Agricultural Resource Management Research Articles

The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland

The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland.The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research.

Estimation of Evapotranspiration and Soil Water Content at a Regional Scale Using Remote Sensing Data

The timely and accurate estimation of soil water content (SWC) and evapotranspiration (ET) is of great significance in drought estimation, irrigation management, and water resources comprehensive utilization. The unsupervised classification was used to identify the crops in the region. Based on MOD16A2 and the meteorological data, a SEBS model was used to estimate the ET in the Jiefangzha Irrigation Field from 2011 to 2015. Based on the crop water stress index (CWSI), the SWC in 2014 was retrieved and verified with the measured SWC on different underlying surfaces (sunflower, corn, wheat, and pepper). The results showed that: (1) The positional accuracy of maize, sunflower, wheat, and pepper are 0.81, 0.80, 0.90, and 0.82, respectively; (2) The annual ET from 2011 to 2015 presented well the spatial distribution of the ET within the field; (3) The validation results of the estimated SWC on the underlying surface of wheat and sunflower showed a good robustness, the R2 was 0.748 and 0.357, respectively, the RMSE was 2.61% and 2.309%, respectively, and the MAE was 2.249% and 1.975%, respectively. However, for maize and pepper with more irrigation times, the SWC estimation results, based on the CWSI were poor, indicating that the method was more sensitive to soil drought and suitable for the crop SWC estimation with less irrigation and drought tolerance. The results can provide a reference for the agricultural water resources management and the irrigation forecast at a regional scale.

Agricultural resource and risk management with multiperiod stochastics: A case of the mixed crop-livestock production system in the drylands of Jordan

Generally, agricultural production involves several challenges. In the drylands, it is further complicated by weather-related risks and resource degradation. In this paper, we present a case study of the mixed crop-livestock production system in Jordan. To better capture the nature of response farming in the drylands, we develop a methodology for using crop simulation models to directly generate data for optimizing production practices of an integrated crop-livestock producing household in a dynamic stochastic context. The approach optimizes producer’s adaptations to random events, such as weather, which are realized throughout the planning horizon. To ensure the sustainability of the optimized production decisions, long-term valuations of end of horizon soil attributes are included in the objective function. This approach endogenizes the tradeoff between short-and long-run productivity. Model results show that due to the limited natural resource endowments and financial liquidity constraints of the typical farm households in the study area, we find these households have limited options. To optimally respond to weather conditions during the production season, better manage risk, and achieve improvements in soil attributes, a typical household would need larger farm size, larger flock, and better financial liquidity than it currently commands. Like all such models, the farm household model used in this paper is not suitable for drawing policy implications. Therefore, targeted analysis using appropriate sectoral or economy-wide models will be needed in the future to identify and test the efficacy of different policy and institutional interventions including land consolidation, establishment of producer and marketing cooperatives, access to financial services including agricultural credit, and crop insurance in expanding the resource base of farmers—thereby positioning them for higher earnings, ensuring soil conservation, and enhancing the sustainability of the production system.

A comprehensive assessment of precipitation products: Temporal and spatial analyses over terrestrial biomes in Northeastern Brazil

Precise rainfall estimations at spatio-temporal resolution are vital for numerous applications, including agricultural monitoring, hydrometeorology, and water resources management. Northeast Brazil (NEB) is vulnerable to extreme interannual climate variability and precipitation is closely linked to the livelihoods of its citizens and the regional health of the economy. Satellite rain data is an alternative source of information for regions such as NEB. Thus, this study aimed to evaluate the performance of monthly rainfall estimations derived of latest versions from five precipitation products: (i) CHIRPS, (ii) ERA5-Land, (iii) TerraClimate, (iv) TRMM, and (v) IMERG in the NEB and comparing them to the observed precipitation for the period 2001–2019. The results showed that the satellite-based products were more regularly correlated with the observed precipitation than the model-based products. CHIRPS, IMERG and, TRMM showed an average Pearson's correlation coefficient (r) value above 0.93. All products tended to present lower relative error in the rainy months; TerraClimate gave the highest average value of root-mean-square error (RMSE) for NEB (41.61 mm/month). The best global performance was observed in the Amazon biome (r ∼ 0.90), and the poorest in the Atlantic Forest biome, which tended to underestimate the precipitation. In spite of the results in the Atlantic Forest, this study demonstrated that products of precipitation can be a useful complement to rainfall data for the NEB. The comprehensive assessment of precipitation products reported here will serve as a reference for managers and researchers to select the ideal product for its intended application and associated uncertainties.

Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia

Reliable projections of evapotranspiration (ET) are important for agricultural and water resources development, planning, and management. However, ET projections using well established empirical models suffer from uncertainty due to their dependency on many climatic variables. This study aimed to develop temperature-based empirical ET models using Gene Expression Programming (GEP) for the reliable estimation and projection of ET in peninsular Malaysia within the context of global warming. The efficiency of the GEP-generated equation was compared to the existing methods. Finally, the GEP ET formulas were used to project ET from the downscaled and projected temperature of nine global climate models (GCMs) for four Representative Concentration Pathways (RCPs), namely, RCP 2.6, 4.5, 6.0, and 8.5, at ten locations of peninsular Malaysia. The results revealed improved performance of GEP models in all standard statistics. Downscaled temperatures revealed a rise in minimum and maximum temperatures in the range of 2.47–3.30 °C and 2.79–3.24 °C, respectively, during 2010–2099. The ET projections in peninsular Malaysia showed changes from −4.35 to 7.06% for RCP2.6, −1.99 to 16.76% for RCP4.5, −1.66 to 22.14% for RCP6.0 and −0.91 to 39.7% for RCP8.5 during 2010−2099. A higher rise in ET was projected over the northern peninsula than in the other parts.

Spatial distribution of the trends in potential evapotranspiration and its influencing climatic factors in Iraq

Understanding the spatial variations in potential evapotranspiration (PET) and its influencing climatic variables is essential for sustainable agriculture and water resources management. However, little published research has investigated the alternation of PET due to climate change in the case of Iraq. The objective of the present study was to analyze the spatial trends in annual and seasonal PET in Iraq. Accordingly, the latest global ERA5-Land dataset of the European Centre for Medium-Range Weather Forecasts for 1981–2021 was employed. The PET was estimated using the FAO-Penman–Monteith method. The modified Mann–Kendall statistical test was applied to evaluate the significance of the trends in PET, which can separate unidirectional trends caused by climate change from the natural variability of climate. The attained results indicate that: (1) Over the past four decades, the annual and seasonal PET witnessed a significant increasing trend in almost all of Iraq, except for the alluvial plain in the eastern and southeastern parts. (2) The increasing trend in PET confirmed the patterns of the trend significance, with the highest increase of 0.28–0.65 mm/decade in southwest Iraq. (3) Summer had the highest upward trend of 0.35–0.65 mm/decade, followed by spring, autumn, and winter. (4) The air temperature was the predominant driving factor of rising PET, showing a positive correlation ranging from 0.77 to 0.88 and a contribution of 26 to 94%, mainly in the south, central, and northwest regions. The reverse contribution of wind speed and surface pressure to PET, particularly in the southeast and southwest, remains offset by the influence of air temperature and net solar radiation. Overall, the PET has risen drastically due to global climate change, indicating the potential for increased atmospheric water demand in the region.

The economic-administrative role of geographic information systems in rural tourism and exhaustive local community development in African marginalized communities

PurposeThe purpose of this study was to examine the latent part of geographic information systems in inclusive sustainable rural tourism, community-based natural resource management (CBNRM) and community development and empowerment in Southern Africa, Africa generally and many rural areas elsewhere worldwide.Design/methodology/approachThe viewpoint utilizes literature and document reviews to assess African and worldwide agricultural, environmental and tourism resources management scenarios. It thus liaises with CBNRM and geographic information systems in sustainable tourism and local community development applications.FindingsThis review viewpoint uncovers a better potential synergetic relationship between tourism and rural (agricultural) activities that geographic information systems along a concept of CBNRM can amplify. Hence, it has poised a need for a decent and integrated tourism strategy to develop and empower the pertinent communities in many rural and marginalized areas within the continent.Originality/valueMany rural communities in Southern Africa and Africa broadly dwell in low-income areas. Such milieus are rich in natural biodiversity, including tourism destination areas. Geographic information systems, sustainable tourism and CBNRM can form a gestalt of local community development projects within their environs.

