Mediterranean agricultural systems are highly vulnerable to increased climatic variability, which threatens soil water availability and the functionality of the soil carbon (C) cycle. Soil management practices strongly influence water dynamics and C-substrate quality, thus potentially affecting the temperature sensitivity of soil respiration. We evaluated the combined effects of tillage (traditional tillage, TT; reduced tillage, RT), fertilization (mineral, MF; addition of biosolid compost, BC), and rainfall inputs (ambient conditions, C; reduction of 30% rainfall inputs, EX) on soil water content (SWC) and storage (SWS), and in situ soil respiration (Resp) dynamics over three agricultural seasons in a Mediterranean legume–wheat rotation, using a factorial field experiment. We also evaluated how the sensitivity of soil respiration to temperature could be affected by tillage and fertilization types in a complementary laboratory experiment under controlled moisture and temperature conditions. RT was effective in improving SWS and mitigating surface desiccation, although this advantage was attenuated in wet years due to homogenization of moisture along the soil profile. Soil Resp was primarily controlled by SWC. BC stimulated soil respiration mainly during the first crop season, with a residual non-significant trend in the third season. This effect appeared constrained under dry periods, although no significant fertilization × rainfall exclusion interaction was detected. The diurnal cycle of Resp showed a clear decoupling from diurnal soil temperature. Crucially, the intrinsic thermal sensitivity of respiration (Q10) remained stable across all tillage and fertilization treatments, suggesting that field variability is driven by water dynamics and crop phenology and not by microbial responses to changes in substrate availability. Our results confirmed the hierarchical role of climate on C-cycling processes.

Climate change and population growth present significant challenges to global food security, underscoring the critical importance of sustainable and efficient agricultural production. Crop rotation is a key agricultural practice that enhances food production, improves soil fertility, reduces pest and disease pressure, and maintains agro-ecological balance. The complexity and diversity of cropping patterns, particularly in the fragmented farmland of southern China, limit the availability of high-resolution crop rotation maps in precision agriculture. To improve the consistency between cropping intensity (CI) estimation and crop pattern (CP) mapping, this study developed a hierarchical framework for extracting cropland, CI, and CP from remotely sensed images. Using the Google Earth Engine (GEE) platform, a 10-m binary cropland/non-cropland map was first generated from the time-series Normalized Difference Vegetation Index (NDVI). Then, CI was derived within cropland regions using an intelligent algorithm that counts the number of growth cycles. Finally, taking advantage of crop phenology and CI constraints, nine cropping patterns were extracted from a diversified cropping region. Comparing with field survey data, the results revealed overall accuracies of 98.97%, 96.47%, and 87.92% for the cropland/non-cropland map, cropping intensity map, and cropping pattern map, respectively. These findings demonstrate the reliability of the generated maps and the potential of the proposed framework for revealing diverse cropping patterns in complex cropping regions.

A novel approach to assessing the tracking accuracy of crop phenology for multi-orbit and multi-feature Sentinel-1 time series

Mycotoxin contamination in wheat is strongly influenced by weather conditions, yet how contamination may evolve under future climate and socioeconomic change remains poorly understood at the European scale. Here, we develop a hybrid modelling framework combining machine learning and crop phenology to assess scenario-dependent changes in mycotoxin contamination. The framework integrates historical monitoring data with climate projections under multiple Shared Socioeconomic Pathways. Model evaluation shows strong performance for low-contamination conditions, while the ability to distinguish higher contamination levels remains limited due to class imbalance. Results indicate an overall increase in contamination risk under climate change, particularly for deoxynivalenol (DON), with relatively higher risk in coastal and northwestern Europe. These findings highlight the role of climate-driven shifts in crop phenology and weather conditions, and provide a scenario-based framework for exploring future mycotoxin risk patterns rather than precise quantitative predictions.

Multi-scale spatial-temporal remote sensing fusion for phenology identification in rice germplasm resources

Global farming is being shaken by climate change. It is altering the temperature in terms of how hot or cold it becomes, it is affecting rain patterns and it is increasing weather events which means that crops are encountering a number of new stresses and erratic production. Phenological traits refer more or less to the internal clock of the crop. They give a plant information on when to sprout, flower and ripen. They are a primary method of plant communication with the changing weather and their continued existence. This review is a summary of what has been known to maintain yields stable even as the planet is warming. The paper shows the fundamental concept of crop phenology, the primary weather factors that push it about and how the timing of developmental stages relates to the way in which a plant manufactures and consumes nutrients. What the data revealed is that with an increase in temperatures, those phases will shift more rapidly and thus crops will be exposed to certain stress at a younger age than they used to. That interferes with the amount of plant matter they accumulate, the number of seed they receive and the distribution of that seed they receive. There is also a discussion of long-term breeding, which simulates the crop schedules and monitors the progression of how plants change their timing on cameras with satellite or drone technology, all in the name of predicting and addressing future heat issues. Still, there are big gaps. This phenology is the key to surviving the climate variability of crops and it is a good direction to intelligent breeding and the climate-resistant agriculture. It must obviously implement in the betterment of crops the thinking which is driven in case we are to retain our farms as productive when the weather is not so certain.

Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized, particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study, we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton, winter jujube, and tiger nut crops in Tumushuke City, Xinjiang Uygur Autonomous Region, China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection, followed by Savitzky-Golay (S-G) filtering and double logistic function fitting, to automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach, we could systematically compare five classification models (multi-layer perceptron (MLP), residual network-18 (ResNet-18), convolutional long short-term memory (ConvLSTM), Transformer, and random forest classifier (RFC)), demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer's spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability, with F1 scores of 94.37%, 87.75%, and 86.35% for the three crops (winter jujube, cotton, and tiger nut), respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments, enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.

ABSTRACT This study revealed that among climatic factors, rainfall, temperature, and relative humidity significantly affected the seasonal population of fall armyworm (FAW). The drier weather favoured its movement and spread whereas rainy weather with higher humidity reduced adults' foraging and larval activity. The seasonal fluctuations of population showed peaks during specific time periods corresponding to specific crop stage of maize. The damage scores, alone, were insufficient indicators for assessing maize yield losses by FAW, as plant damage and FAW infestation varied across the growth stages of maize. Moreover, variable cropping seasons and weather conditions (rainy or dry) across ecological zones significantly influenced the developmental duration of both maize and FAW. Accumulated growing degree days (GDDs) in combination with crop phenological stages proved to be effective tools for predicting FAW occurrence, infestation severity, and optimal timing for management interventions. Although temperature showed a strong association with GDDs accumulation of both maize crop and FAW, deviations from optimal thermal conditions resulted in non‐linear responses in crop development and FAW activity. Hence, suggesting a regular monitoring of crop regarding FAW incidence in relation to GDDs and crop phenology. Overall, these findings highlighted that the vegetative phase of maize, especially at 4–6 leaf stages, were found very critical for FAW damage, underscoring the utmost important time window to manage FAW effectively. Moreover, integrated consideration of climatic factors, crop phenology, and thermal requirements (GDDs) is essential for effective and predictive management of fall armyworm in maize cropping systems.

A field experiment was carried out during the Kharif season of 2022 at NEBCRC, GBPUA&T, Pantnagar, Uttarakhand to evaluate the comparative evaluation of nano and commercial urea on growth, phenology and yield of maize (Zea mays L.). The experiment was laid out in a factorial randomized block design with 3 replications. The treatments comprised of 12 treatments The results revealed that growth parameters, crop phenology, yield production efficiency and monetary efficiency were increased by the application of 100 % RDN (120 kg N/ha) along with 2 foliar sprays of nano urea or 2 % urea at KH and PT stages but it remained at par with 5/6th RDN + 2 foliar sprays of nano urea at KH and PT stage and 100 % RDN applied through commercial urea. Maximum grain yield (7,288 kg/ha), shelling (77.7 %) and HI (34.6 %) were obtained in 100 % RDN + 2 foliar sprays of nano urea. However, production and monetary efficiency did not vary significantly among 100 % RDN, 100 % RDN + foliar spray of nano urea, 100 % RDN + foliar spray of 2 % urea and 5/6th RDN + nano urea. Overall, concluded that maize can be fertilized with 5/6th RDN (100 kg N/ha) + 2 foliar sprays of nano urea or 2 % urea at the KH and PT stage without a reduction in productivity or profitability compared to 100 % RDN.

ABSTRACT Drought events represent an ever‐growing concern in Mediterranean areas. The occurrence and intensity of this abiotic stress can variously affect the growth and productivity of common bean. In order to shed light on the impact of the drought stress on the growth and agronomic performance of the crop, forty‐four landraces and two testers were grown under stress and non‐stress conditions. The water deficiency started on the onset of the reproductive phase. The experiment was carried out as a split‐plot design over a period of 2 years. The effect of the drought stress on the common bean landraces was assessed by analysing the results of the grain yield and its components, which were significantly affected by the water availability ( p ≤ 0.001). Drought tolerance indices were then calculated based on the grain yield results to further investigate the effect of the water deficiency on the crop phenology and productivity, and to identify drought‐tolerant genotypes. Six landraces with climbing habitus were identified as the most drought tolerant; two dwarf landraces showed low yield depression and low susceptibility to drought. These drought‐resistant candidates could be used to maintain the traditional cultivation of common bean in areas exposed to limited water availability in Southern Italy and other territories with similar climate conditions.

