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Accurate estimation of pan evaporation (Ep) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling Bat algorithm (Bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006–2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (Tmin, Tmax), relative humidity (RH), wind speed (U), and global solar radiation (Rs), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed Bat-CB model (RMSE = 0.859–2.227 mm·d−1; MAE = 0.540–1.328 mm·d−1; NSE = 0.625–0.894; MAPE = 0.162–0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897–2.754 mm·d−1; MAE = 0.531–1.77 mm·d−1; NSE = 0.147–0.869; MAPE = 0.161–0.421) slightly outperformed RF (RMSE = 1.005–3.604 mm·d−1; MAE = 0.644–2.479 mm·d−1; NSE = −1.242–0.894; MAPE = 0.176–0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of Bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, Bat-CB has high accuracy, robust stability, and huge potential for Ep estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries.

This study presents estimate of the effects of climate variables and CO2 on three major crops namely wheat, rapeseed, and sunflower in EU27 Member States. We also investigated some technical adaptation options which could offset climate change impacts. The time-slices 2000, 2020 and 2030 were chosen to represent the baseline and future climate respectively. Furthermore, two realizations within the A1B emission scenario proposed by the Special Report on Emissions Scenarios (SRES), from the ECHAM5 and HadCM3 GCM, were selected. Time series of 30 years for each GCM and time slice were used as input weather data for simulation. The time series were generated with a stochastic weather generator trained over GCM-RCM time series (downscaled simulations from the ENSEMBLES project which were statistically bias-corrected prior to the use of the weather generator). GCM-RCM simulations differed primarily for rainfall patterns across Europe, whereas the temperature increase was similar in the time horizons considered. Simulations based on the model CropSyst v. 3 were used to estimate crop responses; CropSyst was re-implemented in the modelling framework BioMA. The results presented in this paper refer to abstraction of crop growth with respect to its production system, and consider growth as limited by weather and soil water, and responding to CO2 concentrations; pests, diseases, and nutrients limitations were not accounted for in simulations. The results show primarily that different realization of the emission scenario lead to noticeably different crop performance projections in the same time slice. Simple adaptation techniques such as changing sowing dates and the use of different varieties, the latter in terms of duration of the crop cycle, may be effective in alleviating the adverse effects of climate change in most areas, although response to best adaptation (within the techniques tested) differed across crops. Although a negative impact of climate scenarios is evident in most areas, the combination of rainfall patterns and increased photosynthesis efficiency due to CO2 concentrations showed possible improvements of production patterns in some areas, including Southern Europe. The uncertainty deriving from GCM realizations with respect to rainfall suggests that articulated and detailed testing of adaptation techniques would be redundant. Using ensemble simulation would allow identifying the areas where adaptation, like those simulated, may be run autonomously by farmers, hence not requiring specific intervention in terms of support policies.

Several indices can be used to assess the impact of short-term conservation agriculture strategies on improving soil organic carbon (SOC). To find out how the SOC pools and the carbon lability influence the carbon management index (CMI) in response to different agricultural practices in a warm semiarid region, the carbon lability index (LI) and the carbon pool index (CPI) were measured under the interactive effect of different fertilizer applications and crop residue management (hereafter referred to as “farming strategies”) in combination with four crop rotation systems in Ahvaz, Khuzestan, Iran, over four growing seasons from 2018 to 2020. The farming strategies were as follows: (1) using the standard rate of inorganic fertilizer used in the region and removing crop residues from the soil (SIF_no-CR); (2) applying the standard rate of organic fertilizers used in the region and returning 30% of crop residues to the soil (SOF_30% CR); and (3) integrating the use of inorganic and organic fertilizers and returning 15% of crop residues to the soil (IOF_15% CR). The crop rotation systems were fallow-wheat (F-W), corn-wheat (C-W), sesame-wheat (S-W), and mung bean-wheat (B-W). No statistically significant difference was observed between the different farming strategies and rotation systems with respect to LI after two years of the experiment. The highest (1.26) and lowest (1.06) CPIs were observed for SOF_30% CR and SIF_no-CR, respectively. The magnitude of the CMI values followed the order SOF_30% CR (121) > IOF_15% CR (107) ≥ SIF_no-CR (106). B-W and F-W had the highest and lowest CPI with values of 1.29 and 1.01, respectively. No statistically significant difference was found for the different crop rotation systems. Given the low impact of the common farming practices in the region, e.g., SIF_no-CR and F-W, on CPI and CMI at 24 months, our results showed that farming strategies with manure application and crop residue management and summer wheat-based rotation systems appear to be more appropriate farming strategies to improve CMI in arable land.

