mn, and BD was formed. Using two approaches (additive and weighted), two datasets (TDS and MDS), and two scoring methods (linear and non-linear), eight SQIs were developed. The MDS-based linear weighted and non-linear weighted SQI found suitable to evaluate SQ of salt-affected acid soils and SQI had a significant and negative correlation of - 0.83 and - 0.70 (p < 0.01) with EC, respectively. Thus, it is clear that the SQ considerably reduces with an increase in soil salinity. The performance of the MDS-based SQIs was better than the TDS to discriminate different soil salinity classes. The agreement between the linear and non-linear scoring method of SQI had a linear relationship with a coefficient of determination (R2) of 0.91-0.96. Thus, assessing the SQ of salt-affected acid soils using MDS, linear scoring, and weighted approach of the soil quality indexing could save the time and cost involved.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics.

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.