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The question of how agricultural knowledge and innovation systems (AKISs) can address the issue of sustainable management of natural resources (SMNR) is presented in this conference paper. This literature review, which collected published research from the Scopus electronic database, aimed to explore the value of AKISs in enhancing the sustainability of natural resources. Therefore, it examined and evaluated the roles of AKISs as either positive or negative overall. Moreover, it analyzed whether the use of AKISs supports the goal of creating a sustainable system that links agriculture with natural resources. Among its findings, this review presents the positive and negative outcomes of each element and potential future scenarios/suggestions if the current trends persist. [ABSTRACT FROM AUTHOR]

The paper introduces the analysis results over a selected number of small-scale farms in the Western Romanian agriculture, more precisely in the counties of Arad and Timis. The purpose of the investigation is linked to the assessment of need for advisory services input in solving the problems of the farmers, particularly for the ones operating in small farms. Analysing the returned answers in connection with the fields of interest or needed expertise, accounting for the forms of mutual or directional help, compiling any forms of advice inputs, including the specialised structures other than public in absence of a function Farm Advisory Service allow the development of a structured image in terms of current problems, the way of solving and the interactions among the farmers inside or outside their communities. The quantified findings can serve as base for a public policy recommendation in the field of farm advisory as part of the Agriculture Knowledge and Innovation System. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) has brought about transformative changes across diverse industries, and its application in agriculture, particularly in soil management, has become a focal point of research and innovation. This paper explores the paradigm shift enabled by IoT in detecting organic carbon in soil, with the overarching goal of advancing agricultural productivity and sustainability. Emphasizing the pivotal role of organic carbon in soil health, the research addresses the existing challenges associated with its detection and underscores the potential advantages offered by IoT-based solutions for precise and real-time monitoring. The study meticulously examines various IoT technologies, including sensors, data analytics, and wireless communication protocols, within the specific context of organic carbon detection. A thorough review of contemporary techniques, methodologies, and devices utilized in IoT-based organic carbon detection in soil is presented. The paper not only sheds light on the current state-of-the-art but also explores the advantages, limitations, and prospects inherent in the implementation of IoT-enabled systems in agriculture. The envisioned outcomes encompass enhanced soil management practices, heightened crop yields, and the simultaneous preservation of the environment. In essence, this research contributes to the expanding body of knowledge surrounding IoT applications in agriculture, specifically emphasizing the transformative potential of IoT-enabled organic carbon detection for revolutionizing farming practices and promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics. [ABSTRACT FROM AUTHOR]

This paper firstly compares the structure of agricultural supply chain ecosystem from both internal members and external environment and analyzes the information transfer mechanism of information flow, capital flow, logistics and other energy and information in the supply chain. The importance of supply chain ecological coordination mechanism and its functional components are also discussed in depth. On this basis, the biosecurity strategy system of agricultural products supply chain management is constructed and the biosecurity strategy of supply chain management is proposed. Finally, the biosafety risk of agricultural products supply chain management in the e-commerce environment is assessed, and a multi-dimensional risk assessment index is proposed. Meanwhile, risk management theory is applied to provide a detailed analysis of the likelihood, loss, contagiousness, controllability and comprehensiveness of risks. The study showed that the risk indices were relatively high with the average values of 85% for transportation and storage technology risk, 76% for additive abuse risk, and 70% for agricultural input improper risk, respectively. The research in this paper provides new ideas and methods for biosafety management in the supply chain of agricultural products. [ABSTRACT FROM AUTHOR]

Food security, a vital determinant of overall health and well-being, hinges on the simultaneous achievement of four dimensions: physical availability of food, economic and physical access to food, utilization of nutrients, and stability over time. This paper explores the complex and multi-faceted food security situations in Kenya and Zambia, emphasizing key factors such as agricultural production, government policies, and socio-economic conditions. [ABSTRACT FROM AUTHOR]

This document discusses the applications of machine learning in national territory spatial planning. National territory spatial planning is important for solving issues such as human-land conflict and regional development imbalance. Machine learning technology is being used to improve national territory spatial planning, but there is still a need for further exploration and research in this area. The document highlights research papers that focus on topics such as urban spatial environmental monitoring, food security and agricultural development, and methodological innovation in territorial spatial planning. The authors note that there are challenges and difficulties that need to be addressed in the future, including data mining, small-scale research, and practical case studies. [Extracted from the article]

To advance understanding about the way endogenous knowledge on mangrove swamp rice (MSR) farming is transmitted, reinvented and negotiated between generations and highlight the implications this has for R4D. Long-term qualitative research using an ethnographic approach, technography and biographies of farmers. The endogenous Agricultural Knowledge and Innovation System (AKIS) is dynamic but faces shortcomings owing to socio-environmental changes. This highlights the relevance of knowledge exchanges from multiple sources, eroding the divide between endogenous and exogenous from the farmers' point of view. For positive exogenously induced socio-technical change to occur, external actors must fully understand the conditions under which (a) endogenous knowledge is produced and reinvented and (b) endogenous innovations flow among farmers. This will then allow the co-production of innovations adapted to each specific agro-ecological, cultural and socio-economic context, leaving them to be further transformed and refashioned by smallholders to meet their individual 'dispositions' and generate the roots necessary for a strong AKIS. This paper highlights the importance of locally embedded processes of learning and innovation, the better understanding of which can provide a more solid basis for the co-production of technologies and the establishment of an AKIS. This paper improves understanding of socio-technological change in smallholder agriculture by focusing on endogenous processes of learning and innovation. This paper advances the knowledge on endogenous processes of learning and socio-technical change in smallholder agriculture. [ABSTRACT FROM AUTHOR]

