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Biochar is increasingly gaining popularity due to its extensive recommendation as a potential solution for addressing the concerns of food security and climate change in agroecosystems, with biochar application for increased carbon sequestration, enhanced soil fertility, improved soil health, and increased crop yield and quality. There have been multiple studies on crop yield utilizing various biochar types and application amounts; however, none have focused on the influence of diverse biochar types at various pyrolysis temperatures with different application amounts and the integration of fertilizer regimes in maize crops. Therefore, a two-year factorial field experiment was designed in a temperate Himalayan region of India (THRI) to evaluate the residual effect of different biochar on maize yield under different pyrolysis temperatures, various application rates and fertilizer regimes. The study included three factors viz., amendment type (factor 1), rate of application (factor 2) and fertilizer regime (factor 3). Amendment type included 7 treatments: No biochar- control (A1), apple biochar @ 400 °C pyrolysis temperature (A2), apple biochar @ 600 °C pyrolysis temperature (A3), apple residue biomass (A4), dal weed biochar @ 400 °C pyrolysis temperature (A5), dal weed biochar @ 600 °C pyrolysis temperatures (A6), and dal weed residue biomass (A7). The rate of application included 3 levels: Low (L- 1 t ha−1), medium (M- 2 t ha−1), and high (H- 3 t ha−1). At the same time, the fertilizer regimes included 2 treatments: No fertilizer (N) and recommended dose of fertilizer (F). The results revealed that among the various amendment type, rate of application and fertilizer regimes, the A3 amendment, H rate of application and F fertilizer regime gave the best maize growth and productivity outcome. Results revealed that among the different pyrolyzed residues used, the A3 amendment had the highest plant height (293.87 cm), most kernels cob−1 (535.75), highest soil plant analysis development (SPAD) value (58.10), greatest cob length (27.36 cm), maximum cob girth (18.18 cm), highest grain cob yield (1.40 Mg ha−1), highest grain yield (4.78 Mg ha−1), higher test weight (305.42 gm), and highest stover yield (2.50 Mg ha−1). The maximum dry weight in maize and the number of cobs plant−1 were recorded with amendments A4 (14.11 Mg ha−1) and A6 (1.77), respectively. The comparatively 2nd year of biochar application than the 1st year, the H level of the rate of application than the L rate and the application and integration of the recommended dose of fertilizer in maize results in significantly higher values of growth and productivity in maize. Overall, these findings suggest that the apple biochar @ 600 °C pyrolysis temperature (A3) at a high application rate with the addition of the recommended dose of fertilizer is the optimal biochar for enhancing the growth and productivity of maize in the THRI. [ABSTRACT FROM AUTHOR]

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum ( T m a x ) and minimum ( T m i n ) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead T m a x and T m i n forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for T m a x and T m i n across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both T m a x and T m i n forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations. [ABSTRACT FROM AUTHOR]

Drought stress has a significant challenge to crop growth, reproduction, and yield in arid regions. Microbes associated with desert plants offer a promising solution by enhancing moisture and nutrient uptake, mitigating water stress, and ultimately boosting crop growth and yield. In this study, the bacteria isolated from the rhizosphere of native plant Aerva javanica, found in the Cholistan desert, were assessed for their effectiveness to promote chickpea seedlings growth. A total of 21 drought-tolerant rhizobacterial strains were isolated and characterized, with a focus on their ability to produce exopolysaccharide (EPS) and 1-aminocyclopropane-1-carboxylic acid-deaminase (ACC-deaminase) under water-deficit conditions. These conditions were simulated using polyethylene glycol (PEG) 6000 in the growth medium. Notably, strains CD23 and CD21 exhibited the highest EPS production ability, yielding 10.9 and 10.4 mg L-1 of EPS, respectively, under PEG-induced drought stress however, a minute reduction in EPS production was observed under normal conditions. Similarly, ACC-deaminase activity was significant, measuring 3.2 and 3.2 µM mg-1 protein for strains CD23 and CD21, respectively. Further assessments were conducted to evaluate the drought tolerance of these strains at various PEG-6000 levels in jar trials. The strains CD23 and CD21 demonstrated promising results in enhancing chickpea growth. Subsequently, through partial gene sequencing of the 16S rRNA, these strains were identified as Acinetobacter sp. strain CD21 (accession number: MW829536) and Klebsiella sp. strain CD23 (accession number MW829537). The production of ACC-deaminase and EPS by these desert rhizobacteria may serve as crucial indicator for the development of inoculants that can promote chickpea growth under water-deficit conditions. [ABSTRACT FROM AUTHOR]

Crop residue management and water saving are the two major issues for the sustainability of the rice-wheat cropping system. Therefore, a two-year field experiment was conducted in a split-plot design to study the combined effect of three rice residues (residue incorporation (RI), residue standing (RS) and residue removal (RR) and two wheat residue incorporation (WI) and residue removal (WR) management in main plots and two irrigation regimes, i.e., flood (F) and surface drip (SD) in subplots on the growth and water productivity of direct seeded rice (DSR). During both years, RI-WI resulted in significantly higher plant height (PH), leaf area index (LAI) and dry matter accumulation (DMA) than in other residue management treatments. Drip irrigation significantly increased PH, LAI, DMA grain yield, straw, and biological yield, along with a 9.6% irrigation water savings over flood irrigation. During both years, grain yield, straw and biological yield of DSR were significantly higher in RI-WI than in RR-WR and RR-WI. RI-WI had significantly greater apparent water productivity (AWP) and actual water productivity (RWP) of DSR. Drip irrigation had significantly higher AWP and RWP during both years than flood irrigation except RWP during 2017. Transpiration efficiency (TE) in rice residue incorporation was significantly higher than in rice residue standing and removal. During both years, the TE of drip irrigation was also significantly higher than flood irrigation. So, incorporating rice and wheat residues along with drip irrigation improves crop growth and water productivity. [ABSTRACT FROM AUTHOR]