Erosion by water reduces soil quality and hence crop yield. Understanding the response of crop yields to soil erosion is vital in assessing agriculture’s vulnerability to erosion. However, these effects are difficult to quantify. The study presents a quantitative relationship between soil erosion and soil quality and productivity of rainfed wheat (Triticum aestivum) by comparing field plots with different degrees of erosion in some sub-tropical alfisols in the Doon Valley region of India. By comparing the topsoil depth with the reference site as the control, erosion severity was classified into different phases such as slight, moderate, severe, and very severe. A quantitative, weighted additive model was used to evaluate soil quality for different phases of erosion using soil clay content, water holding capacity, soil aggregate, soil organic carbon, pH, CEC, total N, available P, and available K. The synthesis of long-term experimental data revealed that the mean soil erosion rate varied from 5.5 Mg ha⁻¹ yr⁻¹ in slightly eroded plots to 33.4 Mg ha⁻¹ yr⁻¹ in very severely eroded plots. Compared with the reference forest, the soil organic carbon (SOC) declined by 81.4% and water holding capacity by 31% in severely eroded soils. A substantial loss of total N, extractable P, and available K was also observed. Water stable aggregates (WSA) decreased from 86% to 12.6%, and the cation exchange capacity (CEC) from 25 to 12.6 c mol⁽⁺⁾ kg⁻¹. The soil quality index was 0.7 in slightly eroded compared with 0.4 in severely eroded soil. Similarly, the sustainable yield index for wheat was 0.9 and 0.6 for slightly and severely eroded soils, respectively. Thus, there is a strong need to identify land management systems that reduce erosion risks, restore eroded soils, and enhance soil quality.

Globally, climate change and extreme weather events are occurring more frequently, impacting water resources and farming systems. Therefore, spatio-temporal analysis of long-term rainfall is much needed to understand the variability of rainfall occurrence. The present study attempts to analyse spatio-temporal rainfall change scenarios in the 20th century (1901–2000) over Bundelkhand, one of the drought hit regions of India. Analysis shows that major rainfall contributed from 3 months, i.e. July, August and September. However, decreasing rainfall trend during monsoon season and increasing trend during pre-monsoon and post-monsoon season indicates the scenario of shifting rainfall from normal occurrence. This result is supported by decreasing seasonality index (SI) (1.94–1.1). The northern part of the region witnessed positive annual and monsoon rainfall trend but the southern part observed negative trend. Pettitt's test indicates 1983 is the most probable change year with 0.95 probability, after which annual and monsoon rainfall was found decreasing. Wavelet analysis revealed that extreme rainfall occurrence was observed with a periodicity of 2–16 years. However, Bundelkhand rainfall pattern depicts declining rainfall trends, heading towards a further drier phase with more irregular rainfall in the coming era. The study will serve as future reference in similar regions in the world to determine vital weather patterns which may impact farming systems.

Despite the growing importance of soil and water conservation and watershed development projects as an approach to rural development and natural resource management, there has been relatively little research on devising site-specific best management practice (BMP) to check the soil erosion losses within permissible limits, especially in hilly regions. For a sustainable watershed management programme and implementation, site specific BMPs assume importance and hold the promise of making conservation planning and watershed management simpler and more effective. The study was attempted to develop a methodology to obtain BMPs, aiming to reduce the erosion losses in erosion risk areas of the northwestern Himalayas by employing Analytic Hierarchy Process (AHP). The AHP technique was employed to prioritise the potential technologies and select the BMP suitable for a particular land area. The prioritization of technologies was performed with four criteria viz. soil erosion resistance, cost, benefit, maintenance and environment friendliness of conservation measures. The soil erosion scenario of the study area located in the northwestern Himalayas was generated with each selected measure in a SWAT model using DEM, land use maps, a soil map and climate data of the study area. Then, the resultant erosion scenario of the conservation measures was compared and used for the AHP analysis. However, other criteria were assessed based on the judgement of a group of experts as well as farmers. In this study, four conservation measures, viz. Bench Terraces (BT), Vegetative Barrier (VB), Contour Farming (CF) and Zero Tillage + Live Mulch (ZL), were considered for BMP selection. Three scenarios, viz. experts’ judgement, farmers’ opinions and combined expert and farmer opinion, were analyzed to uncover the BMP for the different zones. The result revealed that experts and farmers unanimously preferred ZL as a BMP because of its low-cost implementation value and lower maintenance requirement while significantly controlling the erosion level as well as being environment friendly. The BT was the second most preferred technology for the study area. However, BMP was recommended for different zones having high to very severe erosion (soil loss > 10 t/ha/yr). Therefore, ZL was recommended for the areas with low altitude, whereas BT was recommended for the areas having high slopes because of its high capability for erosion control in the high slopping area. The methodology will act as a useful strategy for decision makers to prioritize the technology and recommend the best management for any region after considering suitable criteria. Future work may consider more criteria for inclusion to thus recommend the technology for a region in a more realistic way.

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability.