Agricultural Risk Assessment Research Articles

Geospatial Models for Predictive Agricultural Risk Assessment and Mitigation in Vulnerable Landscapes

This study addresses complex agricultural risk assessment under simplified conditions through a multi-pronged approach. The research problem focuses on the interactions among soil moisture, vegetation cover, and land use patterns influencing agricultural risks. Using mixed methods, we research soil internal analysis, mathematical modelling, and stakeholder insights. Stratified objective sampling ensures representative data sets and various geospatial tools, including Geographic Information System (GIS) software and remote sensing platforms, are subject to data analysis. Our study reveals a positive relationship between soil moisture and vegetation cover and establishes the role of highlighting the importance of water use in agricultural resilience -Use distribution analysis reveals spatial patterns, which identify targeted strategies for risk mitigation. Soil composition data enhance our understanding of soil health, providing usable insights for sustainable agriculture. These results contribute significantly to the existing body of knowledge and emphasize the importance of understanding detailed agricultural systems under sensitive conditions. Future research should examine temporal dynamics, socioeconomic implications, and adaptive geospatial models to support decision-making. Our research provides valuable insights for practitioners, policymakers and researchers and advances the understanding of agricultural risk in dynamic contexts.

Air temperature estimation over winter wheat fields by integrating machine learning and remote sensing techniques

Air temperature (Ta) is significant to numerous Earth's surface and agricultural processes. For agricultural fields (e.g., winter wheat), Ta plays an important role in crop growth monitoring, crop yield estimation, and agricultural risk assessment. In this study, a revised Ta estimation model is proposed by integrating the temperature-vegetation index (TVX) method with multiple regression models. Daily Ta from 2013 to 2021 over winter wheat fields in Henan province (the Top 1 winter wheat-producing province in China) was estimated. The revised models utilized the TVX method over regions and periods featuring extensive vegetation coverage, especially during winter wheat growing seasons, then employed three regression models including multivariate linear regression (MLR) model, random forest (RF) machine learning regression model, and deep belief network (DBN) deep learning regression model. The satellite remote sensing observations, meteorological in-situ Ta measurements, and model assimilations were integrated into the revised models to retrieve Ta. Results obtained through methods proposed in this study were calibrated and validated by in-situ Ta measurements from weather networks. The statistical results reveal that the revised methods can produce Ta estimation over winter wheat-planting areas with higher accuracy compared with traditional regression methods, with R2, MAE, and RMSE ranges from 0.915 to 0.971, 1.204 to 2.097 °C, and 1.623 to 2.911 °C, respectively. Integrating the TVX with RF has the best performance with the highest R2 (0.971), lowest MAE (1.204 °C), and lowest RMSE (1.623 °C) over the winter wheat planting area.

Agricultural risk assessment of compound dry and hot events in China

Compound dry and hot events or extremes (CDHEs) can cause reduced crop yield, posing threat to food security. Their impacts can be even higher than their univariate counterparts due to the compounding effects of the two extremes. As such, understanding the agricultural risk of CDHEs is critical for devising adaptation measures for the agricultural sector under global warming. However, previous studies mainly focus on changes in the hazard-related characteristics of CDHEs, while the risk assessment encompassing hazards, exposure, and vulnerability is still lacking. In this study, we assess changes in the risk of CDHEs to cropland in historical and future periods across the mainland of China, which incorporates cropland exposure and socio-economic vulnerability at regional scales. Along with increased hazards of CDHEs and heterogeneous changes of vulnerability, the risk of CDHEs was shown to increase across large regions of the country (e.g., Northeast China, North China, and East China) for the historical period from 1967 to 2014. Based on CMIP6 simulations, the agricultural risk of CDHEs is projected to increase in the future period 2053–2100 (by a factor of 1.3) compared with that in the historical period. This study provides a useful basis for agricultural risk assessments of CDHEs and developing adaptation measures in China under global warming.

