Agricultural Management Strategies Research Articles

Drought risk in Moldova under global warming and possible crop adaptation strategies.

This study analyzes the relationship between drought processes and crop yields in Moldova, together with the effects of possible future climate change on crops. The severity of drought is analyzed over time in Moldova using the Standard Precipitation Index, the Standardized Precipitation Evapotranspiration Index, and their relationship with crop yields. In addition, rainfall variability and its relationship with crop yields are examined using spectral analysis and squared wavelet coherence. Observed station data (1950-2020 and 1850-2020), ERA5 reanalysis data (1950-2020), and climate model simulations (period 1970-2100) are used. Crop yield data (maize, sunflower, grape), data from experimental plots (wheat), and the Enhanced Vegetation Index from Moderate Resolution Imaging Spectroradiometer satellites were also used. Results show that although the severity of meteorological droughts has decreased in the last 170 years, the impact of precipitation deficits on different crop yields has increased, concurrent with a sharp increase in temperature, which negatively affected crop yields. Annual crops are now more vulnerable to natural rainfall variability and, in years characterized by rainfall deficits, the possibility of reductions in crop yield increases due to sharp increases in temperature. Projections reveal a pessimistic outlook in the absence of adaptation, highlighting the urgency of developing new agricultural management strategies.

Financial risk management in agriculture: mitigation strategies and protection against economic variables

This work explores essential strategies for managing financial and operational risks in the agricultural sector, a crucial pillar of the global economy, but one that is exposed to a wide range of financial risks. These risks include fluctuations in commodity prices, access to credit, operational risks, climate change, exchange rate variations, and government policies. To ensure the sustainability and profitability of agricultural operations, it is imperative that producers adopt effective financial risk management strategies. This article addresses the main mitigation and protection strategies against economic variables in the context of agriculture. The methodology of this study is based on a systematic literature review, focused on identifying, analyzing, and synthesizing financial risk management strategies in agriculture. The review covers the analysis of advanced precision technologies, which optimize agricultural production and reduce waste, as well as sustainable agricultural practices, adequate insurance to mitigate the impacts of climate risks, and commodity hedging. Strategies for diversifying crops and agricultural activities to distribute risk and improve financial resilience are also discussed. In conclusion, effective financial risk management in agriculture requires a multifaceted approach that integrates mitigation strategies for various types of risks. The adoption of advanced technologies, sustainable agricultural practices, adequate insurance, and a thorough understanding of market and policy dynamics are fundamental to strengthening resilience and ensuring the sustainability of the agricultural sector against adverse economic variables.

Altering cold-regulated gene expression decouples the salicylic acid-growth trade-off in Arabidopsis.

In Arabidopsis (Arabidopsis thaliana), overproduction of salicylic acid (SA) increases disease resistance and abiotic stress tolerance but penalizes growth. This growth-defense trade-off has hindered the adoption of SA-based disease management strategies in agriculture. However, investigation of how SA inhibits plant growth has been challenging because many SA-hyperaccumulating Arabidopsis mutants have developmental defects due to the pleiotropic effects of the underlying genes. Here, we heterologously expressed a bacterial SA synthase gene in Arabidopsis and observed that elevated SA levels decreased plant growth and reduced the expression of cold-regulated (COR) genes in a dose-dependent manner. Growth suppression was exacerbated at below-ambient temperatures. Severing the SA-responsiveness of individual COR genes was sufficient to overcome the growth inhibition caused by elevated SA at ambient and below-ambient temperatures while preserving disease- and abiotic-stress-related benefits. Our results show the potential of decoupling SA-mediated growth and defense trade-offs for improving crop productivity.

Assessment of Advanced Machine and Deep Learning Approaches for Predicting CO2 Emissions from Agricultural Lands: Insights Across Diverse Agroclimatic Zones

