Search Results (87)

In the past three decades, localized research has highlighted shifts in rainfall patterns and temperature trends in central Ethiopia, a region vital for agriculture and economic activities and heavily dependent on climate conditions to sustain livelihoods and ensure food security. However, comprehensive analyses of long-term climate data remain limited for this area. Understanding local climate trends is essential for enhancing agricultural resilience in the study area, a region heavily dependent on rainfall for crop production. This study analyzes historical rainfall and temperature patterns over the past 30 years and projects future climate conditions using downscaled CMIP6 models under SSP4.5 and SSP8.5 scenarios. Results indicate spatial variability in rainfall trends, with certain areas showing increasing rainfall while others experience declines. Temperature has shown a consistent upward trend across all seasons, with more pronounced warming during the short rainy season (Belg). Climate projections suggest continued warming and moderate increases in annual rainfall, particularly under SSP8.5 by the end of the 21st century. It is concluded that both temperature and rainfall are projected to increase in magnitude by 2080, with higher Sen’s slope values compared to earlier periods, indicating a continued upward trend. These findings highlight potential breaks in agricultural calendars, such as shifts in rainfall onset and cessation, shortened or extended growing seasons, and increased risk of temperature-induced stress. This study highlights the need for localized adaptation strategies to safeguard agriculture production and enhance resilience in the face of future climate variability. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Climate change has increasingly disrupted traditional farming systems, particularly in highland areas where environmental changes are more pronounced. This study explores how rice farmers in the Lake Toba highlands, Indonesia—both irrigated and non-irrigated—have gradually shifted away from traditional knowledge (TK) in response to climate challenges and what new adaptation strategies have emerged to sustain rice production. This study employed a descriptive qualitative approach with a broad and holistic perspective. Data were collected from 130 purposively selected rice farmers in two sub-districts: Harian (irrigated) and Pangururan (non-irrigated). Data were gathered through in-depth interviews guided by semi-structured statements, focusing on farmers’ lived experiences and adaptation strategies across the rice farming cycle—from planting to harvesting. The findings revealed that while the two groups differ in water access and environmental conditions, they show similar trends in shifting away from traditional indicators. Farmers increasingly adopted new adaptation strategies such as joining farmer groups, using water pumps in non-irrigated areas, switching to more climate-resilient crop varieties, and adjusting planting calendars based on personal observation rather than inherited natural signs. This shift from traditional to practical, experience-based strategies reflects farmers’ responses to the fading reliability of traditional knowledge under changing climatic conditions. Despite the loss of symbolic TK practices, farmers continue to demonstrate resilience through peer collaboration and contextual decision-making. This study highlights the need to strengthen farmer-led adaptation while preserving valuable elements of TK. Future research should expand across the Lake Toba highlands and incorporate quantitative methods to capture broader patterns of local adaptation. Full article

Agriculture remains the backbone and major source of livelihood for men and women in most parts of sub-Saharan Africa. However, the gender-differentiated roles in agricultural transformation as a coping strategy to climate change in this context still beg for empirical substantiation. Using the Santchou Landscape of Cameroon as a case, this study sought to (a) examine the effects of climate change on agricultural practices, (b) characterize gender-differentiation in agro-based climate adaptation interventions, and (c) explore the gender-based challenges to agro-based climate adaptation planning. A representative sample of 159 households was conducted in five communities in the study area, complemented by key informant interviews (N = 5). The data collected were analyzed descriptively. The findings of this study revealed the following conclusions: Firstly, climate change significantly affects agricultural practices in the Santchou Landscape as mirrored in faming season fluctuation as well as the alteration of the farming calendar. Secondly, men and women play differentiated roles in agro-based climate adaptation, especially through farming practices such as the introduction of drought-resistant crops, the the practice of intercropping and agroforestry. Thirdly, gender-based challenges to agro-based climate adaptation include unequal access to land between men and women and unequal access to farm inputs, agricultural training, and technology. This study provides empirical evidence to substantiate the theoretical position on gender-differentiated roles in agro-based climate adaptation. Further studies are required to establish the incidence of gender variations in agro-based climate adaptation on livelihoods. Full article