With the new challenges and crises facing agriculture, digitalization and green transformation have become important ways to solve the problems. This paper uses an international economics perspective to chart a new path for sustainable agricultural development. Specifically, it analyzes whether two-way international direct investment (FDI) can facilitate agricultural digital-green fusion(DGF)? Using a sample of 31 provinces (autonomous regions) from 2012 to 2021, this study finds: (1) Two-way FDI can significantly contribute to agriculture's DGF. (2) In the mechanism test, it is proved that two-way FDI can promote agriculture's DGF level by promoting green technology innovation capacity and overall regional technology innovation capacity. (3) The positive effects of two-way FDI are prominent in the eastern and central regions, coastal regions, and economically developed areas. (4) In the spatial Durbin model, the local two-way FDI growth improves agriculture's DGF level in the surrounding areas to a certain extent. The government is advised to prioritize openness, foster an environment for technological innovation, leverage spatial radiation for agricultural DGF, and advance digitally empowered agricultural modernization. [ABSTRACT FROM AUTHOR]

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

Advancing the construction and application of new quality productive forces is an essential prerequisite for achieving high-quality agricultural development and expediting the establishment of agricultural powerhouses. This study aims to elucidate the internal mechanisms through which new quality productivity contributes to high-quality agricultural development and to explore practical pathways for enhancing agricultural quality through its promotion. Utilizing panel data spanning 2012 to 2021 from 30 provinces and municipalities in mainland China, the entropy method is employed to gauge levels of new quality productivity and high-quality agricultural development. Additionally, employing research methodologies including SYS-GMM and threshold effect models, this study empirically investigates how the advancement of new quality productivity influences high-quality agricultural development. Our research reveals the following key findings: (1) The development of new quality productive forces significantly enhances high-quality agricultural development, exhibiting a heterogeneous distribution pattern favoring the "eastern region > western region > central region" and "northern region > southern region". (2) New quality productive forces can bolster the level of high-quality agricultural development by fostering innovation, coordination, openness, and shared development within its subsystems. However, they may impede progress by inhibiting improvements in green development within the subsystems. (3) The results of the threshold effect test demonstrate that the promotion effect of the development of new quality productive forces on high-quality agricultural development escalates with the level of high-quality agricultural development. Specifically, as the level of high-quality agricultural development exceeds the first threshold value of 0.1502, the promotion effect becomes significant; crossing the second threshold value of 0.2010 further amplifies this effect. This paper's primary marginal contribution involves empirically analyzing the potential nonlinear effects of advancing new quality productivity in enhancing the level of high-quality agricultural development. This enriches empirical research on how new quality productivity fosters the development of high-quality agriculture. [ABSTRACT FROM AUTHOR]

Based on the panel data of provincial units in China from 2000 to 2020, this paper uses the entropy method to calculate agricultural environmental regulation level in China from the perspectives of afforestation projects, water conservancy projects and environmental protection projects. Then, this paper calculates agricultural non-point source pollutant emissions in China by using the unit survey and evaluation method, and calculates agricultural green productivity growth in China by using the SBM directional distance function and Luenberger productivity index. On this basis, the influence of environmental regulation on agricultural green productivity growth in China and its mechanism are empirically analysed. The results show that:(1) During the sample period, agricultural environmental regulation level and agricultural green productivity growth increased significantly, while the emission density of agricultural non-point source pollution decreased significantly; (2) On the whole, environmental regulation significantly promoted agricultural green productivity growth in China, which was mainly reflected in the promotion of agricultural green technology progress change; (3) Environmental regulation can significantly promote agricultural green productivity growth in China through the channel of agricultural innovation investment. The innovation of this paper lies in the measurement of agricultural environmental regulation, and the analysis of the moderating effect of agricultural innovation investment on the impact of environmental regulation on agricultural green productivity growth in China. The research conclusions of this paper verify the 'Porter Hypothesis' theory in the field of agriculture, and have guiding significance for China to promote agricultural green productivity growth by relying on environmental regulation. [ABSTRACT FROM AUTHOR]