A Long Way toward Climate Smart Agriculture: The Importance of Addressing Gender Inequity in the Agricultural Sector of Guatemala

In the context of climate change’s detrimental effects on agricultural production and food security, climate-smart agriculture (CSA) strategies constitute a promising approach to reduce vulnerability and boost adaptation capacity and resilience within farmers. However, CSA strategies should address gender dynamics to reach their full potential. This study analyzed the barriers and opportunities for the implementation of gender-sensitive CSA strategies in rural Guatemala, a low-latitude country with a high gender gap index, through the perceptions of agricultural extensionists. For this purpose, we conducted an online survey among Guatemalan agricultural extensionists who attended a series of Climate Services for Agriculture workshops between May and July 2021 and analyzed the results using a qualitative approach. Results suggest that women in rural Guatemala are frequently excluded from climate information access, agricultural training, and decision-making spaces in which agricultural resource management strategies are defined. We argue that this exclusion represents a barrier to the improvement in adaptation capacity and resilience and that gender inequity should be addressed to implement successful gender-sensitive CSA approaches. Generating gender-sensitive indicators and training extensionists against gender bias could be a starting point, but further research is necessary to understand gender dynamics in rural Guatemala.

Management and Utilization of Plant Genetic Resources for a Sustainable Agriculture.

Despite the dramatic increase in food production thanks to the Green Revolution, hunger is increasing among human populations around the world, affecting one in nine people. The negative environmental and social consequences of industrial monocrop agriculture is becoming evident, particularly in the contexts of greenhouse gas emissions and the increased frequency and impact of zoonotic disease emergence, including the ongoing COVID-19 pandemic. Human activity has altered 70–75% of the ice-free Earth’s surface, squeezing nature and wildlife into a corner. To prevent, halt, and reverse the degradation of ecosystems worldwide, the UN has launched a Decade of Ecosystem Restoration. In this context, this review describes the origin and diversity of cultivated species, the impact of modern agriculture and other human activities on plant genetic resources, and approaches to conserve and use them to increase food diversity and production with specific examples of the use of crop wild relatives for breeding climate-resilient cultivars that require less chemical and mechanical input. The need to better coordinate in situ conservation efforts with increased funding has been highlighted. We emphasise the need to strengthen the genebank infrastructure, enabling the use of modern biotechnological tools to help in genotyping and characterising accessions plus advanced ex situ conservation methods, identifying gaps in collections, developing core collections, and linking data with international databases. Crop and variety diversification and minimising tillage and other field practices through the development and introduction of herbaceous perennial crops is proposed as an alternative regenerative food system for higher carbon sequestration, sustaining economic benefits for growers, whilst also providing social and environmental benefits.

A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product

A reliable thermal-derived method for spatially and temporally continuous latent heat flux (LE) estimates is vital for agricultural water resource management. In this study, we proposed a novel three-source LE model (TSLEM) derived by all-weather thermal infrared (TIR) land surface temperature (LST) to generate all-sky daily LE in mainland China. In this approach, LE was partitioned into the energy fluxes from canopy transpiration, soil evaporation, and interception water evaporation, respectively. Importantly, a new strategy was used to decompose radiational temperature into soil temperature (Ts), canopy temperature (Tc) and interception water temperature (Ti). Then soil evaporation was estimated by Penman-Monteith (PM) equation that parameterizes a soil resistance as a function of the normalized difference temperature index (NDTI) derived from Ts. A simplified MOD16 algorithm framework was used to estimate canopy transpiration and a Priestley-Taylor (PT) model to estimate interception evaporation. The proposed method was validated using 26 eddy covariance (EC) tower sites in mainland China across various vegetation types and applied to generate spatial continuous daily LE in mainland China. The results show that TSLEM accurately yielded daily LE with an average coefficient of determination (R2) of 0.53 (p<0.01) and root-mean-square-error (RMSE) of 27.37 W/m2, indicating TSLEM is a promising method for generating daily LE using all-weather LST at the regional scale.