Estimation of Rice Yield Considering Heading Stage Using Satellite Imagery and Ground-Based Data in Indonesia

Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.Understanding the temporal and spatial variability in crop yield is considered as one of the key steps in agricultural risk assessment. Therefore, a study of an irrigated area in Cihea, West Java, Indonesia, was conducted to assess rice yield per field using SENTINEL-2 imagery and yield observation data in 2018 and 2019. The study area is located in the Citarum River basin. SENTINEL-2 images were used to derive paddy rice’s growth curve and estimate rice growth stages based on the normalized difference vegetation index. Using these results, the regression model formula using Band 4 (665 nm) and the normalized difference water index in the ripening stage was created (R2 = 0.40, RMSE = 1.21 t/ha). The results from this model were used to generate yield maps, which illustrated a distinct spatial variation in rice yield, such as the average rice productivity in the study area was relatively high, however, the difference between years tended to be small in the upper stream area. The results of this study show that this method is effective in this area to monitor rice yield condition and distribution.

Agricultural risk assessment to enhance the food systems of the Mbororo minority community in the Northwest region of Cameroon

Increasing population pressure, coupled with the effects of climate change manifested by longer dry seasons, wildfires, and conflicts, threatens the food systems of minority communities. In the case of the Mbororo minority community of Northwest Region of Cameroon, these threats are exacerbated by already existing problems of marginalisation. This study assesses agricultural risks in the global context of food distribution and gross domestic product that also poses a specific threat to the food system of the Mbororo people. The study aims to (1) assess the types of agricultural risk encountered by the Mbororo community, (2) examine the likelihood and severity of these risks, and (3) appraise local risk management strategies adopted to minimise the negative impacts of these risks on the food system. The study makes use of a mixed method approach for data collection. Findings have revealed animal diseases (83 %), absence of infrastructures (83.6 %), price variation (76.6 %), and drought (75.8 %) as the most encountered risks in the case study area. Especially, drought, political insecurity, pest and rodent, farmer-grazer conflict, and crop and animal diseases emerged as the highest intensity or priority risks with the need for urgent management strategies intervention. Agricultural risk assessment is frequently used to assess and prioritize risks but has hardly been used in the case of minority groups such as the Mbororo community of Northwest Cameroon.

Risk of Chrysantemum Flower Supply Chain in Central Java Province and Yogyakarta Special Region

The distribution of chrysanthemum flowers from Bandungan District to consumers in the city can be at risk of damage. This study aims to determine the risk sources and types in the chrysanthemum supply chain; analyze risks probability and impact risk as well as risk capacity management among the chrysanthemum supply chain agents; and identify the risk priority in the chrysanthemum supply chains based on the level of loss and vulnerability. Data collection was conducted between April and September 2019 at Bandungan sub-distric Semarang distric. This study interviewed fifty farmers, fifteen middlemen, eight suppliers, and twentyfive florists. Additionally, there were also interviewed three expert regarding chrysanthemum faming. These were the leader of Astha Bunda Kalirang farmers group, the head of marketing division of Astha Bunda Kaliurang Farmers Group, and staff on BPTP in Yogyakarta. Data was analyzed using Rapid Agricultural Risk Assessment (RapAgRisk). Results showed that there were six risk sources and 23 risk types that were present along the chrysanthemum supply chain. Six risks source such as natural hazard, weather, market, management and operational, logistical and infrastructure, and plant biological and environmental. Supply chain actors who have the highest risk are at farmer level.

Analysing the decision making for agricultural risk assessment: An application of extreme value theory

As the most contributed sectors in agriculture, rice farming is facing various risks, namely uncertainty such as crop failure caused by climate change, including air temperature, weather, rainfall and others. Indonesia is categorised as an agricultural country with a tropical climate. By this season, the farmers can plant the rice. Rice farming is currently an inseparable part of most agricultural societies in Indonesia, especially in West Java. However, changes in air temperature, weather and annual rainfall, can increase the uncertainty and upward the risk of crop failure. Thus, the current study seeks to investigate the decision making for agricultural risk assessment (climate variable) through the formulation of a risk model for agricultural insurance in Indonesia. This study utilised the climate variables, which consist of air temperature, wind speed, maximum and minimum temperatures, and rainfall. For determining the magnitude of risk, we applied the Block Maxima method and Peak Over Threshold. The results of this study found that the highest risk of losses occurred in November, December, January, February and March with a value of 0.17485.