The need to develop and provide integrated observation systems to better understand and manage global and regional environmental change is one of the major challenges facing Earth system science today. In 2008, the German Helmholtz Association took up this challenge and launched the German research infrastructure TERrestrial ENvironmental Observatories (TERENO). The aim of TERENO is the establishment and maintenance of a network of observatories as a basis for an interdisciplinary and long‐term research program to investigate the effects of global environmental change on terrestrial ecosystems and their socio‐economic consequences. State‐of‐the‐art methods from the field of environmental monitoring, geophysics, remote sensing, and modeling are used to record and analyze states and fluxes in different environmental disciplines from groundwater through the vadose zone, surface water, and biosphere, up to the lower atmosphere. Over the past 15 years we have collectively gained experience in operating a long‐term observing network, thereby overcoming unexpected operational and institutional challenges, exceeding expectations, and facilitating new research. Today, the TERENO network is a key pillar for environmental modeling and forecasting in Germany, an information hub for practitioners and policy stakeholders in agriculture, forestry, and water management at regional to national levels, a nucleus for international collaboration, academic training and scientific outreach, an important anchor for large‐scale experiments, and a trigger for methodological innovation and technological progress. This article describes TERENO's key services and functions, presents the main lessons learned from this 15‐year effort, and emphasizes the need to continue long‐term integrated environmental monitoring programmes in the future. Plain Language Summary: This paper discusses the importance of creating comprehensive environmental observation systems to better understand and address global and regional environmental changes. In 2008, a German research infrastructure named Terrestrial Environmental Observatories (TERENO) was established to build and maintain a network of observatories. The goal is to conduct interdisciplinary, long‐term research on the impacts of global environmental changes on terrestrial ecosystems and their socio‐economic effects. The TERENO network employs advanced methods from environmental monitoring, geophysics, remote sensing, and modeling to study various environmental aspects. Over the past 15 years, four observatories have been part of this network, contributing to valuable experience in overcoming challenges and exceeding expectations. Today, TERENO is a crucial component for environmental modeling and forecasting in Germany, serving as an information hub for practitioners and policymakers. It also fosters international collaboration, supports large‐scale experiments, and drives methodological and technological advancements. The article highlights key lessons learned from this 15‐year effort and emphasizes the importance of continuing such integrated environmental monitoring programs in the future. Key Points: Integrated observatories ensure a holistic Earth Systems perspective, offering data for current and future ecological challengesThe scientific and societal value of observatories is invaluable, but their design, construction and operation require considerable effortFor assured long‐term data collection, research infrastructure must have flexible design for adapting to changing research needs [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm. [ABSTRACT FROM AUTHOR]

Shadow, a natural phenomenon resulting from the absence of light, plays a pivotal role in agriculture, particularly in processes such as photosynthesis in plants. Despite the availability of generic shadow datasets, many suffer from annotation errors and lack detailed representations of agricultural shadows with possible human activity inside, excluding those derived from satellite or drone views. In this paper, we present an evaluation of a synthetically generated top-down shadow segmentation dataset characterized by photorealistic rendering and accurate shadow masks. We aim to determine its efficacy compared to real-world datasets and assess how factors such as annotation quality and image domain influence neural network model training. To establish a baseline, we trained numerous baseline architectures and subsequently explored transfer learning using various freely available shadow datasets. We further evaluated the out-of-domain performance compared to the training set of other shadow datasets. Our findings suggest that AgroSegNet demonstrates competitive performance and is effective for transfer learning, particularly in domains similar to agriculture. [ABSTRACT FROM AUTHOR]

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This research conducts an analysis of the sustainability of urban agriculture in Shanghai over the period 2010 to 2020, employing the Triple Bottom Line (TBL) concept as a framework to evaluate sustainability across economic, environmental, and social dimensions through the formulation and application of a comprehensive indicator system. Utilizing the Delphi method alongside the Analytic Hierarchy Process (AHP) for determining indicators and their respective weights, this study adopts a methodologically rigorous approach to analysis. The findings reveal an overall enhancement in agricultural sustainability, albeit accompanied by a decline in economic sustainability. Notably, environmental sustainability emerged as a paramount concern, underscoring the essentiality of incorporating environmental indicators within urban agricultural initiatives. The paper addresses significant challenges such as elevated land prices, demographic shifts, and the imperative for more stringent environmental regulations. It advocates for a multidimensional strategy integrating advanced agricultural technologies and cross-sectoral partnerships to bolster sustainability. Furthermore, the study accentuates the necessity of achieving equilibrium among economic feasibility, environmental stewardship, and social equity to pursue sustainable urban agriculture in Shanghai. Additionally, it highlights the critical role of strategic agricultural policy formulation in fostering sectoral resilience and ensuring enduring sustainability. [ABSTRACT FROM AUTHOR]

Farm Advisory constitutes one of the most important tools to support rural development in the European Union and is also an integral part of Agricultural Knowledge and Innovation System (AKIS). The purpose of this paper is to present the results of the evaluation of the two calls for certification of Farm Advisors in Greece, which were addressed to individuals. The evaluation was based on a questionnaire survey of candidates who participated to the online certification procedure. The analysis is based on descriptive statistics methods and shows that overall most respondents were satisfied with most Modules, although they suggest to provide better links between scientific evidence and practical applications. Although there are serious limitations that do not permit to draw generalized conclusions, the evaluation procedure pointed out specific domains that require improvements and, especially, that a more robust evaluation system is required. [ABSTRACT FROM AUTHOR]

The aim of this paper is to answer the question of whether the Greek AKIS system can contribute to the different requirements of the new trends in agriculture according to its main functions. A SWOT analysis has been applied to examine the internal and external environment. Data were collected from 61 experts/representatives of organizations (policy, education, research, consulting, agricultural cooperatives, credit, private companies, and farmers). The data were analysed using Excel spreadsheets and the Statical Package for Social Sciences (SPSS V.28). Based on this method, dominant strengths and weaknesses as well as opportunities and threats of AKIS were identified as a starting point, as well as useful guidance for decision makers, local authorities, and the other actors in Greece. [ABSTRACT FROM AUTHOR]

This paper investigates the implementation of innovation and the advisory needs of farmers in agricultural holdings in Western Macedonia. The research carried out was divided into two parts. Initially, we investigated how the programs under analysis related to innovation in the agricultural sector, specifically with respect to the improvement plans that were implemented in the region. Specifically, parameters such as age, gender, place of residence, and the types and number of investments were examined. The analysis revealed farmers' characteristics and the types of their investments, in addition to their degrees of adoption of innovation and their attitudes towards innovation. We also tried to clarify the reasons leading farmers toward innovation and the importance of consulting services in their orientation towards innovative ideas. [ABSTRACT FROM AUTHOR]