Brown marmorated stink bug (BMSB), Halyomorpha halys (Stål) (Hemiptera: Pentatomidae), severely impacts global hazelnut production, especially in Türkiye, the top producer, and Oregon, USA. This study analyzed seasonal BMSB population dynamics and damage types on hazelnut fruiting bodies across phenological stages. Field monitoring in Oregon's Willamette Valley (2014-2016) and Türkiye's Eastern Black Sea region (2019-2021) showed distinct seasonal activity patterns. Hazelnut phenology in Türkiye advances 6 weeks earlier than in Oregon with higher heat accumulation sustaining BMSB activity post-harvest. Semi-field cage experiments conducted in 2022 in Türkiye quantified insect-induced damage in relation to nut development. Five distinct damage types were identified: shell malformation (4.7%) in May-June, blank black shell (2.2%) and empty kernel (6.5%) in June, shriveled kernel (8.4%) in late June-mid-July, and corked kernel (10.5%) in July-August. A single adult was capable of damaging up to 537 nuts per season, with losses reaching 85% during the kernel expansion stage. Damage increased progressively through the season, with total damage 1.6-fold higher during kernel expansion compared to early development. Corked kernel damage was uniquely associated with BMSB feeding, confirming its diagnostic value. Findings demonstrate that BMSB injury is strongly shaped by the timing of insect activity relative to hazelnut phenology, with peak mid- to late-season activity driving greater losses, particularly in late-maturing cultivars. By linking damage types with per-capita impact, this study defines critical intervention windows for integrated pest management and highlights the need for adaptive forecasting tools that integrate pest biology, climate, and crop phenology.

• Voxel-based virtual Chinese cabbage fields with shadow correction were developed to simulate 3D canopy structural dynamics. • Red band reflectance was simulated using a voxel-based canopy framework driven by incident solar irradiance simulated by SMARTS model and validated against Sentinel 2 imagery. • A lookup table was constructed by simulating Red band reflectance across growth stages, cultivation patterns, sun zenith angles, and survival rates. • The proposed framework provides a 3D-based methodology that can be also extended to other vegetable crops for satellite-based phenology monitoring. Crop phenological information is crucial for agricultural activities such as fertilizer management, irrigation, disease prevention, and yield estimation. While optical Vegetation Indices and Synthetic Aperture Radar estimate crop phenology using satellite time series data, their universal application remains limited. Recently, simulating reflectance of virtual scenarios has proven effective for detecting crop biophysical parameters. This study simulated extensive voxel-based virtual Chinese cabbage fields under various seasons, growth stages, cultivation patterns, and survival rates across East Asia. A lookup table was created using reflectance simulations from the Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) model, linked to Sentinel-2 Red band reflectance with shadow correction. Our key findings include: (1) Red band reflectance decreases from the rosette stage to heading, rebounding slightly during dormancy due to biophysical changes; (2) higher Sun Zenith Angles (SZAs) in winter increase atmospheric path length, leading to greater radiation scattering and absorption, reducing Red band reflectance; (3) a 0.6-meter cultivation spacing is optimal, as 0.4 meters may hinder growth due to leaf overlap, while 0.8 meters could lead to inefficient land use; and (4) increased survival rates elevate vegetation fraction, reducing Red band reflectance. The lookup table was validated against Sentinel-2 imagery from 13 sites across Northeastern China, Japan, and the Koreas, achieving an R² value of 0.75. This methodology, while focused on Chinese cabbage, is also expected to enhance phenological stage detection for other vegetable crops using high-resolution satellite imagery.

Sulphur and manures nutrition in oilseeds: Crop demand, physiology, phenology, deficiency symptoms and management

Using CSM-CROPGRO as a template for perennial tree crop phenology modeling – TreeGRO