AbstractPrediction of carbon dioxide (CO2) emissions from agricultural soil is vital for efficient and strategic mitigating practices and achieving climate smart agriculture. This study aimed to evaluate the ability of two machine learning algorithms [gradient boosting regression (GBR), support vector regression (SVR)], and two deep learning algorithms [feedforward neural network (FNN) and convolutional neural network (CNN)] in predicting CO2 emissions from Maize fields in two agroclimatic regions i.e., continental (Debrecen-Hungary), and semi-arid (Karaj-Iran). This research developed three scenarios for predicting CO2. Each scenario is developed by a combination between input variables [i.e., soil temperature (Δ), soil moisture (θ), date of measurement (SD), soil management (SM)] [i.e., SC1: (SM + Δ + θ), SC2: (SM + Δ), SC3: (SM + θ)]. Results showed that the average CO2 emission from Debrecen was 138.78 ± 72.04 ppm (n = 36), while the average from Karaj was 478.98 ± 174.22 ppm (n = 36). Performance evaluation results of train set revealed that high prediction accuracy is achieved by GBR in SC1 with the highest R2 = 0.8778, and lowest root mean squared error (RMSE) = 72.05, followed by GBR in SC3. Overall, the performance MDLM is ranked as GBR > FNN > CNN > SVR. In testing phase, the highest prediction accuracy was achieved by FNN in SC1 with R2 = 0.918, and RMSE = 67.75, followed by FNN in SC3, and GBR in SC1 (R2 = 0.887, RMSE = 79.881). The performance of MDLM ranked as FNN > GRB > CNN > SVR. The findings of the research provide insights into agricultural management strategies, enabling stakeholders to work towards a more sustainable and climate-resilient future in agriculture.

Impacts of Calcium Cyanamide Application as a Nitrogen Source on Growth, Yield, Quality, and Storage Durability of Short-day Onion

Rapid leaching of soluble nitrogen (N) sources in soil poses a significant challenge in agricultural practices. Therefore, gaining a comprehensive understanding of crop responses to slow-release N application rates has become crucial to contributing valuable insights to optimize N management strategies in agriculture. A field study was conducted to investigate the influence of preplant calcium cyanamide fertilizer on the growth, yield, quality, and shelf life of short-day onion. Six levels of calcium cyanamide (CaCN2, 19.8% N), 0, 90, 120, 200, 400, and 600 kg⋅ha−1 CaCN2, which are equivalent to 0, 17.82, 23.76, 39.6, 79.2, and 118.8 kg⋅ha−1 N, respectively, replicated four times were broadcasted and incorporated into the top 5 to 10 cm of soil. Using 400 kg⋅ha−1 of CaCN2 yielded noteworthy improvements in various parameters of onion growth, such as plant height, leaf count, bulb weight per plant, bulb diameter, bulb length, and overall plant weight, as indicated by the study results. The application of different levels of CaCN2 as an N source exerted a significant influence on these growth factors. Moreover, the study revealed a direct correlation between CaCN2 application levels and the storage life of onions. Specifically, the findings demonstrated that the application of 400 kg⋅ha−1 CaCN2 resulted in enhanced yield and overall onion plant growth. However, the application of 600 kg⋅ha−1 CaCN2 increased the incidences of bulb weight loss, rots, and sprouting during the 8-week storage period at room temperature. These findings provide valuable insights for onion investors and farmers in the region and offer practical recommendations for optimizing fertilizer use and storage practices to improve onion production and minimize postharvest losses.

Spatial and temporal dynamic zoning of crop water consumption under past and future climate and socioeconomic changes in China

The impacts of multiple changes in climatic and socioeconomic conditions within countries and regions are spatially heterogeneous, thus complicating stringent agricultural water management. Here we developed a framework for the dynamic identification of zone types and provided targeted agricultural water management strategies for each zone in response to global change. Considering China as an example, eight zones of typical major grain crop production prefectures were identified based on an analysis of the spatial and temporal evolution characteristics at four levels (agricultural, natural, social, and economic) according to the water footprint of major grain crop production at the prefecture-level for the past 15 years (2004–2018). We then presented the response of China's future zoning landscape for 2030, 2050, and 2080 under three representative scenarios by combining shared socioeconomic paths (SSPs) and representative concentration paths (RCPs). Results show that, by 2080, the national water consumption of the major grain crop production will increase under all scenarios. Half of the prefectures facing function shifts are likely to change from low to high water-consuming zones. Different zonal prefectures react differently under global changes, especially those that are prone to functional transformation, and should pay attention to their instability. Consideration of the water footprint in agricultural zoning is of great importance for national sustainable water resources management. This study proposes a more explicit approach to coping with global change, that is, to propose regionalized and prior agricultural water management strategies and measures for China and beyond.

Multi-criteria GIS-based land suitability analysis for rice cultivation: a case study in Guilan Province, Iran.