Fungal spore calendars for Mexico are non-existent. This research represents the first fungal spore concentration data in the atmosphere of Hermosillo, Mexico, a city in the Sonoran Desert with high rates of allergies and public health problems. We used standardized sampling techniques frequently used by aerobiologists, including a Burkard spore trap to monitor airborne fungal spores daily for 2016–2019 and 2022–2023. Results are expressed as daily fungal spore concentrations in air (spores/m³ air). The most common fungal outdoor spores corresponded to Cladosporium (44%), Ascospora (17%), Smut (14%), Alternaria (12%), and Diatrypaceae (7%) of the total 6-year data. High minimum temperatures produce an increase in the most important spores in the air (Cladosporium and Alternaria), whereas precipitation increases Ascospore concentrations. The most important peak of fungal spore concentration in the air is recorded during summer–fall in all cases. Airborne fungal spores at Hermosillo had a greater impact on human health. These data will be of great help for the prevention, diagnostics, and treatment of seasonal allergies in the population and for the agricultural sector that has problems with some pathogens of their crops caused by fungus. Full article

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality $NDVI$ time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality $NDVI$ time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original $NDVI$ time series and producing high-quality $NDVI$ profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an $RMSE$ of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality $NDVI$ time series data could produce timely and spatially explicit phenological information at the field and crop levels. Full article

Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM_2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM_2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM_2.5 levels, with R² values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM_2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM_2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution. Full article

Brazil, a leading rice producer globally, faces challenges in systematically mapping its diverse rice fields due to varying cropping systems, climates, and planting calendars. Existing rice mapping methods often rely on complex techniques like deep learning or microwave imagery, posing limitations for large-scale mapping. This study proposes a novel approach utilizing Sentinel-2 spectral–temporal metrics (STMs) in conjunction with a random forest classifier for rice paddy mapping. By extracting diverse STMs and training both regional and global classifiers, we validated the method across independent areas. While regional models tended to overestimate rice areas, the global model effectively reduced discrepancies between our data and the reference maps, achieving an overall classifier accuracy exceeding 80%. Despite the need for further refinement to address confusion with other crops, STM exhibits promise for national-scale rice paddy mapping in Brazil. Full article

Tomatoes are one of the predominant vegetable crops grown throughout the year in Tamil Nadu, India. Their perishable nature and resource-intensive cultivation make them susceptible to biotic stress. The damage caused by invasive insect pests, bacterial wilt during the rainy season, and viral diseases are major yield-limiting factors, and the farmers mostly depend on calendar-based insecticide applications for insect pest and disease management in tomatoes. The desired tomato hybrids grafted onto bacterial wilt-resistant eggplant rootstocks offer protection against bacterial wilt during the rainy season. The integrated pest and disease management (IPDM) practices consist of resistant grafted tomato seedlings (wild eggplant rootstocks EG 203 and TS 03), bioinoculants (Bacillus subtilis + Trichoderma asperellum + Purpureocillium lilacinum), pheromone traps (Phthorimaea absoluta and Helicoverpa armigera), botanicals (azadirachtin), microbial pesticides (Bacillus thuringiensis, Metarhizium anisopliae, and Beauveria bassiana), and bio-rationals, which were evaluated in four locations in two major tomato-growing tracts of Tamil Nadu. The results revealed that the treatment EG 203 eggplant rootstock-grafted tomato along with IPDM practices performed better across all experimental locations than the other treatment combinations viz., TS 03 eggplant rootstock-grafted tomato + IPDM, tomato + IPDM, grafted tomato + farmers’ practice and tomato + farmers’ practice. The EG 203-grafted tomato recorded a higher yield than the farmers’ practice with significantly superior biometric parameters. The treatment of EG 203-grafted tomato and IPDM practices can be adopted for safer tomato production by enabling a reduction in pesticide applications while enhancing productivity. Full article

Throughout its history, Papua New Guinea (PNG) has faced recurrent agricultural droughts, imposing considerable strain on both livelihoods and the economy. Particularly severe droughts have been associated with El Niño climate patterns. During these episodes, PNG becomes especially vulnerable to extended periods of aridity and diminished precipitation. Historically, humanitarian assistance for these events has primarily focused on responding to emergencies after an agricultural drought has been declared and communities have already been impacted. Here, we developed a proactive agricultural drought-triggering method for anticipatory action (AA) in PNG to offer a more sustainable and cost-effective approach to address this hazard. Our AA uses weather forecasts and risk data to identify and implement mitigative actions before a disaster occurs. The research details a step-by-step guide from early warning to action implemented by the Food and Agricultural Organization of the United Nations and the Government of Papua New Guinea. This preemptive disaster risk management initiative integrates a combined drought index (CDI) with specific thresholds and tailored anticipatory actions based on crop calendars. Moreover, the developed CDI provides a 3-month lead time for implementing AA to reduce the impact of the agricultural drought. During the El Niño-induced drought event that began in 2023, the CDI was tested and the AA was piloted for the first time. Full article