Digital twins are essential in Agriculture 5.0, providing an accurate digital representation of agricultural objects and processes, enabling data-driven decision-making, the simulation of future scenarios, and innovation for a more efficient and sustainable agriculture. The main objective of this article is to review and compare the main tools for the development of digital twins for Agriculture 5.0 applications using 3D point cloud models created from photogrammetry techniques. For this purpose, the most commonly used tools for the development of these 3D models are presented. As a methodological approach, a qualitative comparison of the main characteristics of these tools was carried out. Then, based on some images taken in an orange grove, a quality analysis of the 3D point cloud models obtained by each of the analyzed tools was carried out. We also obtained a synthetic quality index in order to have a way to categorize the different pieces of software. Finally, as a conclusion, we compared the performance of the different software tools and the point clouds obtained by considering objective metrics (from the 3D quality assessment) and qualitative metrics in the synthetic quality index. With this index, we found that OpenDroneMap was the best software in terms of quality-cost ratio. Also, the paper introduces the concept of Agriculture 6.0, exploring the integration of advancements from Agriculture 5.0 to envision the potential evolution of agricultural practices and technologies, considering their impact on social and economic aspects. [ABSTRACT FROM AUTHOR]

We studied innovation processes in agriculture and nutrition to discuss a scaling approach that encompasses the technical, institutional, and behavioral dimensions of change. To understand dynamics across these dimensions, we analyzed farmers' innovation processes through two analytical lenses: farmer-centered and structural. Focus group discussions in Kapchorwa, Uganda, and Teso South, Kenya, looked at farmers' choices of innovations. Individual interviews and stakeholder workshops at both study sites increased understanding of the local innovation system. To address local challenges, strive for livelihood aspirations, and fulfill personal taste preferences, farmers selected and adapted practices promoted by a research project. A wide range of additional support providers encouraged farmers to develop innovations in agriculture, marketing, and nutrition. By promoting innovation as a process rather than an outcome, it is possible to address context-specific needs and enhance farmers' adaptive capacities. Scaling these processes necessitates the involvement of innovation support service providers in order to create an enabling environment for experimentation. Analytical dualism highlights the different roles of human agency and structures in innovation processes needed to design successful scaling strategies. This paper sets out a novel approach to understanding the increasingly discussed dimensions of scaling by linking them with concepts from innovation studies. [ABSTRACT FROM AUTHOR]

Sustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation. [ABSTRACT FROM AUTHOR]

Organic agriculture, as a holistic system, aims to maximize the productivity of agricultural sector by integrating tradition, innovation and science, helping to improve the quality of agrifood products reaching the market. One of the most rapidly evolving sectors in the EU is the organic manufacturing industry. Due to the increased interest of consumers it has had a significant increase in the last decade. This paper offers a detailed perspective on organic agriculture in Romania in the context of current European development. We present essential information about the organic sector, current legislation and market data, at national and European level. Official data show that organic agriculture has registered an increase in plant and animal production in recent years in Romania. Although it represents a small part of the cultivated area and total production, organic agriculture is attracting attention due to the principles of sustainable development and the associated economic opportunities. [ABSTRACT FROM AUTHOR]

The article examines the factors of neo-industrialization and their impact on the functioning of Russian agriculture. The purpose of the work is to identify the key factors of neo-industrialization and assess their impact on the sustainable development of agriculture. The scale of technological changes has been studied, and a comparative analysis of neo-industrial trends in various sectors of the agro-industrial complex has been carried out. The specifics of neo-industrial processes in livestock farming are characterized in detail. A mathematical model of threefactor linear regression has been developed, characterizing the influence of parameters of technological development of Russian agriculture on changes in gross value added. The results of the empirical analysis allow us to conclude that there is a direct relationship between capital-labor ratio and gross value added, as well as between the number of highly productive workers and gross value added. It has been calculated that the investment component has the greatest influence. Thus, the increase in the share of investments in reconstruction and modernization in fixed capital affects the increase in gross value added by 223.7 billion rubles. The practical value of the results lies in the development of mechanisms to stimulate and support the introduction of innovations in agricultural production in order to accelerate neo-industrialization. [ABSTRACT FROM AUTHOR]

In ensuring the country's food security, the dominant role belongs to scientific activity and the development of advanced production technologies. Moreover, the process of implementation of innovations and further stimulation of demand for the results obtained are of greatest relevance. The purpose of the study is to develop theoretical and methodological approaches to assessing the implementation of scientific developments and new technical solutions in agriculture in the context of innovative transformation of the economy. The article reveals significant interregional and intercountry differentiation of patent activity, and notes the gap between the total number of patents and the inventions being introduced into Russian agriclture. Based on a scientometric analysis of patents with text mining, trends and patterns in the formation of promising patent developments in agriculture have been identified and a bibliometric map has been generated in the direction of the relationship between areas of patent activity in Russian agriculture. The predominant clusters have been identified - agricultural engineering; soil treatment, harvest, pest control, production of herbicides and their components. The results obtained determine the need to develop effective mechanisms for supporting patent activity in agriculture and stimulating the introduction of scientific developments and new technical solutions into the production process in order to determine the most promising areas of research in agricultural science. The practical implementation of the research results is associated with the justification of the necessary incentive measures and government support for scientific developments in the context of fundamental technological changes and innovative structural shifts. [ABSTRACT FROM AUTHOR]