Water balance calculation capability of hydrological models

Currently, in the world, there are many different hydrological models built and developed to solve problems related to the hydrological cycle. Each model has its specific mathematical foundations to describe physical processes in nature. Therefore, each model has its various characteristics: setting up the model, input data requirements, model calibration and verification, and output results. Water balance is still playing an important role in the effective management and use of water resources for agriculture. Based on the results of the hydrological parameter’s calculation, the water balance of the study basin can be calculated by the user or by the separated module of each model. Each hydrological models have its advantages and disadvantages. However, it is impossible to simulate hydrological processes and water balance completely accurately in nature. Still, simulation results can give us a view of the changing trend of hydrological components and the water balance. Model developers are gradually completing the shortcomings and improving the efficiency and accuracy so that the model can simulate reality with the highest accuracy. This paper sets out to review the fifteen hydrological models currently widely used in the world. Within the frame of the present study, some models are only briefly introduced; the rest are considered in more detail, from more aspects, from specific examples so that readers can decide for themselves which model is suitable for their study area and simulation needs, especially in the identity of the complex and unpredictable impacts of climate change on the agricultural sector.

Current Conditions and Projected Changes in Crop Water Demand, Irrigation Requirement, and Water Availability over West Africa

Climate variability and change greatly affect agricultural and water resource management over West Africa. This paper presents the current characteristics and projected change in regional crop water demand (CWD), irrigation requirement (IR), and water availability (WA) over West Africa. Observed and simulated daily rainfall, minimum temperature, maximum temperature, and evapotranspiration are used to derive the above agro-meteorological and hydrological variables. For future periods, high-resolution climate data from three regional climate models under two different scenarios, i.e., representative concentration pathway (RCP) 4.5 and 8.5, are considered. Evaluation of the characteristics of present-day CWD, IR, and WA indicated that the ensemble mean of the model-derived outputs reproduced the prevailing spatial patterns of CWD and IR. Moreover, the wetter part of the domain, especially along the southern coast, was correctly delineated from the drier northern regions, despite having biases. The ensemble model also simulated the annual cycle of water supply and the bimodal pattern of the water demand curves correctly. In terms of future projections, the outcomes from the study suggest an average increase in the CWD by up to 0.808 mm/day and IR by 1.244 mm/day towards the end of the twenty-first century, compared to the baseline period. The hot-spot areas, where there is higher projected increment in CWD and IR, are over Senegal, Southern Mali, and Western Burkina Faso. In most cases, WA is projected to decrease towards the end of the twenty-first century by −0.418 mm/day. The largest decline in WA is found to be over Guinea and most of the eastern parts of West Africa. Despite the current under-utilization of the existing groundwater resources, the threat of global warming in reducing future WA and increasing CWD suggested caution on the scale of irrigation schemes and management strategies. The outcomes from the study could be a crucial input for the agricultural and water managers for introducing effective measures to ensure sustainability of irrigated farm lands.

Sustainable water resource management in steep-slope agriculture

Steep-slope agricultural landscapes are under threat due to climate change. On the one hand, the growing frequency of extreme high-intensity rainfall events concentrated in both temporal and spatial scales are causing flash floods or slope failure risk scenarios. On the other hand, future climate projections indicate a significant expansion of arid zones in the steep slope agricultural system. There is evidence that these landscapes face a high risk of growing water scarcity. Considering their unique role in crop production, ecosystem diversity, and crop production, ecosystem diversity, and cultural heritage, understanding sustainable water resource management for mitigating climate change-induced drought has never been more urgent than today. In these landscapes, unique indigenous knowledge of water conservation is adopted to manage water resources improving their resilience optimally. It is, therefore, necessary to promote water storage to mitigate floods or increase the resilience to prolonged drought (creating at the same time favourable conditions for biodiversity). Modern technological advances (e.g., high-resolution remote sensing and GIS-based modelling) are crucial in supporting these activities and understanding earth’s surface processes.