The Midwest United States experiences significant climate variability that affects the optimal soybean planting date. Inaccurate estimation of planting dates in actuarial models for Weather Index Insurance (WII) can increase basis risk and adverse selection. This study develops a dynamic, transparent WII framework that adapts to temporal climate variability and integrates crop phenology into risk assessment and pricing. A two-step methodology is proposed: first, planting dates are predicted at a 10 km resolution using a machine learning model trained on pre-growing season variables. Second, remote-sensing indices during the growing season are synchronised with predicted phenological stages at weekly intervals to improve county-level crop yield prediction. The framework links these dynamic yield predictions to insurance design, enabling more accurate premium ratings and capital management. The planting date model achieves a mean absolute error (MAE) of 3.33 days and a root mean square error (RMSE) of 4.4 days, outperforming comparable studies. The dynamic yield model improves prediction accuracy by 7.8% (RMSE) and 6.7% (MAE) over a static benchmark based on fixed contract dates. Results show that Gross Primary Production (GPP) during early growth stages drives the dynamic model, whereas the benchmark relies on mid-to-late season climate variables. The stronger early-season signal highlights the dynamic model’s ability to capture critical predictors during germination and early vegetative phases, which are highly indicative of final yields. This framework advances WII design by promoting adaptive, data-driven, and climate-responsive agricultural risk management. • Integrates climate change and land-use dynamics in landslide forecasting • Employs XGBoost with SHAP for explainable AI-based susceptibility mapping • Projects landslide risk in Western Ghats under four future SSP scenarios • Identifies regional hotspots with rising susceptibility by 2100 • Provides data-driven insights for adaptive, climate-resilient planning

Wheat blast, caused by Pyricularia oryzae Triticum lineage, is a major constraint to wheat expansion in the Brazilian Cerrado. Delayed sowing is often recommended for disease avoidance; however, late sowing can increase the risk of water deficit. This study quantifies the joint risks from head blast and water deficit in the Brazilian Cerrado to inform sowing-date recommendations. Using daily weather data from a 62-year period (1961-2023), crop phenology was simulated based on an accumulated growing degree day approach. Heading and maturity dates were estimated for six sowing dates defined at 10-day intervals across the sowing window, starting on February 25. Epidemic probability was calculated using a logistic model, while water-deficit events were identified based on an accumulated effective precipitation threshold (150 mm) during crop growth. For each pixel within the potential wheat area in the Cerrado and for each sowing date, we empirically estimated the probability of at least one hazard, fitted smooth pixel-wise probability curves, and identified the optimal sowing date. A spatially smoothed 7-day bin classification was used to generate a practical sowing-recommendation map. In addition, we assessed the effects of the El Niño-Southern Oscillation on epidemic probability. Results showed that mid- to late-March sowings generally minimize joint risk. Spatial heterogeneity highlighted regions suitable for earlier sowing and potential expansion. El Niño/La Niña events amplified/suppressed epidemic probability, particularly for intermediate sowing dates where sensitivity was greatest. These findings support region-specific, multi-hazard sowing recommendations and the incorporation of climate-variability signals into decision support for tropical rainfed wheat.

Maize (Zea mays L.) growth and productivity are strongly influenced by temperature and seasonal climatic conditions that regulate crop phenology and thermal energy utilization. However, limited information is available on seasonal variability of thermal indices and heat use efficiency of maize under different varieties and nutrient management practices in the Tarai region of Uttarakhand. Therefore, a field experiment was conducted at the N.E. Borlaug Crop Research Centre, Pantnagar during the winter (November 2024-April 2025) and spring (February-June 2025) seasons to evaluate seasonal effects on thermal indices, phenology and heat use efficiency of maize. The experiment was laid out in a split-plot design with three maize varieties (PCM-04, DKC-9081 and DKC-9188) as main plot treatments and four nutrient management levels as subplots: T1-control (no fertilizer), T2-vermicompost (80:20), T3-poultry manure (80:20) and T4-recommended dose of inorganic fertilizers (RDF). Temperature-based indices including Growing Degree Days (GDD), Heliothermal Units (HTU), Photothermal Units (PTU) and Heat Use Efficiency (HUE) were computed. Spring maize accumulated higher thermal units (GDD 1691.45 °C Day, HTU 14327.25 and PTU 21830.23) and recorded higher grain yield (9373.04 kg ha⁻¹) than winter maize (GDD 1303.38 °C Day, HTU 9824.29, PTU 14733.81; yield 7305.00 kg ha⁻¹). Spring maize also matured earlier (92-110 days) compared to winter maize (132-151 days). The treatment V₃F₄ (DKC-9188 with RDF) recorded the highest grain yield and heat use efficiency in both seasons. The results indicate that improved varieties combined with optimum nutrient management enhance thermal resource utilization and maize productivity under different seasonal environments.