The purpose of this paper was to examine the differences in using agricultural entrepreneurship support and how it relates to characteristics such as the agrarian structure, socioeconomic development level, and land use across the Polish territory. The analyses were carried out at the district (poviat) level which is an intermediate administrative unit. This study found that the decisions on co-financing the measures dedicated to non-agricultural economic activity heavily depended on the agrarian structure. The highest levels of activity were witnessed in districts where the agrarian structure was particularly fragmented. [ABSTRACT FROM AUTHOR]

This paper investigates four different mobile robots with respect to their driving characteristics and soil preservation properties in an agricultural environment. Thereby, robots of classical design from agriculture as well as systems from space robotics with advanced locomotion concepts are considered to determine the individual advantages of each rover concept with respect to the application domain. Locomotion experiments were conducted to analyze the general driving behavior, tensile force, and obstacle‐surmounting capability and ground interaction of each robot. Various soil conditions typical for the area of application are taken into account, which are varied in terms of moisture and density. The presented work covers the specification of the conducted experiments, documentation of the implementation as well as analysis and evaluation of the collected data. In the evaluation, particular attention is paid to the change in driving characteristics under different soil conditions, as well as to the soil stress caused by driving, since soil quality is of critical importance for agricultural applications. The analysis shows that the advanced locomotion concepts, as used in space robotics, also have positive implications for certain requirements in agricultural applications, such as maneuverability in wet conditions and soil conservation. The results show potential for design innovations in agricultural robotics that can be used, to open up new fields of application for instance in the context of precision farming. [ABSTRACT FROM AUTHOR]

Agriculture has been the backbone of African economies, and agricultural development has the potential to drive overall economic growth. Considering the importance of agriculture in Africa, foreign aid donors have supported the sector to help push economic growth and reduce poverty. Studies indicate governance quality of a recipient country is an important factor for agricultural aid. Building upon this, the paper explores how bilateral donors provided agricultural aid to two African countries that have different institutional quality, Ghana and Uganda. The analysis of agricultural aid in those two countries was carried out with bilateral aidprofile data from 2011 to 2022. Despite the similar economic importance of agriculture in Ghana and Uganda, donors to Ghana invested the largest amount in agriculture among aid sectors, followed by health. On the other hand, donors to Uganda supported mainly health-relevant sectors with agriculture being ranked only fifth. Donors in both countries implemented agricultural aid mostly as project types. Yet, donors disbursed their aid funds through different aid channels. Donors to Ghana disbursed 48% of agricultural aid funds through the public sector institutions channel (or the state channel). By contrast, donors to Uganda disbursed only 24% of agricultural aid funds through the state channel, essentially bypassing Ugandan public agencies. They instead provided 76% of agricultural aid through the non-state channels such as non-governmental organizations, multilateral organizations, and private entities. Similar donor preferences of the aid disbursement channels were observed with total aid across all aid sectors. Overall, the donor aid profiles suggested donors provided Ghana with more flexible agricultural aid, while in Uganda they controlled agricultural aid more tightly. There could exist multiple underlying reasons for this, but the different level of institutional quality is highly likely one of the possible reasons. For foreign aid, governance quality is an important issue equally to both donors and recipients. [ABSTRACT FROM AUTHOR]

As part of sustainable agricultural innovations and the alternative food movement, agroecology provides important tools to achieve the sustainable development goals (SDGs). Yet very few existing studies have explicitly addressed their linkages. Synergies among the SDGs are often examined at the national level, overlooking nuanced opportunities revealed by small agroecology initiatives at the local scale. Based on literature review, field visits and semi-structured interviews with farms and relevant organizations in China, this research investigates how locally embedded agroecology practices lead to advancement of multiple SDG targets and facilitate the synergies among the SDGs. It also highlights their motivations, achievements, and challenges. We argue that the technocentric metrics of SDGs underappreciate the application of traditional knowledge and small-scale low-tech innovations. By bringing visibility to low-tech and small agroecology initiatives, this paper advocates for stronger inclusion of grassroots agroecology initiatives in SDG discussions among researchers and policy makers. [ABSTRACT FROM AUTHOR]

The production of fisheries and shrimp has been twice every 10 years for the previous five decades, making it the most rapidly expanding food industry. This growth is due to intensive farming and the conversion of agriculture into aquaculture in many parts of South Asia. Furthermore, intensive aquaculture generates positive economic growth but leads to environmental degradation without proper monitoring. Unfortunately, technical innovation is less in aquaculture than agricultural and manufacturing industries. The advent of remote sensing and soft computing has expanded various opportunities for utilizing and integrating technological advances in civil and environmental disciplines. This paper presents the aquaculture scenario in the western Godavari delta region of Andhra Pradesh and proposes various novel assessment tools to monitor the aquaculture environment. An experimental investigation was carried out on the physicochemical characteristics of the inland aquaculture ponds to evaluate water quality in the aquaculture ponds. Furthermore, to assess the intensity of inland aquaculture, the current work concentrates on the potential application of remote sensing and soft computing approaches. Geospatial models of kriging and inverse distance weighing (IDW) show higher performance in estimating ammonia levels in the intensive aquaculture groundwaters with coefficient of determination (R2) values of 0.947 and 0.901, respectively. Teaching learning-based optimization (TLBO) and adaptive particle swarm optimization (APSO), two of the five soft computing techniques utilized in the study, perform better than the others. Additionally, it was found that remote sensing-based assessment tools and soft computing prediction models were both trustworthy, accurate, and easy to use. Furthermore, these methods could assist in the real-time evaluation of inland aquaculture waters by stakeholders and policymakers. [ABSTRACT FROM AUTHOR]