Stackelberg equilibrium water allocation patterns in shallow groundwater area under uncertainties

The hierarchical agricultural water resource management problem in shallow groundwater area is expected to be solved through establishing a practical and feasible mathematical model. This study proposes a multi-level programming model with considerations of process-based crops growth model and uncertainties. The model is solved by a fuzzy approach that ensures the decision-making proceeds from top to bottom sequentially and the leader supervises the solution search direction. The leeway given by each level decision maker expands the decision-making space and lets Stackelberg equilibrium solution more satisfactory. The model is applied in Hetao Irrigation District in China. After optimization, the Stackelberg equilibrium water allocation pattern and satisfaction degrees of each level decision makers can be identified. Summarized conclusions are as follows: (1) under uncertainties of available water, the water diversion is allocated in a similar pattern, while the groundwater allocation has larger variabilities; (2) when available water decreases, local farmers can improve crop production through modifying or optimizing irrigation water amount in each growth period; (3) the combined use of water diversion and groundwater is expected to become an effective approach for achieving a win–win situation between government managers and local farmers. The proposed model performs well in generating Stackelberg equilibrium solutions satisfied by multi-level decision makers. It can be extended to provide decision makers a more complete decision support system and also has potential to be applied in other similar areas.

Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration

Sub-seasonal forecasting (SSF) is the prediction of key climate variables such as temperature and precipitation on the 2-week to 2-month time horizon. Skillful SSF would have substantial societal value in areas such as agricultural productivity, hydrology and water resource management, and emergency planning for extreme events such as droughts and wildfires. Despite its societal importance, SSF has stayed a challenging problem compared to both short-term weather forecasting and long-term seasonal forecasting. Recent studies have shown the potential of machine learning (ML) models to advance SSF. In this paper, for the first time, we perform a fine-grained comparison of a suite of modern ML models with start-of-the-art physics-based dynamical models from the Subseasonal Experiment (SubX) project for SSF in the western contiguous United States. Additionally, we explore mechanisms to enhance the ML models by using forecasts from dynamical models. Empirical results illustrate that, on average, ML models outperform dynamical models while the ML models tend to generate forecasts with conservative magnitude compared to the SubX models. Further, we illustrate that ML models make forecasting errors under extreme weather conditions, e.g., cold waves due to the polar vortex, highlighting the need for separate models for extreme events. Finally, we show that suitably incorporating dynamical model forecasts as inputs to ML models can substantially improve the forecasting performance of the ML models. The SSF dataset constructed for the work and code for the ML models are released along with the paper for the benefit of the artificial intelligence community.

Quantitative driving analysis of climate on potential evapotranspiration in Loess Plateau incorporating synergistic effects

Potential evapotranspiration (ET0) is an important part of regional hydrological cycle and energy cycle. In the context of global climate change, it is of great significance to reveal the spatiotemporal variation of ET0 and its response to climate change for further understanding the evolution mechanism of water cycle in changing environment. In this study, the Penman-monteith formula was utilized to calculate ET0 in 6 sub-basins of the Loess Plateau (LP) during 1974–2019 using the daily observed data from 84 meteorological stations. The Mann-Kendall trend test and Kriging interpolation were applied to analyze the temporal and spatial characteristics of ET0 on annual and seasonal scales. In addition, the multivariate adaptive regression splines model, sensitivity analysis and contribution rate were used to identify the driving factors of ET0 variations by considering the synergistic effect of multiple factors. Results showed that (1) The ET0 of LP showed an insignificant increase at a rate of 5.448 mm/10a on the annual scale. At the seasonal scale, ET0 increased in spring and winter, but decreased in summer and autumn. Spatially, the annual average, spring, summer and autumn ET0 showed a trend of higher north–south and lower east–west; (2) The annual ET0 of LP is most sensitive to relative humidity (RH). At the seasonal scale, ET0 was most sensitive to sunshine hours(S) in spring and autumn, average temperature (T) in summer and RH in winter; (3) RH is the main influencing factor of ET0 change in the LP. At the seasonal scale, wind speed (WS) and T dominated the ET0 variation in spring and winter, respectively. S dominated the ET0 variation in summer and autumn; (4) The variation trend and driving factors of ET0 in each sub-catchment were different from those in the LP, and there were significant differences among the sub-catchments. This study provides a reference for the sustainable development of vegetation restoration and the management of agricultural water resources in the LP.

