Market is the structure for the development and delivery of innovations that are able to address environmental, societal, and economic challenges. The lack of enabling conditions for market development has resulted in low investment levels and economic stagnation, impacting livelihoods in Africa. Although there have been efforts to implement market-driven reforms, challenges such as inadequate policies, weak legal frameworks, transparency issues and bureaucratic inefficiencies pose significant risks for public and private investments and for their potential to reach the target beneficiaries. This situation also discourages development partners and businesses from investing in the region.Technical assistance is crucial to improve the investment climate. This paper presents a framework to help governments create a more conducive environment for agricultural market development and the private sector to navigate through the existing challenges. Traditional technical assistance practices have faced criticism for adopting a one-size-fits-all approach that overlooks local contexts. Recently, however, there has been a shift towards more context-based and adaptive assistance, which informs this framework. This framework emphasizes key elements that contribute to an enabling environment, including institutions, such as policies, regulations, and legal frameworks, as well as clear market and regulatory information that help reduce transaction costs. The framework is theoretically based on new institutional economics and political economy approaches. It focuses on assistance in three areas with three categories of delivery partners: policy support to governments, institutional capacity strengthening (especially of National Agricultural Research and Extension Systems) and (agri)business acceleration support to small- and medium-scale enterprises. Through such assistance, this framework seeks to help create an enabling environment for the delivery of innovations that offer solutions to emerging climate, societal and economic crises. These solutions, especially those developed and scaled by the private sector, are targeted toward recipients such as farmers (including women and the youth), marginalized groups, displaced communities, refugees and migrants. The framework utilizes value chain and market development as the primary delivery structures. This framework has guided several recent enabling environment assistance practices under CGIAR’s International Water Management Institute (IWMI). This paper explores these practices and positions CGIAR as a strong technical assistance partner. While this framework offers a systematic approach to analyzing the enabling environment, the technical assistance driven by this framework promotes collaboration and co-creation. It actively engages governments, national research and extension offices, farmers and other stakeholders in influencing policies and business transaction advisories that directly benefit them. Furthermore, it aims to strengthen their capacities to diagnose and overcome enabling environment challenges as they arise. By helping to create an enabling environment for the private sector—especially small- and medium-scale enterprises that innovate and scale—and derisking the investment climate, this framework seeks to strengthen agrifood market systems to foster food security and alleviate poverty.

This study explored the determinants of scientific attitude among undergraduate agriculture students in Telangana State of India, analyzing their attitude levels and demographic influences. A sample of 250 B.Sc. Agriculture students from Professor Jayashankar Telangana Agricultural University was surveyed using random sampling. Factor analysis, normal probability curve, and inferential analysis revealed that most students exhibited a moderate scientific attitude, with significant differences based on gender, age, and parental occupation. Male students and those below 19 showed higher engagement, while students from government-employed families had the highest scores. Findings suggested the need for targeted educational strategies, including curiosity-driven learning, hands-on experiments, gender equity, early exposure, and infrastructure upgrades, to enhance scientific attitudes in agricultural education.

Optimal water resource allocation in agricultural irrigation districts constitutes a core strategy for achieving coordinated regional water–food–ecosystem development. However, current studies rarely integrate inter-basin water diversion projects into the allocation, and the prolonged operation of diversion systems fails to adequately consider their ecological impacts in the irrigation districts. This study incorporates inter-basin water diversion into supply–demand dynamics and considers its influence on groundwater table changes in terrestrial ecological targets. Inexact two-stage stochastic programming (ITSP) was applied for optimal water allocation to address uncertainties from fluctuations in future water availability and interval ambiguity in socioeconomic information. Taking the densely populated agricultural irrigation district of Huaibei as a case study, we established a multi-stakeholder allocation model, considering the Yangtze-to-Huai water diversion project, to maximize comprehensive benefits under multiple scenarios of water availability for the years of 2030 and 2040. The results demonstrate that the district will face escalating water scarcity risks, with demand–supply gaps widening when available water resources decrease. The water redistribution in the second stage reduces scarcity-induced losses, achieving maximum comprehensive benefits. The water diversion project enhances supply capacity and boosts economic gains. The project can also decrease the fluctuation range of the total benefits by 5 × 10⁶ CNY (2030) and 3.4 × 10⁷ CNY (2040), compared with the scenario without the project. From 2030 to 2040, limited water resources will progressively shift toward sectors with higher economic output per unit water, squeezing agricultural allocations. Therefore, for irrigation districts in developing countries, maintaining a minimum guaranteed rate of agricultural water proves critical to safeguarding food security.