To improve the innovation of agricultural machinery product styling, this paper proposes a shape structure behavior function (SSBF) model suitable for the industrial design field. The feature line evolution method combining shape grammar and genetic algorithm was used for modelling the of the grader, which not only maintains the product style characteristics but also reflects the typical identification characteristics of the bionic prototype and produces a new product modelling scheme. By conducting cognitive and recognition experiments on product styling features, the ranking of product styling features and the contribution of each component to product styling were determined. The method of combining shape grammar and quadratic Bézier curve was used to express and encode feature lines, and genetic algorithm was used to evolve biomimetic forms to form product feature lines with typical biological morphological features; The extracted form bionic elements were integrated into the grader modelling design, and the interaction evaluation was carried out through the genetic algorithm evolution scheme. The basic form elements were extracted and analyzed, and the deduction rules were formulated and reorganized. The derived feature line geometric data considered the product's image features and the bio-inspired prototype, which can be used for the follow-up guidance of industrial design schemes. [ABSTRACT FROM AUTHOR]

Human capital development is considered the primary source of knowledge and skills in the innovation process. Despite this, there is evidence of a lack of technical and managerial knowledge among emerging sugarcane farmers who are beneficiaries of South Africa's land reform programme, thus, limiting their full potential in terms of innovation and productivity which is detrimental to their competitiveness. This paper employs the Crépon, Duguet, and Mairesse (CDM) approach, correcting for endogeneity problems, to estimate the causal impact of on-the-job training expenditure, used as a proxy for human capital development, on innovation, and innovation on farm productivity. It is based on a case study of 35 emerging sugarcane farmers in the KwaZulu-Natal north coast, South Africa. The results from the CDM model confirm the causal relationships between human capital (on-the-job training) and the innovation behaviour of the farmers, which positively impact the farm's productivity. This result underscores the relevance of human capital development in boosting innovation and productivity in the agricultural sector. [ABSTRACT FROM AUTHOR]

The demand for sustainable agricultural technologies still lags behind the supply confirming the demand articulation failure of transformational innovation change agricultural policies. To understand the reasons for demand shortcomings, the evaluation of developed policies is required. In the literature, there is little evidence on this topic, henceforth, this paper conducts a systematic review of the primary methodological approaches used to assess the influence of policies on the dissemination of agricultural innovations. The results showed that there are two clusters of evaluation; the first investigates how policies affect agricultural innovation adoption, and the second studies how policies affect yields and profitability. For the first cluster, 70% of the studies analyzed adoption decisions using the Double-hurdle, Probit, or Tobit models or captured changes in adoption levels over time using the Adoption and Diffusion Outcome Prediction Tool and discrete-time duration models. This cluster is related to the assessment of the input and output additionalities of innovation policies. In 58% of the studies related to the second cluster, the focus was the assessment of economic and environmental implications using mathematical programming models, particularly agent-based modeling. The purpose of evaluation in this cluster is more focused on behavioral additionality. There were no experimental or quasi-experimental methods among the methods utilized in this cluster. The majority of studies do not incorporate the evaluation of economic, social, and environmental aspects together; therefore, evaluation outlooks suggest increasing interest in sustainability impact. It is suggested that models from both clusters be used in combination to explore input, output, and behavioral additionalities simultaneously. Furthermore, including white-box evaluation approaches to evaluate demand-oriented innovation policy in the agricultural sector, in addition to usual black-box approaches, is a necessity. [ABSTRACT FROM AUTHOR]

The information coordination of fresh agricultural products supply chain is conducive to improving the overall efficiency and competitiveness of the supply chain, and reducing carbon emissions. Under the carbon cap-and-trade policy, this paper constructed an evolutionary game model of supply chain information collaboration involving a fresh agricultural products supplier, a cold chain logistics service provider and a fresh agricultural products retailer, explored the selection of information collaboration strategies of each subject, and simulated it with system dynamics. The results show that: The stability strategy of the game system is related to the initial willingness of each subject. When any two parties participate in information collaboration, the other party will also choose to participate in collaboration. In addition, the choice of information collaboration strategy of the subject is also related to the collaborative planning ability, collaborative strategy formulation ability, consumers' preference, the initial cost of each subject, revenue sharing and cost sharing mechanism, government subsidies, government regulation and carbon cap-and-trade policy. It is worth noting that the collaborative planning capability is more conducive to promoting the subject to choose information collaborative strategy than the collaborative strategy formulation capability; the carbon trading price has a greater impact on the subject's participation in collaboration, while the total amount of carbon quota has no impact on the choice of the subject's information collaboration strategy. Furthermore, based on the research results, the corresponding management suggestions are put forward from the two aspects of the government and the participants. [ABSTRACT FROM AUTHOR]

