Browsing by Author "Panigrahi, Niranjan"

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    Assessment of traditional rainwater harvesting system in barren lands of a semi-arid region: a case study of Rajasthan (India)
    (Elsevier, 2022-06-25) Yadav, Basant; Patidar, Nitesh; Sharma, Anupma; Panigrahi, Niranjan; Sharma, Rakesh K.; Loganathan, V.; Krishan, Gopal; Singh, Jaswant; Kumar, Suraj; Parker, Alison
    Study region Dudu station, Rajasthan, India Study focus Rainwater harvesting can be used as a method to recharge aquifers. This can happen with a variety of scales and technologies. One such example is shallow infiltration ponds (Chaukas) which recharge groundwater and increase soil moisture facilitating pastureland development. A HYDRUS-1D model was used to estimate potential groundwater recharge. The model was calibrated using field data from 2019 and validated using data from 2020. The time series of Normalized Difference Vegetation Index (NDVI) was derived at annual scale to assess changes in the vegetation cover. New hydrological insights for the region The modeling revealed that an additional 5% of the rainfall depth was being recharged into the groundwater. In addition, the additional soil moisture was allowing natural grass cover to develop, which could be used by the local community as pastureland. These twin benefits that the local communities are realizing could be scaled up beyond Dudu, to India, and worldwide, as many regions have barren land that is slightly sloping, together with permeable soils, which are the only conditions for the construction of Chaukas. These Chauka systems have helped in sustainable water resources management in these water-stressed regions and the additional livelihood support through developed pastures for animal husbandry.
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    Evaluation of regression algorithms for estimating leaf area index and canopy water content from water stressed rice canopy reflectance
    (Elsevier, 2021-08-11) Panigrahi, Niranjan; Das, Bhabani Sankar
    Optical remote sensing (RS) with robust algorithms is needed for accurate assessment of crop canopy features. Despite intensive studies on algorithms, their performance using RS needs to be improved. We evaluated five different algorithms (partial-least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), locally-weighted-PLSR (PLSRLW) and PLSR with feature selection (PLSRFS)) for rapid assessment of leaf area index (LAI) and canopy water content (CWC) for rice canopies using canopy reflectance spectra over visible to short-wave infrared region. Two pooled datasets of LAI (600) and CWC (480) were collected from two replicated field experiments during 2014–15 and 2015–16 rice growing season. The performance of each algorithm was evaluated using coefficient of determination (R2). Results showed that PLSRLW performed more accurately than other algorithms with R2 values 0.77 and 0.66 for LAI and CWC, respectively. We also used a bootstrapping approach to generate a kernel density estimator of root mean squared error values for each model. The results suggested that the improvement in prediction accuracy of LAI and CWC can be achieved if a suitable algorithm is selected by assigning higher weights to calibration samples, which has similar canopy structure as the test sample. Subsetting of the canopy spectral data results large error values in test dataset, therefore the use of entire season canopy spectral data should be used for model calibration.
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    Exploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale
    (MDPI, 2024-10-01) Dash, Prava Kiran; Miller, Bradley A.; Panigrahi, Niranjan; Mishra, Antaryami
    Essential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R2 of model fit. Lin’s concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents.
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    Identifying opportunities to improve digital soil mapping in India: a systematic review
    (Elsevier, 2021-12-27) Dash, Prava Kiran; Panigrahi, Niranjan; Mishra, Antaryami
    Soaring food demand, population pressure, land degradation, small size of agricultural land holdings, and diversified soil types in India require advanced digital soil mapping (DSM) for sustainable land management. This paper systematically reviews the common trends of SCORPAN based DSM in India to identify the important research gaps and opportunities to improve in future. A systematic literature search from 2000 to October 2021 has yielded 35 numbers of peer reviewed articles, which have performed DSM in India following the SCORPAN approach. The increased number of published articles from 2017 onwards suggests that there is a growing interest for DSM in India over the past few years. However, only two articles have prepared digital soil maps at the national extent. Moreover, the local and regional extent DSM are being limited to only a few parts of the country. There still remains 50% of the states and Union Territories of the country where no DSM studies have been performed so far except the national and global level interventions. Among the target variables, soil carbon related attributes have been predicted most frequently, whereas soil classes have been rarely predicted. Environmental covariates representing organism (O) and relief (R) have been widely included for DSM, whereas the use of other covariates has been limited. Among different machine learning (ML) algorithms, regression kriging has been adopted most frequently followed by random forest and quantile regression forest. Most articles have adopted data splitting (76%) as the model and map evaluation approach, whereas independent validation has been limited to only 5% of the articles. Only 34% of the articles have presented the uncertainty maps. Major research gaps identified by this review include lack of standardized digital soil databases, poor sampling density, coarse resolution, limited use of environmental covariates, insufficient comparative studies among ML algorithms, inadequate independent validation, and undersupply of uncertainty maps. Key evidences from this review will be helpful for improving future DSM activities by scientists and practitioners involved with DSM in India and abroad.
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    Identifying opportunities to improve management of water stress in banana production
    (Elsevier, 2020-09-19) Panigrahi, Niranjan; Thompson, Andrew J.; Zubelzu, Sergio; Knox, Jerry W.