ABSTRACT Phenological metrics are a set of measurements obtained from Earth observation (EO) satellite image time series that allow the estimation of phenological stages. These include indicators like the start of the greening season, the onset of senescence, and the growing season length. They are useful for crop monitoring. Today, large volumes of images are produced and made available by different EO satellites. These large EO data sets pose a challenge for storage and processing systems, exceeding the capacity of personal computers to handle them. This paper presents a free and open-source tool for phenological metrics analysis from large EO image collections that runs on server-side infrastructure and does not require local data downloads. The Web Crop Phenology Metrics Service (WCPMS) is the core of this tool, designed to estimate phenological metrics as a web service. The tool extracts phenological metrics associated with spatial locations, based on the Brazil Data Cube (BDC) platform. It calculates phenological metrics from data cubes of distinct remote sensing image collections. The potential of the tool is shown through an experiment estimating soybean sowing dates using phenological metrics compared with field data obtained in the Central-South region of Brazil.

Understanding how farming systems management influences soil microbial communities is essential for advancing sustainable agriculture in tropical regions. Long-term experiments provide valuable opportunities to assess how cumulative management practices shape soil microbial diversity and community composition. We investigated prokaryotic communities after 15 years of continuous management in the Long-Term Farming Systems Comparison Trial (SysCom-Kenya) at two contrasting sites (Chuka and Thika) in the Central Highlands of Kenya. Four systems were evaluated: conventional low-input (Conv-Low), conventional high-input (Conv-High), organic low-input (Org-Low), and organic high-input (Org-High). Soil samples were collected at key crop growth stages (vegetative, reproductive, and maturity) of maize, baby corn, and potato. Prokaryotic diversity and community composition were characterized using 16S rRNA gene amplicon sequencing, and soil chemical properties were analyzed to explore potential abiotic drivers. Prokaryotic community composition and diversity varied primarily with site and farming system, with secondary variation across crop growth stages. Across all systems, communities were dominated by members of the phyla Proteobacteria and Actinobacteria, followed by Acidobacteria, Firmicutes, and Chloroflexi. Organic systems, particularly organic high-input, tended to support higher richness and evenness than conventional systems, while low-input systems consistently exhibited lower prokaryotic richness and diversity than high-input systems. Diversity generally increased toward later crop growth stages, although phenological effects were variable. Canonical correspondence analysis identified soil pH, ammonium-N, and available phosphorus as important correlates of community structure, especially at the drier Thika site. Taxon-specific enrichment patterns differed across systems and crop stages, indicating compositional differentiation rather than functional dominance. Our findings indicate that long-term management intensity and organic input diversity exert a stronger influence on soil prokaryotic communities than short-term crop phenology. Despite limitations from sample pooling, this study provides novel evidence from sub-Saharan Africa that diversified organic input management can enhance soil microbial diversity and potential resilience, supporting sustainable soil management in tropical farming systems.

BackgroundUnderstanding how farming systems management influences soil microbial communities is essential for advancing sustainable agriculture in tropical regions. Long-term experiments provide valuable opportunities to assess how cumulative management practices shape soil microbial diversity and community composition.MethodsWe investigated prokaryotic communities after 15 years of continuous management in the Long-Term Farming Systems Comparison Trial (SysCom-Kenya) at two contrasting sites (Chuka and Thika) in the Central Highlands of Kenya. Four systems were evaluated: conventional low-input (Conv-Low), conventional high-input (Conv-High), organic low-input (Org-Low), and organic high-input (Org-High). Soil samples were collected at key crop growth stages (vegetative, reproductive, and maturity) of maize, baby corn, and potato. Prokaryotic diversity and community composition were characterized using 16S rRNA gene amplicon sequencing, and soil chemical properties were analyzed to explore potential abiotic drivers.ResultsProkaryotic community composition and diversity varied primarily with site and farming system, with secondary variation across crop growth stages. Across all systems, communities were dominated by members of the phyla Proteobacteria and Actinobacteria, followed by Acidobacteria, Firmicutes, and Chloroflexi. Organic systems, particularly organic high-input, tended to support higher richness and evenness than conventional systems, while low-input systems consistently exhibited lower prokaryotic richness and diversity than high-input systems. Diversity generally increased toward later crop growth stages, although phenological effects were variable. Canonical correspondence analysis identified soil pH, ammonium-N, and available phosphorus as important correlates of community structure, especially at the drier Thika site. Taxon-specific enrichment patterns differed across systems and crop stages, indicating compositional differentiation rather than functional dominance.ConclusionOur findings indicate that long-term management intensity and organic input diversity exert a stronger influence on soil prokaryotic communities than short-term crop phenology. Despite limitations from sample pooling, this study provides novel evidence from sub-Saharan Africa that diversified organic input management can enhance soil microbial diversity and potential resilience, supporting sustainable soil management in tropical farming systems.