Ensuring food security and sustainable resource management has become a paramount global concern, prompting significant attention to land suitability analysis for enhancing agricultural production. In this study, an AHP-weighted overlay method was employed to delineate rice cultivation suitability in Guilan province, Iran, a central hub for rice production. Sixteen climatic, topographic, and soil variables were integrated, and individual maps were reclassified to align with the specific requisites for rice production. The results revealed three suitability classes: including 'very suitable,' 'suitable,' and 'moderately suitable', covering 91%, 6%, and 3% of the land, respectively. Soil attributes, particularly organic matter, significantly influenced suitability (weight value of 0.745), with topographic and soil factors outweighing climate in assessment. While salinity is generally absent, organic matter deficiency affects 44% of the land. Phosphorus imbalances are prevalent, with potassium toxicity observed in 10%. Microelement deficiencies, especially in iron and zinc, are noted. Additionally, the results indicated that topographic and soil attributes played a more significant role than climate-related factors in assessing land suitability for rice cultivation within the study area. This research provides a comprehensive spatial analysis of all variables in the study region, shedding light on the complexities of land suitability for rice cultivation. These findings contribute to the understanding of agricultural sustainability and resource management strategies in the context of food security.

Can Farmland Transfer Reduce Fertilizer Nonpoint Source Pollution? Evidence from China

China repeatedly surpasses international fertilizer safety limits, resulting in significant fertilizer nonpoint source pollution (denoted as FNSP), which adversely affects food security and agricultural sustainability. Simultaneously, farmland transfer has emerged as a pivotal strategy for transitioning between agricultural production methods. The present study aims to investigate the relationship between farmland transfer and FNSP. In line with the aim of the study, based on China’s panel data from 2005 to 2020, the fixed-effect model, mediating-effect model, spatial Durbin model, and threshold regression model are employed. The findings reveal that farmland transfer exerts a significant inhibitory effect on FNSP. The reduction in FNSP through farmland transfer is facilitated by the decrease in fertilizer application intensity and increase in compound fertilizer application. Further, farmland transfer demonstrates a significant spatial spillover effect on FNSP, mitigating pollution levels within regions and influencing neighboring areas. Moreover, a nonlinear relationship between farmland transfer and FNSP is observed. These findings contribute to understanding the intricate dynamics between agricultural land management strategies and environmental sustainability, offering valuable insights for policymakers and stakeholders engaged in promoting green and sustainable agricultural practices.

Animal Detection based Smart Farming in Animal Repellent Using AI and Deep Learning

The combination of artificial intelligence (AI) and deep learning in agriculture has ushered in a new era in agriculture with new solutions designed to solve many problems. This paper presents an animal killing system for smart agriculture that uses artificial intelligence and deep learning to reduce the growing problem of animal damage. As the world population continues to grow, increasing food availability is important; Therefore, it is critical to protect crops from wild animals and pests. Deforestation due to livestock farming has become one of the largest human-wildlife conflicts due to human interference with habitats and deforestation. Wild animals can kill farmers working in the fields, causing major crop losses. Farmers suffered huge crop losses due to wild animals such as elephants, wild boars and deer attacking agriculture. Protecting crops from wild animals is one of the biggest concerns of today's farmers. There are many ways to solve this problem, both lethal (such as shooting and trapping) and non-lethal. (such as railings, pesticides, organic matter, netting or electric fencing). The sensor rotates around the lens and uses DCNN software to detect the target. If an animal is found, it sends a message to the Animal Repellent Module with information on the type of ultrasound that should be produced based on the animal. The development of drones with controlled flight control, as well as lightweight and powerful hyperspectral snapshot cameras that can be used to calculate crop biomass growth and fertilization status, responds to complex agricultural management strategies. KEYWORDS: Animal detection, VGG-Net, Bi-LSTM, convolutional neural network, activity recognition, video surveillance, wild animal monitoring, alert system.