European agriculture must transform to confront the many challenges it faces, yet there are different sets of values that may underpin future agricultural change. However, we currently lack thorough understanding of the implications of operationalising these plural values for European land systems. In this article, we apply the IPBES Nature Futures Framework to develop a set of three land-change scenario narratives—the Agricultural Futures Framework (AFF)—representing the diverse ways that humans value agricultural land systems: Land for Food and Land for Nature, Land as Culture, and Land for Society. We operationalise the AFF scenarios in the CLUMondo land-change model and simulate how these alternative value perspectives could reshape land systems in Europe by 2050, accounting for future demands for food production, carbon sequestration, nature restoration, and nitrogen emission reduction. We find that significant land-system reconfiguration would be needed to fulfil these multiple demands under all three scenarios, ranging from 18.8% to 37.1% of all European land, with multiple scenarios showing multifunctional pathways in Eastern Romania and Bulgaria, productivist pathways in Poland and Slovakia, and marginalisation/rewilding pathways in Central France and Northern Spain. However, the magnitude and spatial locations of change differ substantially across the prioritised value perspectives, suggesting that these may have large implications for future agricultural development. The AFF framework and our findings are useful for identifying opportunities to reconcile the divisions in values perspectives, and to prioritise interventions for agri-food transformations.

One of the cornerstones of a developed economy is undoubtedly the agricultural sector. Agriculture is at the center of both human nutrition and economic activities. The use of drones, especially in the process of spraying fields, has the potential to increase the efficiency of agricultural production. Drones can be programmed to scan the field and spray pesticides on these areas. This allows farmers to manage the processes of protecting their crops and removing pests more effectively. Spraying with drones minimizes the negative effects encountered in spraying with tractor-drawn machines. Drones can easily reach places that tractor-drawn machine cannot reach and can spray more precisely. In addition, thanks to drones, the need for labor is also reduced, so that one person can spray a large area in a short time. In this study, the process of spraying with drones was observed in detail by a company with an unmanned aerial vehicle-2 (UAV) license for agricultural spraying. This modern spraying method using drones was meticulously evaluated step by step. In traditional methods, farmers or workers may be directly exposed to pesticides while spraying with tractor-drawn machines, but thanks to drones, this exposure is minimized, which provides a great advantage in terms of occupational health and safety (OHS). In addition, the speed of the work process, less use of water and pesticides, and the need for labor are among the advantages. However, the problem of not being able to connect to GPS, accidents that may occur under the command of the drone, and limitations such as adverse weather conditions can be considered disadvantages of drone spraying. The findings reveal how drone spraying has transformed agriculture.

One of the cornerstones of a developed economy is undoubtedly the agricultural sector. Agriculture is at the center of both human nutrition and economic activities. The use of drones, especially in the process of spraying fields, has the potential to increase the efficiency of agricultural production. Drones can be programmed to scan the field and spray pesticides on these areas. This allows farmers to manage the processes of protecting their crops and removing pests more effectively. Spraying with drones minimizes the negative effects encountered in spraying with tractor-drawn machines. Drones can easily reach places that tractor-drawn machine cannot reach and can spray more precisely. In addition, thanks to drones, the need for labor is also reduced, so that one person can spray a large area in a short time. In this study, the process of spraying with drones was observed in detail by a company with an unmanned aerial vehicle-2 (UAV) license for agricultural spraying. This modern spraying method using drones was meticulously evaluated step by step. In traditional methods, farmers or workers may be directly exposed to pesticides while spraying with tractor-drawn machines, but thanks to drones, this exposure is minimized, which provides a great advantage in terms of occupational health and safety (OHS). In addition, the speed of the work process, less use of water and pesticides, and the need for labor are among the advantages. However, the problem of not being able to connect to GPS, accidents that may occur under the command of the drone, and limitations such as adverse weather conditions can be considered disadvantages of drone spraying. The findings reveal how drone spraying has transformed agriculture.

The connectivity of forest ecosystems is increasingly recognized as a key factor in evaluating the sustainability of forest management, with significant implications for biodiversity conservation. This study examines the impact of afforestation programs on forest evolution, fragmentation, and connectivity in León province, Spain, over the past 25 years (1996–2020). Three scenarios were modeled across two periods (1996–2006 and 2006–2020), integrating data from the national forest inventories (IFN2, IFN3, and IFN4) and afforestation program records provided by the Junta de Castilla y León. The evolution of connectivity “with” and “without” afforestation was analyzed using Graphab 2.6 and graph theory, and several connectivity metrics were calculated. The first period analyzed, influenced by the two initial afforestation programs, corresponded to the end of a forest expansion phase, followed by a decrease in tree cover. Despite this reduction, a net positive balance of up to 24% of all connectivity metrics (NC, PC, Flux, and ECA) was observed throughout the study period. Afforestation in mountain areas enhanced tree cover continuity, resulting in a more homogeneous but less diverse landscape. Conversely, afforestation in agricultural lands increased landscape heterogeneity, diversifying and extending the ecological network of connections. These programs have played a crucial role in shaping the landscape, influencing its diversity and the evolution of forest connectivity. Legislation grounded in technical and ecological principles should be prioritized as a strategic tool to address pressing land management challenges and preserve natural values.