Botanical gardens play a crucial role in documenting and sustaining traditional ecological knowledge (TEK) that were integral to the lives of Indigenous peoples. TEK has gained significant attention in discussions on sustainable development. Faced with threats to the maintenance and transfer of this knowledge, alternative approaches like community-based ethnobotanical gardens are emerging as effective tools for conservation. This paper details a research partnership that focused on storing and sharing the Bunun ethnic community’s TEK to conserve and promote plant and crop diversity. This collaboration further led to the co-development of an Indigenous ecological calendar detailing knowledge about crops, specifically beans. The ecological calendar emerged as an effective tool for supporting knowledge sharing, facilitating the communication of crop knowledge along with both common and scientific names. The Indigenous ecological calendar has also become a valuable tourism resource for guided tours, helping to build recognition of Indigenous knowledge, and making it accessible to future generations. Full article

In response to the adverse effects of climate change-induced frequent extreme disasters on agricultural production and supply stability, this study develops a comprehensive agricultural disaster prevention system based on current adaptation strategies for mitigating agricultural meteorological disasters. The primary goal is to enhance disaster preparedness and recovery through three core platforms: a fine-scale weather forecast service system, a crop disaster early warning system, and an agricultural information service platform for disasters. The results show that every major agricultural production township in Taiwan now has dedicated agricultural weather stations and access to refined weather forecasts. Additionally, a disaster prevention calendar for 76 important crops is established, integrating cultivation management practices and critical disaster thresholds for different growth periods. Utilizing this calendar, the crop disaster early warning system can provide timely disaster-related information and pre-disaster prevention assistance to farmers through various information dissemination tools. As a disaster approaches, the agricultural information service platform for disasters provides updates on current crop growth conditions. This service not only pinpoints areas at higher risk of disasters and vulnerable crop types but also offers mitigation suggestions to prevent potential damage. Administrative efficiency is then improved with a response mechanism incorporating drones and image analysis for early disaster detection and rapid response. In summary, the collaborative efforts outlined in this study demonstrate a proactive approach to agricultural disaster prevention. By leveraging technological advancements and interdisciplinary cooperation, the aim is to safeguard agricultural livelihoods and ensure food security in the face of climate-induced challenges. Full article

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest. Full article

Fruits, garden plants, and agricultural crops grown in Poland exhibit wide variations in their sensitivity to frost, particularly in early spring. In the case of frost, generally, the yield and quality are reduced, and sometimes, entire plants can be destroyed. This article characterizes the occurrence of ground frosts (at 5 cm agl) and air frosts (at 200 cm agl) in Poland gathered from 52 meteorological stations affiliated with IMGW-PIB between 1971 and 2020. To assess the real risk of frost to plants, the variability of this phenomenon was analyzed per thermal growing season (defined as air temperature >5 °C), rather than in traditional calendar terms as presented in most studies. In the climatic conditions of Poland, the growing season is characterized by a reported 28 days with ground frost and 13.3 days with air frost, approximately. In spring, the last ground frost disappears, on average, on a country scale, on May 14, and air frost on April 27. In turn, in autumn, the first ground frost is recorded, on average, on 1 October and air frost on 14 October. On the basis of the selected characteristics of frost and the growing season, four areas of potential risk of ground and air frost in the growing season, as well as in spring, were determined with the use of cluster analysis. Full article

There is valuable information that can be obtained beyond using a fixed crop calendar with coarse spatial resolution. Knowing the dynamics of the timing and location in which a particular crop is planted and harvested, with an annual temporal resolution and a fine spatial resolution, is crucial not only for monitoring crop conditions and production but also for understanding crop management under changing climates. In this study, the Normalized Difference Vegetation Index (NDVI) was utilized to develop a historical crop calendar for paddy in Indonesia with a 1 km resolution from 2001 to 2021. The result of this study is the first dynamic crop calendar that includes information about the planting, peak, and harvesting dates, as crop growth indicators, derived from the analysis of NDVI value fluctuations. Additionally, this dataset also includes the total number of cropping seasons each year. In Indonesia, there are intensive agricultural activities, including two dry cropping seasons that occur after the wet cropping season. However, this dataset is limited only to crops grown during the dry seasons, which typically begin in February and June. This dataset offers significant information at a finer spatiotemporal resolution to enable studies on agricultural fields undergoing climate change, although it is more country–specific than the other established dataset. The annual crop calendar dataset from 2001 to 2021 underscores the significance of examining the variability in cropping seasons over the years. This exploration aims to deepen our comprehension of the interplay between cropping seasons, climatic indicators, and even the social factors influencing farmers’ decisions. Furthermore, presented at a 1 km resolution, this dynamic crop calendar underscores the need for a more precise representation of diverse cropping intensities and seasons, particularly within small and fragmented agricultural areas. Full article