Multimedia based risk forecasting model for frequent natural disasters

Agricultural catastrophe risk assessment is not only a practical problem which needs to be solved for the stable development of China’s agriculture under the overall background of global climate change, but also a scientific issue which urgently needs to be solved for the discipline of agricultural risk assessment. At present, the academic circle mainly studies the theories and methods of agricultural catastrophe risk assessment from different perspectives based on risk factors, risk loss as well as risk mechanism, but fails to provide a clear answer to the loss degree of agricultural production caused by extreme weather events as well as the probability distribution of agricultural catastrophe loss. In view of this problem, and on the basis of modern risk analysis and assessment theory, a basic framework for agricultural catastrophe risk assessment was constructed in this thesis, and the parallel random tree algorithm was adopted to construct an agricultural economic risk prediction model in the environment of frequent natural disasters, which could be effectively applied to agricultural economic risk prevention and control.

Agricultural Worker Injury Comparative Risk Assessment Methodology: Assessing Corn and Biofuel Switchgrass Production Systems.

Keeping workers safe is a continuing challenge in agricultural production. Risk assessment methodologies have been used widely in other industries to better understand systems and enhance decision making, yet their use in production agriculture has been limited. This article describes the considerations and the approach taken to measure the difference in worker injury risks between two agricultural production systems. A model was developed specifically for the comparison of worker injury risk between corn and biofuel switchgrass production systems. The model is composed of injury and exposure values that were used in a Monte Carlo simulation. The output of this risk assessment shows that approximately 99% of the values from the Monte Carlo simulation rank corn production as a greater worker injury risk than biofuel switchgrass production. Furthermore, the greatest contributing factors for each production system were identified as harvest, and that finding aligns with current literature.

Risk Assessment of Agricultural Drought Disaster in Southern China

Risk assessment of drought disaster is necessary for the sustained agriculture development under the background of global climate change, and, meanwhile, it is an urgent scientific issue needed to be solved in agricultural risk assessment discipline. In this paper, collecting the areas covered by drought disasters, areas affected by drought disasters, areas with no harvest by drought disasters, areas planted, and yield per unit area data, agricultural drought disasters losses of the southern five provinces in China were calculated. The best probability density function was obtained according to the goodness of fit test results. Then, the value at risk (VaR) from financial market risk research method was used to the effective measure of agricultural drought risk. The results show that, in southern five provinces of our research region, risk of agricultural drought disaster in Yunnan and Guizhou is greater than other three provinces. In the scenario of 50 years and 100 years for a return, the crop loss caused by drought will reach 13.6% and 17.4% in Yunnan province, and the crop loss will be more serious in Guizhou province and be up to 15.3% and 18.1%. The above results can provide multifaceted information about drought risk that can help to guide management of drought disasters.

Assessment of Flood Catastrophe Risk for Grain Production at the Provincial Scale in China Based on the BMM Method

Flood catastrophe risk assessment is imperative for the steady development of agriculture under the context of global climate change, and meanwhile, it is an urgent scientific issue need to be solved in agricultural risk assessment discipline. This paper developed the methodology of flood catastrophe risk assessment, which can be shown as the standard process of crop loss calculation, Monte Carlo simulation, the generalized extreme value distribution (GEV) fitting, and risk evaluation. Data on crop loss were collected based on hectares covered by natural disasters, hectares affected by natural disasters, and hectares destroyed by natural disasters using the standard equation. Monte Carlo simulation based on appropriate distribution was used to expand sample size to overcome the insufficiency of crop loss data. Block maxima model (BMM) approach based on the extreme value theory was for modeling the generalized extreme value distribution (GEV) of flood catastrophe loss, and then flood catastrophe risk at the provincial scale in China was calculated. The Type III Extreme distribution (Weibull) has a weighted advantage of modeling flood catastrophe risk for grain production. The impact of flood catastrophe to grain production in China was significantly serious, and high or very high risk of flood catastrophe mainly concentrates on the central and eastern regions of China. Given the scenario of suffering once-in-a-century flood disaster, for majority of the major-producing provinces, the probability of 10% reduction of grain output is more than 90%. Especially, the probabilities of more than 15% decline in grain production reach up to 99.99, 99.86, 99.69, and 91.60% respectively in Anhui, Jilin, Liaoning, and Heilongjiang. Flood catastrophe assessment can provide multifaceted information about flood catastrophe risk that can help to guide management of flood catastrophe.

Developing higher resolution climate change scenarios for agricultural risk assessment: progress, challenges and prospects

Climate change presents perhaps the greatest economic and environmental challenge we have ever faced. Climate change and its associated impacts, adaptation and vulnerability have become the focus of current policy, business and research. This paper provides invaluable information for those interested in climate change and its impacts. This paper comprehensively reviews the advances made in the development of regional climate change scenarios and their application in agricultural impact, adaptation and vulnerability assessment. Construction of regional climate change scenarios evolved from the application of arbitrary scenarios to the application of scenarios based on general circulation models (GCMs). GCM-based climate change scenarios progressed from equilibrium climate change scenarios to transient climate change scenarios; from the use of direct GCM outputs to the use of downscaled GCM outputs; from the use of single scenarios to the use of probabilistic climate change scenarios; and from the application of mean climate change scenarios to the application of integrated climate change scenarios considering changes in both mean climate and climate variability.

Connectivity of the American Agricultural Landscape: Assessing the National Risk of Crop Pest and Disease Spread

More than two-thirds of cropland in the United States is devoted to the production of just four crop species—maize, wheat, soybeans, and cotton— raising concerns that homogenization of the American agricultural landscape could facilitate widespread disease and pest outbreaks, compromising the national food supply. As a new component in national agricultural risk assessment, we employed a graph-theoretic approach to examine the connectivity of these crops across the United States. We used county crop acreage to evaluate the landscape resistance to transmission—the degree to which host availability limits spread in any given region—for pests or pathogens dependent on each crop. For organisms that can disperse under conditions of lower host availability, maize and soybean are highly connected at a national scale, compared with the more discrete regions of wheat and cotton production. Determining the scales at which connectivity becomes disrupted for organisms with different dispersal abilities may help target rapid-response regions and the development of strategic policies to enhance agricultural landscape heterogeneity.

Assessment of the ClimGen stochastic weather generator at Cameroon sites

Simulation of agricultural risk assessment and environmental management requires long series of daily weather data for the area being modelled. Acquiring and formatting this data can be very complex and time-consuming. This has led to the development of weather generation procedures and tools. Weather generators can produce time series of synthetic weather data of any length, interpolating observed data to produce synthetic weather data at new sites. Any generator must be tested to ensure that the data that it produces is satisfactory for the purposes for which it is to be used. The aim of this paper is to test a commonly used weather generator, ClimGen (version 4.1.05) at eight sites with contrasting climates in Cameroon. Statistical test were conducted, including t-test and F-test, to compare the differences between generated weather data versus 25 years observed weather data. The results showed that the generated weather series was similar to the observed data for its distribution of monthly precipitation and its variances, monthly means and variance of minimum and maximum air temperatures. Based on the results from this study, it can be concluded that ClimGen performs well in the simulation of weather statistics in Cameroon. Key words: Weather generators, weather data, Cameroon, climate change.

CLIMGEN-UP: A user-friendly weather data generator

User-friendly Windows software, CLIMGEN-UP, was developed based on the CLIMGEN weather data generator. CLIMGEN-UP calculates and stores monthly statistical parameters, based on historic data of daily rainfall, minimum and maximum temperature. It can then generate and store long-series of daily rainfall, minimum and maximum temperature, based on the monthly statistical parameters. Historic and generated data can be displayed graphically. CLIMGEN-UP is written in Delphi v. 5.0 and makes use of a Paradox database. The program is available for use with Windows 98/ME/2000 and NT on an IBM-PC or compatible computer, and is supplied as executable code on 3.5″ disk. Possible applications are in hydrology, environmental and ecosystem management as well as agricultural risk assessment where long-term simulations of system behaviour are required.