Digital technologies promise to transform agri-food systems such that they contribute to a more inclusive, economically, and/or environmentally sustainable future. However, without careful attention to the approaches for their introduction, development, and rollout, they are likely to fail to address key aspects of inclusion. Contemporary inclusive innovation mechanisms focus on the involvement of farmers in the innovation processes, but do not elucidate how to operationalize a deeper inclusivity that takes account of the nature of engagement with the farmers and dynamics of the relationships required for successful outcomes. This article analyzes the concept of inclusivity and its operationalization using the case of the Kenya Agricultural Livestock and Research Organization (KALRO). We develop lines of inquiry on how inclusivity can be defined, sought and operationalized in digital agri-food systems. Based on a case study approach, we interviewed 36 stakeholders in the KALRO digital transformation initiative reflecting on literature discussions from secondary data. Primary and secondary data were synthesized to develop lines of inquiry that enabled a deeper exploration of inclusivity. KALRO's rollout and scale-up of digital agriculture technologies for farmers hinges on three evaluative criteria – usability, accessibility, and relevance – which are associated with aspirations for inclusive innovation. Despite these efforts, some technologies they developed included some farmers and excluded others. For the included, inclusion was limited to the accessibility of certain types of information while desired forms of information lacked sufficient mechanisms for access or were not made available. The practice of promoting access, usability and relevance of the technologies while has supported innovation for and with the farmers, there are still challenges in innovating with the farmer. The lines of inquiry "inclusive innovation with" and "inclusive innovation for" introduced in this paper not only help stakeholders gain a deeper, more nuanced and holistic understanding of inclusive innovation, they also reveal the complexity of inclusivity and its operationalization in agri-food systems that are digitalizing. This paper analyzes inclusivity from a practice perspective highlighting areas where the theoretical perspective and empirical observations diverge. The paper makes the case for meaningful changes in how inclusivity is approached, considering the practical needs and preferences of those directly impacted by innovation and the potential of operationalizing 'inclusive innovation with ' as a deeper and more effective approach to inclusive innovation. [Display omitted] • Operationalizing deeper inclusivity in digital technologies can transform agri-food systems. • Using a case study, we develop lines of inquiry for understanding how inclusivity can be understood and sought. • The study reveals the complexity of inclusivity and its operationalization. • Pursuing an approach that focusses on 'innovation for' the farmer has limitations in enhancing inclusivity. • The study identifies benefits of distinguishing and pursuing 'inclusive innovation with' beneficiaries. [ABSTRACT FROM AUTHOR]

Global food systems are confronted with multifaceted challenges, including climate change, malnutrition, and biodiversity loss, disproportionately affecting the livelihoods of millions, particularly in low- and middle-income countries. Recognizing its potential to address these challenges, innovation is increasingly central in public sector organizations. However, despite growing emphasis, the adoption of innovation portfolio management approaches remains limited. Drawing on a case study of CGIAR, a global research partnership dedicated to agricultural challenges, this paper seeks to accomplish two objectives. The primary objective is to produce a set of best practices in innovation portfolio management that can benefit public sector organizations. The secondary objective is to demonstrate how this offers an important precursor to guide organizational decision-making and investment towards responsible food system transformation. An in-depth analysis of CGIAR's approach to innovation portfolio management is based on qualitative and quantitative data from sources such as interviews, observations, documents, and archival records. The study adopts a descriptive case study approach, utilizing an analytical framework that recognizes the importance of methods, mindsets, and mechanisms in describing and analyzing the CGIAR case. Key lessons encompass the need for a holistic perspective on innovation portfolio management. The paper advocates for the integration of an innovation portfolio management approach within broader (1) organizational processes, including human resources, finance, legal frameworks, and risk management; and (2) innovation systems where food system transformation is the outcome of interactions and partnerships between government, public and private sector organizations. This study furnishes actionable guidance to public organizations seeking to harness innovation portfolio management for responsible and impactful food system transformation. [Display omitted] • Innovation portfolio management is underutilized in the public research and development sector. • CGIAR is mainstreaming an innovation portfolio management approach based on Scaling Readiness principles. • The objective is to enhance CGIAR's effectiveness and performance in achieving its 2030 research and innovation strategy. • Innovation portfolio management necessitates a holistic approach with investment in methods, mindsets, and mechanisms. • Integrating this approach into decision-making and program cycles serves as a significant incentive for its adoption and success. [ABSTRACT FROM AUTHOR]

Controlled release technology of chemical fertilizers is an important cornerstone for the efficient development of sustainable agriculture. Damage to the coating layer and fragmentation of particles during the application of controlled release fertilizers can lead to the loss of their controlled release properties, making the development of effective damage reduction operations a key focus in the study of such agricultural materials. Therefore, this paper employs the bonded particle model (BPM) method within the discrete element method (DEM) framework to investigate the mechanism of coating damage in controlled-release fertilizers. It establishes a classification of damage types based on the level of damaged particles as an effective analytical approach. Furthermore, a comparative analysis is conducted between the discrete element particle model (DEPM) method and the bonded particle model method in terms of their performance in discharge mechanics and flow characteristics. The results indicate that the breakage rate of fertilizer discharge is influenced by both the formation of a steady state and the depletion of the fertilizer load within the container, with an average breakage rate of 1.511% over the total period. Differences in the average maximum compression force and mass flow rate between the discrete element particle model group and the bonded particle model group were 60.355 N and 1.998 g/s, respectively, reflecting limitations imposed by the fertilizer models on expected damage deformation behaviors. This study elucidates the process and potential impacts of fertilizer particle damage and deformation, revealing the strengths and limitations of two simulation model approaches under different application conditions, providing insights for sustainable agricultural production and technological innovation in fertilization equipment. [Display omitted] • Construct of model using the reciprocating piston mechanical stacking method. • Breakage rate is affected by stable state formation and fertilizer consumption. • Particle quantity gradation forms the criterion for categorizing types of damage. • Damage types reflect overall trends, predominantly partial and missing types. • The presentation of damage deformations is constrained by the fertilizer model. [ABSTRACT FROM AUTHOR]

