Current Issue
Volume 1, Issue 1 - 2026 (Jan-June 2026 )
Issue Details:
Volume 1 Issue 1 (Jan-June 2026)Issue Description:
Welcome to the 2026 issue of Journal of Agrosystems and Analytics. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges. We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research. As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.
Dr. Neelam Patel
Editor-in-Chief
Journal of Agrosystems and Analytics
Articles in This Issue
Assessment of Sugarcane Evapotranspiration Across Growing Seasons Utilizing Remote Sensing and pySEBAL Model
The experiment explored AET seasonal dynamics of a sugarcane field at the Navsari Agricultural University (NAU) in Navsari using the high-resolution remote sensing to address the deficiency of ground-based micrometeorological instruments, e.g. lysimeters or eddy covariance towers. Surface Energy Balance Algorithm on Land (SEBAL) was run in a Python-GRASS GIS setting that combined Landsat 8 satellite data on land with local weather data. The outputs of SEBAL were checked by comparing the estimates with the cloud-based METRIC-EEFlux algorithm. Phenological measurements using NDVI time-series data monitored the sugarcane growth cycle with the greatest vegetative vigor in June and senescence during the months of November to December. Spatial analysis showed that seasonal AET calculated using SEBAL was between 1459 and 1496 mm. An effective rainfall of 676 mm was taken into consideration to estimate the total irrigation water requirements (IWR) of 783 to 820 mm. Conversely METRIC-EEFlux always gave higher values of AET, with the values falling between 1627 and 1698 mm. The pixel-by-pixel comparison of the two models each day showed a moderate correlation (R2 = 0.63) with a Root Mean square error (RMSE) of 1.40mm/day. SEBAL had a mean error (MBE) of bias of -1.17 mm/day, which shows that it was generally underestimated compared to METRIC-EEFlux. The results notwithstanding these differences in the algorithms, SEBAL is a powerful, well-adjusted tool to measure crop water needs and support sound water management decisions in data deficient areas.
Contributors:
Comparative Evaluation of APSIM and CANEGRO Models for Simulating Sugarcane Growth and Yield
This study investigates the performance of the APSIM and CANEGRO crop models in simulating sugarcane growth and yield under different growing environment and fertilizer application. Field experimental data from the 2023–24 season were employed for model calibration, while validation was conducted using data from the 2024–25 season. During calibration, the CANEGRO model demonstrated limited responsiveness to variations in fertilizer dosage, producing uniform outputs across different nutrient treatments. Model validation results indicate that APSIM outperformed CANEGRO in simulating key agronomic parameters including cane yield, aerial dry biomass, and days to emergence. APSIM achieved a higher coefficient of determination (R² = 0.93), D-index (0.95), and lower RMSE (6.01 t/ha) for cane yield compared to CANEGRO (R² = 0.88, D-index = 0.77, RMSE = 6.29 t/ha).
Contributors:
Sensitivity Analysis of the APSIM-Sugar and CANEGRO Sugarcane Growth Simulation Models
This study investigates the sensitivity of the APSIM and CANEGRO crop models to key climatic parameters and genetic coefficients in simulating sugarcane growth and yield. Sensitivity analyses to genetic coefficient identified critical genetic parameters influencing crop performance. In the CANEGRO model, MaxPARCE, APFMX, and STKPFMAX were the most influential for yield, biomass, and sucrose content, while LFMAX and SER0 significantly impacted LAI and stalk height. These findings suggest that calibration efforts should prioritize phenological, growth, and yield-related parameters. In contrast, global sensitivity analysis using the APSIM model highlighted RUE4, MSS, and GLN as the most impactful parameters affecting cane yield, commercial cane sugar (CCS), and sucrose accumulation, with RUE and MSS emerging as key contributors to both biomass production and sugar content. Additionally, both models exhibited sensitivity to climatic variables. A rise in temperature resulted in a decline in cane yield, more pronounced in CANEGRO (31.35% reduction at +6 °C) compared to APSIM (15.81%). Increases in solar radiation enhanced cane yield and sucrose dry mass, while reduced radiation had adverse effects. Overall, the findings provide actionable insights for improving model calibration and support cultivar selection and management practices under projected climate change scenarios.
Contributors:
Assessment of Climate Change Impact on Sugarcane Productivity in South Gujarat Using the CANEGRO Model
The study aims to assess the impact of climate change on sugarcane yield attributes in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. Yield simulations using CANEGRO indicate moderate yield declines (-1% to -2.1%) under SSP245 but substantial reductions (-14% to -15%) under SSP585 due to heat and water stress. Sucrose content also exhibited sharper declines, underscoring the adverse effects of high-emission scenarios. These findings highlight the necessity for climate adaptation and mitigation strategies in sugarcane cultivation.
Contributors:
Assessment of Future Climate Change Projections in South Gujarat Using Bias-Corrected GCMs
The study aims to assess the climate change projections in South Gujarat region using bias-corrected General Circulation Model (GCM) projections under SSP245 and SSP585 scenarios. For maximum temperature, the chosen models were ACCESS-CM2, CMCC-ESM2, GFDL-CM4, KIOST-ESM, and TaiESM1. For minimum temperature, ACCESS-ESM1-5, CNRM-ESM2-1, EC-Earth3, and INM-CM5-0 were selected. The models identified for rainfall simulation included ACCESS-CM2, KACE-1-0-G, MPI-ESM1-2-LR, MRI-ESM2-0, and TaiESM1. These models were selected based on their accuracy in representing historical climate data and their applicability for future climate projections in the study region. Under SSP585, maximum temperature is projected to rise by 2.4 °C and minimum temperature by over 5.6 °C by the end of the century. Rainfall projections suggest a potential increase of up to 14.50% by 2090. An evaluation of GCM bias correction methods revealed that Quantile Mapping (QM) significantly outperformed Linear Scaling (LS) in reducing Root Mean Square Error (RMSE). While LS struggled with complex deviations, QM effectively corrected distributional biases and extreme outliers across temperature and precipitation datasets, proving essential for reliable climate modeling.
Contributors:
Estimation of Land Surface Heat Fluxes Based on Landsat 8 Satellite Data
Land surface heat fluxes encompass net radiation flux (Rn), soil heat flux (G), sensible heat flux (H), and latent heat flux (LE), all of which play a crucial role in understanding energy transfer within earth–atmosphere interactions. This study utilized Landsat 8 data to estimate land surface heat fluxes over the Navsari district of South Gujarat, India using the SEBAL (Surface Energy Balance Algorithm for Land) model. Rn followed a seasonal trend of summer > autumn > spring > winter, with median values ranging from 607.7 W/m2 in summer to 459.9 W/m² in winter. G exhibited a similar pattern, while H varied as summer > winter > spring > autumn. LE showed the opposite trend, peaking in autumn (427.3 W/m2) and decreasing through spring, winter, and summer. Notably, the LE remained higher than the H across all seasons. Rn was primarily allocated to LE across most LULC types, except in water bodies, where it was nearly evenly distributed between LE and G. In the absence of ground-based instruments, SEBAL outputs were validated using EEFlux METRIC, a cloud-based evapotranspiration (ET) estimation tool. The validation showed strong agreement for land surface temperature (LST) (R2 = 0.976, RMSE = 5.63 K) and moderate agreement for ET (R2 = 0.632, RMSE = 1.40 mm/day), albedo (R2 = 0.532, RMSE = 0.06), and crop coefficient Kc (R2 = 0.452, RMSE = 0.18). The SEBAL model was also applied to estimate seasonal ET and determine the total water requirement for sugarcane.