Rising temperature from climate change is the most threatening factor worldwide for crop production. Sustainable wheat production is a challenge due to climate change and variability, which is ultimately a serious threat to food security in Pakistan. A series of field experiments were conducted during seasons 2013–2014 and 2014–2015 in the semi-arid (Faisalabad) and arid (Layyah) regions of Punjab-Pakistan. Three spring wheat genotypes were evaluated under eleven sowing dates from 16 October to 16 March, with an interval of 14–16 days in the two regions. Data for the model calibration and evaluation were collected from field experiments following the standard procedures and protocols. The grain yield under future climate scenarios was simulated by using a well-calibrated CERES-wheat model included in DSSAT v4.7. Future (2051–2100) and baseline (1980–2015) climatic data were simulated using 29 global circulation models (GCMs) under representative concentration pathway (RCP) 8.5. These GCMs were distributed among five quadrants of climatic conditions (Hot/Wet, Hot/Dry, Cool/Dry, Cool/Wet, and Middle) by a stretched distribution approach based on temperature and rainfall change. A maximum of ten GCMs predicted the chances of Middle climatic conditions during the second half of the century (2051–2100). The average temperature during the wheat season in a semi-arid region and arid region would increase by 3.52 °C and 3.84 °C, respectively, under Middle climatic conditions using the RCP 8.5 scenario during the second half-century. The simulated grain yield was reduced by 23.5% in the semi-arid region and 35.45% in the arid region under Middle climatic conditions (scenario). Mean seasonal temperature (MST) of sowing dates ranged from 16 to 27.3 °C, while the mean temperature from the heading to maturity (MTHM) stage was varying between 12.9 to 30.4 °C. Coefficients of determination (R2) between wheat morphology parameters and temperature were highly significant, with a range of 0.84–0.96. Impacts of temperature on wheat sown on 15 March were found to be as severe as to exterminate the crop before heading. The spikes and spikelets were not formed under a mean seasonal temperature higher than 25.5 °C. In a nutshell, elevated temperature (3–4 °C) till the end-century can reduce grain yield by about 30% in semi-arid and arid regions of Pakistan. These findings are crucial for growers and especially for policymakers to decide on sustainable wheat production for food security in the region.

Accelerated climate change and unsustainable agricultural practices have intensified greenhouse gas (GHG) emissions and degraded soil health, particularly in intensively cultivated landscapes. This study presents an integrated geospatial and biophysical triangulation framework to monitor ecosystem health and sustainability in the rice-based systems of Chhindwara district, Madhya Pradesh, India. The objectives were to evaluate GHG emissions, assess land use change, analyze soil health, and identify socio-economic drivers influencing agricultural sustainability under a regenerative agriculture (RA) paradigm. A combination of high-resolution satellite imagery, field-based soil sampling (n = 430), socio-economic surveys, and carbon stock modeling using the InVEST tool was employed. Land Use and Land Cover (LULC) changes over a decade (2011–2021 years) were mapped using Random Forest classification. GHG emissions were estimated using the Cool Farm Tool, while Water Use Efficiency (WUE) and biodiversity indices were evaluated across villages and seasons. In Sausar, there was a 4.6% decrease in agricultural area, a 6.6% rise in dense vegetation, and noticeable soil degradation. Mokhed had a higher soil organic content (1.07%) than Sausar (0.51%), and its carbon stocks were higher (13–35 Mg C/ha) than Sausar’s (7–13 Mg C/ha). Furthermore, Mokhed’s GHG emissions during the Kharif season were much greater at 4,000 kg CO 2 eq/ha than those during the Rabi season, which were just 1,500 kg CO 2 eq/ha. WUE varied by season and crop, with Rabi vegetables achieving the maximum WUE at 10.4 kg/ha-mm and cotton demonstrating the lowest efficiency at 1.4 kg/ha-mm in Kharif, underscoring the potential of targeted interventions. The framework demonstrated that integrating geospatial, biophysical, and socio-economic data provides actionable insights for climate-resilient agriculture. The findings support landscape-level planning for soil restoration, GHG emission mitigation, and sustainable intensification in rainfed regions to escalate RA transitioning and benefits for the local communities.

Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an extensive dataset of weather, soil, and crop phenology variables in 271 counties across Germany from 1999 to 2019. We proposed a Convolutional Neural Network (CNN) model, which uses a 1-dimensional convolution operation to capture the time dependencies of environmental variables. We used eight supervised machine learning models as baselines and evaluated their predictive performance using RMSE, MAE, and correlation coefficient metrics to benchmark the yield prediction results. Our findings suggested that nonlinear models such as the proposed CNN, Deep Neural Network (DNN), and XGBoost were more effective in understanding the relationship between the crop yield and input data compared to the linear models. Our proposed CNN model outperformed all other baseline models used for winter wheat yield prediction (7 to 14% lower RMSE, 3 to 15% lower MAE, and 4 to 50% higher correlation coefficient than the best performing baseline across test data). We aggregated soil moisture and meteorological features at the weekly resolution to address the seasonality of the data. We also moved beyond prediction and interpreted the outputs of our proposed CNN model using SHAP and force plots which provided key insights in explaining the yield prediction results (importance of variables by time). We found DUL, wind speed at week ten, and radiation amount at week seven as the most critical features in winter wheat yield prediction.