Developing agricultural pest management strategies with reduced-risks to surface water: An economic case study of California's Central Coast region

Pesticides are critical for protecting agricultural crops, but the off-site transport of these materials via spray drift and runoff poses risks to surface waters and aquatic life. California's Central Coast region is a major agricultural hub in the United States characterized by year-round production and intensive use of pesticides and other chemical inputs. As a result, the quality of many waterbodies in the region has been degraded. A recent regulatory program enacted by the Central Coast Regional Water Quality Control Board set new pesticide limits for waterways and imposed enhanced enforcement mechanisms to help ensure that water quality targets are met by specific dates. This regulatory program, however, does not mandate specific changes to pest management programs. In this study, we evaluate the economic, environmental, and pest management impacts of adopting two alternative pest management programs with reduced risks to surface water: 1) replacing currently used insecticide active ingredients (AIs) that pose the greatest risk to surface water with lower-risk alternatives and 2) converting conventional arthropod pest management programs to organic ones. We utilize pesticide use and toxicity data from California's Department of Pesticide Regulation to develop our baseline and two alternative scenarios. We focus on three crop groups (cole crops, lettuce and strawberry) due to their economic importance to the Central Coast and use of high-risk AIs. For Scenario 1, we estimate that implementing the alternative program in the years 2017–2019 would have reduced annual net returns on average by $90.26 – $190.54/ha, depending on the crop. Increased material costs accounted for the greatest share of this effect (71.9%–95.6%). In contrast, Scenario 2 would have reduced annual net returns on average by $5,628.12 – $18,708.28/ha during the study period, with yield loss accounting for the greatest share (92.8–97.9%). Both alternative programs would have reduced the associated toxic units by at least 98.1% compared to the baseline scenario. Our analysis provides important guidance for policymakers and agricultural producers looking to achieve environmental protection goals while minimizing economic impacts.

Redox signalling in plant-nematode interactions: Insights into molecular crosstalk and defense mechanisms.

Plant-parasitic nematodes, specifically cyst nematodes (CNs) and root-knot nematodes (RKNs), pose significant threats to global agriculture, leading to substantial crop losses. Both CNs and RKNs induce permanent feeding sites in the root of their host plants, which then serve as their only source of nutrients throughout their lifecycle. Plants deploy reactive oxygen species (ROS) as a primary defense mechanism against nematode invasion. Notably, both CNs and RKNs have evolved sophisticated strategies to manipulate the host's redox environment to their advantage, with each employing distinct tactics to combat ROS. In this review, we have focused on the role of ROS and its scavenging network in interactions between host plants and CNs and RKNs. Overall, this review emphasizes the complex interplay between plant defense mechanism, redox signallingand nematode survival tactics, suggesting potential avenues for developing innovative nematode management strategies in agriculture.

MARKETING MANAGEMENT OF AGRICULTURAL BUSINESS: CHALLENGES OF DIGITALIZATION

The article analyzes the impact of digitalization processes on the agricultural sector and the challenges faced by enterprises in this industry. The author analyzes the impact of external and internal environmental factors on the development of agricultural businesses and the problems that delay the development of digital technologies in agriculture. It is noted that an important element of marketing management is a systematic approach that allows establishing relationships between these factors, assessing their impact and formulating marketing management strategies. Based on the results of the study, the author substantiates the need to create favorable conditions for the introduction of digital tools in the marketing management of agricultural businesses. The results of the analysis also indicate the potential of using innovative marketing methods, such as analyzing market trends and using digital communication channels, to overcome the challenges of digitalization and increase the competitiveness of the agricultural sector. The prospects for the development of digitalization processes in the marketing management of agricultural enterprises are considered. It is noted that through systematic market analysis, budget management, and the use of digital technologies and communication channels, enterprises can skillfully navigate market fluctuations while optimizing operational resources. The study provides the conclusions necessary for the further development of marketing management strategies in agriculture, taking into account the current challenges of digitalization and the needs of agricultural businesses in Ukraine. It is stated that the prospects for the development of digitalization processes in agrarian business emphasize the importance of integrating marketing management into the strategic planning of agricultural enterprises and its potential to increase the efficiency of innovation in this sector.

Environmentally Friendly Strategies for Recycling Agricultural Waste to Produce Renewable Energy: A Case Study of Durian Fruit

The increasing need for harvests, resources, and facilities is associated with significant biowaste production from agricultural processes. Although the waste is nutrient-rich, it may become refinement lands for ailment-triggering bacteria when not managed correctly. The waste can be transformed into raw materials for valuable crops or sources of environmentally friendly energy. Therefore, this study examined agricultural waste management strategies as Indonesia's sustainable renewable energy source. SWOT and TOPSIS methods were used to identify the optimal approach for advancing renewable energy in Riau Province, while multiple respondents participated in identifying critical criteria and evaluating each option. The results showed that based on SWOT analysis, the Strength–Opportunity (SO) factor favored using agricultural waste for renewable energy in Indonesia. Furthermore, TOPSIS analysis indicated that Alternative A2 (Bioethanol) had the most significant distance among the alternatives, with a weight of 0.825. Future studies are needed to provide more accurate results and improve the current understanding regarding the evolution of renewable energy in Indonesia. Additionally, in-depth investigations should prioritize increased consumer awareness of renewable consumption, higher producer productivity, and strengthened policies.