    Banana (Musa spp.) is one of the most valuable global agricultural commodities, with commercial plantations responsible for supplying nearly 15 % of total global banana production. These plantations are underpinned by major infrastructural investments and a high dependence on fertilizer, pesticide and irrigation inputs. In contrast, smallholders and subsistence farmers often cultivate bananas for local markets with minimal inputs. Water stress due to increasing rainfall variability and competition for water resources are emerging as major production constraints for both commercial and smallholder production. Water stress-induced yield losses of up to 65 % have been reported due to loss in bunch weight even in moderate to low rainfall areas. Thus, investments in more efficient irrigation systems and water-saving technologies are being widely promoted to increase water productivity through improved scheduling to reduce drainage and runoff losses. This paper synthesises scientific and industry evidence on crop growth and development including root and shoot development, plant water relations, and yield response to water. It also critiques the importance of irrigation scheduling for maximising irrigation efficiency. New evidence to support the synchronization of irrigation with crop water demand to reduce environmental impacts is provided. High variability in crop water demand (1200–2690 mm per year) was found to be linked to cultivar choice, crop development cycle, and fluctuating conditions in environmental and edaphic factors. The findings confirm that irrigation should be scheduled at moderate levels of soil water deficit sufficient to promote deep and extensive rooting while maintaining banana quality. Management practices are recommended to mitigate water stress without compromising yield under limited rainfall and irrigation conditions. The ratooning cycle of banana also affects rooting activity and crop coefficients (Kc) compared to other annual crops. These aspects need to be considered when improving irrigation and crop modelling for banana. The findings provide valuable new insights and evidence for scientists and practitioners involved in banana research and management.
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    Modelling water fluxes to improve banana irrigation scheduling and management in Magdalena, Colombia
    (Springer, 2022-08-25) Zubelzu, Sergio; Panigrahi, Niranjan; Thompson, Andrew J.; Knox, Jerry W.
    In this paper, an irrigation scheduling model for banana (Musa sp.) was developed to simulate crop growth and water fluxes under typical commercial plantation conditions. Whilst generic models exist for scheduling irrigation for many crops, their suitability for bananas are limited because of the asynchronous nature of crop growth. Individual fields on banana plantations typically contain trees at varying stages in their development cycle, so it is important for scheduling to account for this heterogeneity in simulating crop production. A crop modelling approach was developed using field data from Magdalena, an economically important region of banana production in Colombia. Following model development and calibration, irrigation water demand was estimated and weekly irrigation scheduling advice then transmitted by SMS to individual farmers in the region. The model also takes into account farmer feedback on actual irrigation practices to compare against estimated irrigation demands and to train model performance. Despite good model calibration, analysis of irrigation practices from farmer feedback showed only moderate to poor correlation between actual irrigation applications and the scheduling guidance. This implies a reluctance of farmers to change long-established traditional irrigation management practices, despite awareness of the impacts of systematic over-irrigation on yields and increased nutrient leaching risks. Significant ongoing research efforts will be needed to support improved knowledge and practical water management for key plantation crops.
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    Rainwater Harvesting (RWH) tool for soft fruit production in polytunnels
    (Cranfield University, 2021-07-30 16:47) Knox, Jerry; Panigrahi, Niranjan; Hess, Tim; Holman, Ian
    Working with growers and key stakeholders in Kent, and with funding from Kent County Council, researchers from Cranfield University have designed and developed a simple Microsoft Excel-based tool to help soft fruit growers and farm business advisors evaluate the hydrological performance and water storage effectiveness of RWH systems to support decision making regarding their viability for protected cropping. The tool was designed to be simple and intuitive to use and requires only a very limited set of farm level input data. The outputs include the MS excel RWH tool and a Guidance manual for end users.
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    Scaling up indigenous rainwater harvesting: a preliminary assessment in Rajasthan, India
    (MDPI, 2023-05-27) Rawat, Akanksha; Panigrahi, Niranjan; Yadav, Basant; Jadav, Kartik; Mohanty, Mohit Prakash; Khouakhi, Abdou; Knox, Jerry W.
    Rainwater harvesting (RWH) has the potential to enhance the sustainability of ground and surface water to meet increasing water demands and constrained supplies, even under a changing climate. Since arid and semi-arid regions frequently experience highly variable spatiotemporal rainfall patterns, rural communities have developed indigenous RWH techniques to capture and store rainwater for multiple uses. However, selecting appropriate sites for RWH, especially across large regions, remains challenging since the data required to evaluate suitability using critical criteria are often lacking. This study aimed to identify the essential criteria and develop a methodology to select potential RWH sites in Rajasthan (India). We combined GIS modeling (multicriteria decision analysis) with applied remote sensing techniques as it has the potential to assess land suitability for RWH. As assessment criteria, spatial datasets relating to land use/cover, rainfall, slope, soil texture, NDVI, and drainage density were considered. Later, weights were assigned to each criterion based on their relative importance to the RWH system, evidence from published literature, local expert advice, and field visits. GIS analyses were used to create RWH suitability maps (high, moderate, and unsuited maps). The sensitivity analysis was also carried out for identified weights to check the inadequacy and inconsistency among preferences. It was estimated that 3.6%, 8.2%, and 27.3% of the study area were highly, moderately, and unsuitable, respectively, for Chauka implementation. Further, sensitivity analysis results show that LULC is highly sensitive and NDVI is the least sensitive parameter in the selected study region, which suggests that changing the weight of these parameters is more likely to decide the outcome. Overall, this study shows the applicability of the GIS-based MCDA approach for up-scaling the traditional RWH systems and its suitability in other regions with similar field conditions, where RWH offers the potential to increase water resource availability and reliability to support rural communities and livelihoods.