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

The construction industry is growing on a very rapid scale. Faster technological developments have been made to solve a number of issues faced by the construction industry. When it comes to materials used in construction, concrete produced from Ordinary Portland Cement is the most dominant material from many years, but it leads to serious environmental issues such as high carbon emissions produced during the cement’s production which is about eight to ten times the emissions of world’s carbon dioxide. Geopolymer Concrete lowers carbon emissions as it uses waste from industries, thus it is a sustainable and eco-friendly substitue to Ordinary Portland Cement concrete. This review assesses the fresh and hardened Geopolymer Concrete’s characteristics produced using by-products of industries, including Fly Ash, alccofine and Ground Granulated Blast Furnace Slag. Emphasizing strength and durability, the contribution of these precursors in improving Geopolymer Concrete’s performance is thoroughly investigated. Geopolymer Concrete may have more initial production costs due to cost of alkali activators but this cost can be outweighed by the long term environmental impact and economic advantages. The information presented will help scholars, business leaders, and legislators promote sustainable building methods by implementing Geopolymer Concrete.

The objective of this study is to identify cowpea genotypes that are tolerant to both phosphorous and drought stresses on highly weathered soil. It is hypothesized that (1) genotypes that have the highest grain yield under optimum conditions do not perform best under P or water stress and (2) genotypes that have the highest grain yield under P stress conditions also perform well under water or combined water and P stress. An experiment was conducted in the humid forest zone of Ghana during two dry seasons (2017 and 2018). Ten cowpea genotypes were evaluated in response to four combinations of P fertilizer and drought treatments. The treatments included 0 kg P ha−1 + water stress (0P + WS; control treatment); 60 kg P ha−1 + water stress (60P + WS); 0 kg P ha−1 + no water stress (0P + NWS); 60 kg P ha−1 + no water stress (60P + NWS; optimum condition) in both field experiments. The experiment was laid out in a split plot arrangement with three replications. The grain yield of the cowpea genotypes during 2017 growing cycle ranged between 1094 and 3600 kg ha−1, and in 2018 between 928 and 3125 kg ha−1. In both growing cycles, genotypes Asontem and GH5344 had the highest grain yield under optimum conditions (60 kg P ha−1 + water). Under combined P and water stress, Hans adua, GH6060 and Asontem were the best three genotypes with grain yield ranging between 1678 and 1478 kg ha−1 and this observation was made during both growing cycles. In conclusion, the genotypes showed a variable response to the different treatments in this study. Hypothesis 1 (genotypes that have the highest grain yield under optimum conditions do not perform best under water or P stress conditions) was not confirmed as the genotypes GH2309 and GH6060 (ranking 3rd and 4th under optimum conditions) were among the three best cultivars both under water or P stress conditions. Hypothesis 2.1 (genotypes that have the highest grain yield under P stress conditions perform well under water stress conditions) was confirmed for all genotypes studied except for the genotype Asontem. Hypothesis 2.2 (genotypes that have the highest grain yield under P stress conditions perform also well under combined water and P stress) was true since the best four genotypes under P stress where the best four genotypes under combined water and P stress (0P + WS). GH6060, Hans adua and Asontem are most adapted to combined water and P stress and need to be further explored to ascertain their potential as drought and phosphorus deficiency-tolerant genotypes.

Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble framework integrating Recursive Feature Elimination (RFE) with meta-learning techniques. Seven advanced machine learning models- Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extremely Randomized Trees (ET), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and a Meta Classifier (MC) were applied using Remote Sensing and GIS tools to identify key landslide-conditioning factors and classify susceptibility zones. Model performance was assessed through metrics such as accuracy, precision, recall, F1 score, and AUC of the ROC curve. Among the models, the Meta Classifier (MC) achieved the highest accuracy (0.956) and AUC (0.987), demonstrating superior predictive ability. Gradient Boosting (GB), XGBoost, and RF also performed well, with accuracies of 0.943 and AUC values of 0.987 (GB and XGBoost) and 0.983 (RF). Extremely Randomized Trees (ET) exhibited the highest accuracy (0.946) among individual models and an AUC of 0.985. SVM and LR, while slightly less accurate (0.941 and 0.860, respectively), provided valuable insights, with SVM achieving an AUC of 0.972 and LR achieving 0.935. The models effectively delineated landslide susceptibility into five zones (very low, low, moderate, high, and very high), with high and very high susceptibility zones concentrated in Darjeeling and Kalimpong subdivisions. These zones are influenced by intense rainfall, unstable geological structures, and anthropogenic activities like deforestation and urbanization. Notably, ET, RF, GB, and XGBoost demonstrated efficiency in feature selection, requiring fewer input variables while maintaining high performance. This study establishes a benchmark for landslide susceptibility mapping, providing a scalable and adaptable framework for geospatial hazard prediction. The findings hold significant implications for land-use planning, disaster management, and environmental conservation in vulnerable regions worldwide.