Optimizing agricultural management in China for soil greenhouse gas emissions and yield balance: A regional heterogeneity perspective

Balancing soil greenhouse gas emissions (SGE) with crop production is crucial for sustainable agriculture in China. Optimizing field management practices regionally can effectively promote carbon reduction and food security, given the varying SGE responses across agroecological regions. This study employed meta-analysis and machine learning techniques to assess SGE responses to nine different farming practices in seven distinct agroecological regions. It aimed to identify optimal management strategies for balancing SGE and crop production within each region. Findings underscore substantial spatial heterogeneity in the effects of management practices on SGE, with the combined use of organic and inorganic fertilizers (MOF-MF) demonstrating the most pronounced variability (standard deviation, SD = 1.80), where MOF resulted in SGE 610% of that with MF in the Southern China (SC) region, yet only 54% in Northwestern China (NWC). Conversely, straw incorporation exhibited the most minor variability (SD = 0.27), influencing SGE by 35%–62%. Additionally, the study reveals that soil and climatic conditions significantly modulate SGE responses. In scenarios characterized by humid and warm climates with soil pH ranging from 5.5 to 8.5 or initial SOC ≤10, the environmental impact on SGE was marginal. However, in highly acidic or alkaline soils or with initial SOC >10, SGE responses varied markedly with management practices in arid and cold conditions. The study further proposes agricultural management strategies to achieve the lowest SGE while maximizing crop yield. It recommends specific methods for fertilizer applications, straw management, irrigation, and tillage practices meticulously tailored to the unique conditions of each agroecological region. This research offers essential insights for agricultural stakeholders regarding cultivation strategies and delivers theoretical guidance for enhancing agricultural management and land-use planning throughout China's heterogeneous landscapes.

Nitrogen dynamics as a function of soil types, compaction, and moisture.

In this study, the complex interactions between soil types, compaction, and moisture on nitrogen (N) transformation processes such as ammonia (NH3) volatilization, ammonification, nitrification, and denitrification were examined over a 30-day period using a simulated column approach. Two soil types: loam, and sandy loam, were subjected to three compaction treatments-control, surface, and sub-surface compaction-and two moisture regimes, dry and wet. Liquid urea ammonium nitrate (32-0-0) was used as the N fertilizer source at a rate of 200 kg N ha-1. Key indicators of N transformations were measured, including residual concentrations of ammonium (NH4-N) and nitrate (NO3-N), NO3-N leaching, NH3 volatilization, and nitrous oxide (N2O) emissions. Findings revealed that compaction significantly increased residual NH4-N concentrations in deeper soil profiles, with the highest 190.80 mg kg-1 recorded in loam soil under sub-surface compaction and dry conditions. Nitrification rates decreased across both soil types due to compaction, evidenced by elevated residual NH4-N levels. Increased NO3-N leaching was observed in loam soil (178.06 mg L-1), greater than sandy loam (81.11 mg L-1), due to initial higher residual NO3- in loam soil. The interaction of compaction and moisture most affected N2O emissions, with the highest emissions in control treatments during dry weather at 2.88 kg ha -1. Additionally, higher NH3 volatilization was noted in moist sandy loam soil under control conditions at 19.64 kg ha -1. These results highlight the necessity of considering soil texture, moisture, and compaction in implementing sustainable N management strategies in agriculture and suggest recommendations such as avoiding broadcast application in moist sandy loam and loam soil to mitigate NH3 volatilization and enhance N use efficiency, as well as advocating for readjustment of fertilizer rate based on organic matter content to reduce potential NO3-N leaching and N2O emissions, particularly in loam soil.

Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image

The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.

Identification and characterization of the critical genes encoding Cd-induced enhancement of SOD isozymes activities in Zhe-Maidong (Ophiopogon japonicus).

