Field experiments were carried out in order to investigate if brown manuring (BM) usingSesbaniaplants can be used to control weeds in maize, especiallyCyperus rotundus(Experiment I), and further to optimize the BM technology through appropriateSesbaniaseed rate (S), 2,4-D application time (T) and dose (D) (Experiment II). Each BM treatment received a pre-emergence application of pendimethalin 1.0 kg a.i./ha. Experiment I showed that the BM practice using 15 kg/haSesbaniaseed and 2,4-D 0.50 kg a.i./ha applied at 25 DAS led to better control of weeds, especiallyC. rotundusand higher maize grain yield. Further optimization studies (Experiment II) indicated that among the factors S, T and D, the BM combination S~25 kg/ha, D~0.50 kg a.i./ha and T~25 DAS (i.e. S₂₅T₂₅D_0.50) resulted in lowest weed density (3.1/m²) and dry weight (3.8 g/m²) and highest weed control index (89.2%) at 60 days after sowing (DAS) which was at par with another BM practice S₁₅T₂₅D_0.50. However, the later BM combination led to significantly higher maize productivity (5.25 t/ha) and profitability (net returns (NR) $878/ha), which were 103 and 280% higher, respectively, than the weedy check (WC). TheSesbaniaseed rate S~15 kg/ha gave 7% higher maize grain yield and 12% higher NR than its corresponding level S~25 kg/ha. Therefore,SesbaniaBM with 15 kg seeds/ha and 2,4-D at 0.50 kg a.i/ha applied at 25 DAS can be recommended for effective and eco-friendly weed management in maize, which would provide higher maize grain yield and enhance farmers' profitability.

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.

Climate variability increasingly threatens agriculture in monsoon-dominated regions, where crop performance depends on both rainfall amount and temporal distribution. This study assessed 26 years (2000–2025) rainfall variability, trends, change points and rainfall state persistence across six agro-climatic zones of West Bengal, India, considering three agricultural seasons: pre-kharif (March–June), kharif (July–October), and rabi (November–February). Seasonal and annual rainfall were analysed using Mann–Kendall and Hamed Rao Kendall tests, Sen’s slope, Pettitt change-point analysis, Innovative Trend Analysis (ITA), rainfall anomalies and LOESS, while rainfall-state dynamics were evaluated using a four-state Markov-chain framework. The Old Alluvial Zone showed the strongest drying signal, with significant declines in pre-kharif, kharif and annual rainfall. ITA confirmed significant declines in kharif and annual rainfall. The Hill Zone exhibited contrasting seasonal trajectories, with increasing kharif rainfall (τ = 0.300; 17.72 mm yr⁻¹; p = 0.038) but declining rabi rainfall (τ = −0.327; −2.17 mm yr⁻¹; p = 0.024), highlighting pronounced seasonal divergence. Markov analysis showed dominant no-rain conditions during rabi, with steady-state probabilities of 0.84–0.92, while kharif heavy-rain probabilities reached 0.33 and 0.30 in the Hill and Terai (Terai-Teesta Alluvial Zone) zones, respectively. The integrated assessment helps identify zones where declining seasonal rainfall, persistent dry conditions or greater monsoon rainfall may have increasing relevance for crop and water management. These rainfall-based insights can support location-specific consideration of supplemental irrigation, moisture conservation, rainwater harvesting, drainage and contingency crop planning.