The connectivity of forest ecosystems is increasingly recognized as a key factor in evaluating the sustainability of forest management, with significant implications for biodiversity conservation. This study examines the impact of afforestation programs on forest evolution, fragmentation, and connectivity in León province, Spain, over the past 25 years (1996–2020). Three scenarios were modeled across two periods (1996–2006 and 2006–2020), integrating data from the national forest inventories (IFN2, IFN3, and IFN4) and afforestation program records provided by the Junta de Castilla y León. The evolution of connectivity “with” and “without” afforestation was analyzed using Graphab 2.6 and graph theory, and several connectivity metrics were calculated. The first period analyzed, influenced by the two initial afforestation programs, corresponded to the end of a forest expansion phase, followed by a decrease in tree cover. Despite this reduction, a net positive balance of up to 24% of all connectivity metrics (NC, PC, Flux, and ECA) was observed throughout the study period. Afforestation in mountain areas enhanced tree cover continuity, resulting in a more homogeneous but less diverse landscape. Conversely, afforestation in agricultural lands increased landscape heterogeneity, diversifying and extending the ecological network of connections. These programs have played a crucial role in shaping the landscape, influencing its diversity and the evolution of forest connectivity. Legislation grounded in technical and ecological principles should be prioritized as a strategic tool to address pressing land management challenges and preserve natural values.

Context: In the dynamic and constantly evolving world of agriculture, promoting innovation and ensuring sustainable growth are crucial. A planned division of tasks and responsibilities within agricultural systems, known as efficient role allocation, is necessary to make this vision a reality. Climate-smart agriculture (CSA) movement enjoys widespread support from the research and development community because it seeks to improve livelihoods in response to climate change. Objective: This study explores an innovative approach to optimizing role assignment within agricultural frameworks to effectively scale AI-driven innovations. By leveraging advanced algorithms and machine learning techniques, the research aims to streamline the allocation of tasks and responsibilities among various stakeholders, including farmers, agronomists, technicians, and AI systems. Methods: The methodology involves the development of a dynamic role assignment model that considers factors such as expertise, resource availability, and real-time environmental data. This model is tested in various agricultural scenarios to evaluate its impact on operational efficiency and innovation scalability. The findings demonstrate that optimized role assignment not only enhances the performance of AI applications but also fosters a collaborative ecosystem that is adaptable to changing agricultural demands. Results: & Discussion:This research finds a number of elements that affect how well duties are distributed within agricultural frameworks, including organizational frameworks, leadership, resource accessibility, and cooperative efforts through AI. In addition to advocating for its comprehensive integration into the sector's culture, this paper offers a collection of best practices and techniques for optimizing role allocation in agriculture. Additionally, the study gives a thorough overview, summary, and analysis of a few papers that are specifically concerned with scaling innovation in the field of agricultural research for development. Significance: Furthermore, the study highlights the potential of AI to transform traditional farming practices, reduce labor-intensive processes, and improve decision-making accuracy. The proposed approach serves as a blueprint for agricultural enterprises aiming to adopt AI technologies while ensuring optimal utilization of human and technological resources. By addressing the challenges of role ambiguity and resource allocation, this research contributes to the broader goal of achieving sustainable and resilient agricultural systems through technological innovation.

Understanding vegetation heterogeneity dynamics is crucial for assessing ecosystem resilience, biodiversity patterns, and the impacts of environmental changes on landscape functions. While previous studies primarily focused on NDVI pixel trends, shifts in landscape heterogeneity have often been overlooked. To address this gap, our study evaluated the effectiveness of continuous metrics in capturing vegetation dynamics over time, emphasizing their utility in short-term trend analysis. The study area, located in Iran, encompasses a mix of urban and agricultural landscapes dominated by farming-related vegetation. Using 11 Landsat 8 OLI images from 2013 to 2023, we calculated NDVI to analyze vegetation trends and heterogeneity dynamics. We applied three categories of continuous metrics: texture-based metrics (dissimilarity, entropy, and homogeneity), spatial autocorrelation indices (Getis and Moran), and surface metrics (Sa, Sku, and Ssk) to assess vegetation heterogeneity. By generating slope maps through linear regression, we identified significant trends in NDVI and correlated them with the slope maps of the continuous metrics to determine their effectiveness in capturing vegetation dynamics. Our findings revealed that Moran’s Index exhibited the highest positive correlation (0.63) with NDVI trends, followed by Getis (0.49), indicating strong spatial clustering in areas with increasing NDVI. Texture-based metrics, particularly dissimilarity (0.45) and entropy (0.28), also correlated positively with NDVI dynamics, reflecting increased variability and heterogeneity in vegetation composition. In contrast, negative correlations were observed with metrics such as homogeneity (−0.41), Sku (−0.12), and Ssk (−0.24), indicating that increasing NDVI trends were associated with reduced uniformity and surface dominance. Our analysis underscores the complementary roles of these metrics, with spatial autocorrelation metrics excelling in capturing clustering patterns and texture-based metrics highlighting value variability within clusters. By demonstrating the utility of spatial autocorrelation and texture-based metrics in capturing heterogeneity trends, our findings offer valuable tools for land management and conservation planning.