Superoxide dismutase (SOD) protects plants from abiotic stress-induced reactive oxygen species (ROS) damage. Here, the effects of cadmium (Cd) exposure on ROS accumulation and SOD isozymes, as well as the identification of significant SOD isozyme genes, were investigated under different Cd stress treatments to Zhe-Maidong (Ophiopogon japonicus). The exposure to Cd stress resulted in a notable elevation in the SOD activity in roots. Cu/ZnSODa and Cu/ZnSODb were the most critical SOD isozymes in response to Cd stress, as indicated by the detection results for SOD isozymes. A total of 22 OjSOD genes were identified and classified into three subgroups, including 10 OjCu/ZnSODs, 6 OjMnSODs, and 6 OjFeSODs, based on the analysis of conserved motif and phylogenetic tree. Cu/ZnSOD-15, Cu/ZnSOD-18, Cu/ZnSOD-20, and Cu/ZnSOD-22 were the main genes that control the increase in SOD activity under Cd stress, as revealed via quantitative PCR and transcriptome analysis. Additionally, under various heavy metal stress (Cu2+, Fe2+, Zn2+, Mn2+), Cu/ZnSOD-15, Cu/ZnSOD-18, and Cu/ZnSOD-22 gene expression were significantly upregulated, indicating that these three genes play a critical part in resisting heavy metal stress. The molecular docking experiments performed on the interaction between oxygen ion (O2•-) and OjSOD protein have revealed that the critical amino acid residues involved in the binding of Cu/ZnSOD-22 to the substrate were Pro135, Ile136, Ile140, and Arg144. Our findings provide a solid foundation for additional functional investigations on the OjSOD genes, as well as suggestions for improving genetic breeding and agricultural management strategies to increase Cd resistance in O. japonicus.

A novel Nash-based low-carbon implementation in agricultural supply chain management

The transition to low-carbon agriculture is seen as essential for accelerating the implementation of ecologically friendly and climate-smart agricultural supply chain management strategies. Success in these efforts depends on robust policy frameworks, effective stakeholder participation, and the availability of agricultural products to customers. This study introduces a conceptual and operational structure for the incorporation and management of an agricultural supply chain with a low-carbon scheme through the establishment of a collaborative market environment involving farmers, agricultural enterprises, and consumers. Within this framework, agricultural enterprises and farmers engage in a cooperative Nash game to determine the extent to which they are willing to employ low-carbon practices in response to collaborative market prices and government policies, including subsidies and carbon taxes. The Nash game yields a unique Nash equilibrium, suggesting the possibility of cooperative negotiations among the parties with the goal of improving their financial gains. The proposed model is applied in two Brazilian sugarcane supply chain case studies, integrating with traditional biorefinery mills to produce and market ethanol as a gasoline alternative. Our results suggest that (1) successfully allocating government subsidies and taxes motivates low-carbon adoption; (2) the government taxation policy stabilizes the low-carbon supply chain, improving stakeholder market pricing and consumer affordability; and (3) reduced cost gaps between low-carbon and non-low-carbon alternatives increase low-carbon adoption. As a result, this study provides fresh insights on the Nash collaboration of stakeholders in accordance with government policies to create a cleaner and more affordable agricultural supply chain market.

Towards Sustainable Agriculture: A Critical Analysis of Agrobiodiversity Assessment Methods and Recommendations for Effective Implementation

Agriculture intensification has driven the loss of biodiversity at a global level. The implementation of strategies to conserve and promote biodiversity in agricultural areas can be favoured by adequate assessment methods that foster the awareness of decision makers about the impact of management practices. This paper presents a state-of-the-art review of assessment methods of the overall biodiversity in agricultural systems, focusing on the quantitative methods applied, indicators of biodiversity, and functionalities. It was concluded that compensation effects and difficulties in interpretation are associated with currently common methodologies of composite indicator calculation to assess biodiversity performance. This review allowed for the identification and critical analysis of current methodologies for biodiversity assessments in the agricultural sector, and it highlighted the need for more implementation-oriented approaches. By providing recommendations on what should be considered when formulating biodiversity assessment methods, this study can contribute to the formulation of appropriate assessment frameworks for agricultural management policies and strategies.

Infrared Thermography Monitoring of Durum and Common Wheat for Adaptability Assessing and Yield Performance Prediction.

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p < 0.001 and p < 0.05), providing interesting information for their early prediction.