Livelihood of the Folk People in the novel Sool

Culture is a kind of practice and social behaviour of a particular group of people in the society meant for organizing their lives. It can be attained through the four virtues that are moral, material, pleasure and austerity which is connected to the integral and external part of human life. Tamil people have strongly rooted in its classical language, culture and tradition for more than two thousand years and it has been successfully reflected in its literature which is renowned worldwide for having a great impact on other countries (foreigners). This code of culture and tradition is based on the integral and intellectual insight of Tamil people, not depend on their economic values, this kind of values are found in the novel ‘Sool’ written by the Sakhithya Akademi Award (2019) winning writer So.Dharman who has traced out from the life of Urulaikkudi people depicting their agricultural practices and water resource management especially on various forms of their worship, festival, medicine, rituals and conventional beliefs. These are closely connected to their daily routine life and this research paper is briefly focused on the rustic lives, practices of Tamil people described by the writer So. Dharman. Thus, culture is so called identity and inheritance of Tamil people in and around the world.

Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer

Reference evapotranspiration (ETo) is one of the most important and influential components in optimizing agricultural water consumption and water resources management. In the present study, an efficient deep learning model, Auto Encode Decoder Bidirectional Long Short-Term Memory (AED-BiLSTM), was applied for the first time in forecasting 1–3 weeks ahead of weekly ETo. Three meteorological stations, including Kermanshah (semi-arid), Nowshahr (very humid), and Yazd (arid), which have different climates, were investigated and evaluated. For this purpose, a statistical period of 20 years (2000 to 2019), of which 15 years (2000–2014) for training and the final five years (2015–2015) for model testing were considered. The generalized regression neural network (GRNN) and extreme gradient boosting (XGBoost) machine learning models were used to compare the performance of the newly developed model. A novel CatBoost-Boruta algorithm was used to determine the critical lags in the forecasting process. The results showed that the newly developed model (AED-BiLSTM) in all three meteorological stations had higher ability and accuracy in forecasting weekly ETo than the GRNN and XGBoost models. The outcomes of testing phase in Kermanshah station ascertained that the AED-BiLSTM in terms of (ETo(t + 1): R (correlation coefficient) = 0.9835, RMSE (root mean square error, mm/week) = 3.4597; ETo(t + 2): R = 0.9805, RMSE = 3.7215; ETo(t + 3): R = 0.9795, RMSE = 3.8857) outperformed the other models. In Nowshahr station AED-BiLSTM model had higher accuracy in terms of (ETo(t + 1): R = 0.9057, RMSE = 4.6786; ETo(t + 2): R = 0.9036, RMSE = 4.7697; ETo(t + 3): R = 0.9024, RMSE = 4.8437). Also, in Yazd station AED-BiLSTM perform better than other models in terms of (ETo(t + 1): R = 0.9827, RMSE = 3.1319; ETo(t + 2): R = 0.9800, RMSE = 3.3998; ETo(t + 3): R = 0.9774, RMSE = 3.5679). The XGBoost and GRNN models performed better after the AED-BiLSTM model, respectively. The effect of climate type on the accuracy of the models showed that the models have higher efficiency and accuracy in arid and semi-arid climates.

Multi-criteria decision making of water resource management problem (in Agriculture field, Purulia district) based on possibility measures under generalized single valued non-linear bipolar neutrosophic environment

Purulia district is a drought-prone area in West Bengal, India. Naturally, the farmers of the Purulia district could not plant the crops due to the absence of water resources. Water resource management (WRM) has the potential mechanism to address the available water resources. This paper developed a multi-criterion water resource management technique for defining the available water resources in the agriculture field. We have introduced a novel ranking method of generalized single-valued non-linear bipolar neutrosophic numbers based on possibility measures. Additionally, we have first defined the possibilistic mean of generalized single-valued non-linear bipolar neutrosophic number. Using the possibilistic mean, we have invented the positive and negative rank expositors (Sμ+,Sμ−) for authenticity, hesitate, and falsity membership function. In the present situation, water resource management in agriculture has become a big problem worldwide. Multi-criteria decision-making technique is necessary for developing this situation. Using the proposed multi-criteria decision-making method, we have solved a real water resource management problem in the Purulia district under GSVnTbN-environment. In this decision-making problem, we have considered the GSVnTbN-environment. Because during the summertime, water demand is very high, and at the end of summer, when monsoon season comes, water demand is deficient in the agriculture field of Purulia district. Sometimes the monsoon season, rainwater does not occur entirely. At that time, monsoon crop planting was tough in the agriculture field. So, this district’s water scarcity nature is a non-linear and uncertain type of nature. Therefore, we proposed an MCDM method for water resource management problems under GSVnTbN-environment.

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need.

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.