Agricultural innovations are inclusive when they are used by any member of society who wants to use them. Conversely, agricultural innovations that can only be used by a specific privileged group within society can be characterized as "exclusive". The first objective of this paper is to examine the inclusivity of agricultural innovations in Ethiopia, using national representative data and considering a wide portfolio of innovations resulting from the collaborative research between CGIAR and its national partners. Second, we also examine how measurement error may affect how we characterize the inclusivity of agricultural innovations. We use nationally-representative survey data from Ethiopia (collected in 2018/19) in which best-practice measures of the adoption of a large number of agricultural innovations were embedded, including the adoption of CGIAR-related improved maize varieties measured using two different approaches: subjective, self-reported survey data; and objective DNA fingerprinting of crop samples taken from the same farmers' plots. A rich set of household variables is also collected in the survey, which allows characterizing the types of farmers that are adopting different innovations, and the extent to which conclusions regarding the inclusivity of innovations depends on the measurement of the latter. Many innovations are not disproportionately more likely to be adopted by male, larger, richer, or more connected farmers. When using self-reported data on adoption of improved maize varieties, adoption appears positively correlated with having larger landholdings and households with lower female participation in agriculture, and negatively correlated with poorer households (being among the bottom 40% of consumption distribution). Substituting survey responses with the results of DNA fingerprinting these correlations disappear, with farm size, gender and poverty status no longer predictive of adoption. The results suggest the potential value of offering a menu of innovations to farmers to increase inclusivity, as it allows each farmer to be a critical consumer of potential innovations and select those that best correspond to their own needs and constraints. We also highlight how important data quality is in ensuring we have correct information about inclusive innovation. [Display omitted] • Nationally representative data on adoption of agricultural innovations for Ethiopia. • Examines importance of representative samples and valid measurement in shaping understanding of inclusive innovation. • Patterns of inclusiveness are innovation-specific and can be subject to measurement error. • Objective measures (e.g. DNA fingerprinting for crop variety adoption) can provided unbiased picture of inclusive innovation. [ABSTRACT FROM AUTHOR]

Introduction: Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications. Methods: The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET. Results: Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges. Discussion: The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties. [ABSTRACT FROM AUTHOR]

It is of critical importance to address energy poverty in rural areas if inclusive prosperity is to be achieved. Digital finance offers new opportunities to alleviate energy poverty in these regions. However, previous studies have mainly focused on the impact of digital finance on poverty, neglecting research on its impact in rural areas and on specific forms of poverty. This study aims to fill this gap by investigating the impact of digital finance on rural energy poverty. The period 2011–2021 was selected as the observation period, with 31 provinces serving as the study objects. The fixed effects model was employed to investigate the impact of digital finance on rural energy poverty, while exploring the mediating effect. The results indicate that digital finance alleviates the level of rural energy poverty, and this conclusion remains valid following a series of robustness tests. Furthermore, digital finance can indirectly alleviate rural energy poverty through technological innovation and agricultural entrepreneurship activities. Further research indicates that the impact of digital finance on rural energy poverty is more pronounced in regions with abundant human capital, robust government intervention, and minimal urban–rural disparities. This study extends the theoretical support for digital finance to indirectly support rural energy to alleviate poverty. Likewise, this finding provides a new perspective for the government and relevant departments to improve the welfare of residents and alleviate rural energy poverty. [ABSTRACT FROM AUTHOR]

Despite accelerated developments in agricultural innovations, there has been a limited progress in their adoption by smallholder farmers, especially in the Global South. Innovations are not adequately reaching farmers, and new technologies are often unsuitable for farmers, or the overall innovation landscape is discouraging the deployment of innovations at scale. The methodological approaches to designing, testing and scaling of innovations play an important role. Involving farmers in the innovation process from the start increases the likelihood of them being more relevant and having a higher chance of adoption. Whereas there is much literature on participatory approaches, there is a limited guidance on choosing an appropriate level of farmer participation across the innovation process. This study aims to develop practical guidance for researchers, development agents and project managers on how to choose an appropriate methodological approach with a suitable level of farmer participation for designing, testing and scaling of agricultural innovations, according to levels of innovation readiness and innovation use. Relevant scientific literature on participatory design, research and scaling of agricultural innovations was reviewed, followed by compiling lessons learned from selected vegetable innovation projects implemented by the World Vegetable Center (WorldVeg) in Asia and Africa. A list of methodological approaches for participatory design, testing and scaling of innovations was generated, with various levels of farmer participation. A simple guiding framework was then developed for choosing an appropriate approach with a suitable level of farmer participation, according to the readiness and use of the innovations. Examples of selected approaches and application of the proposed guidance are given in project case studies. Based on the literature review and experience at WorldVeg, we believe it is critical to choose the right level of participation based on how ready are the innovations for application in the local setting. This dimension of how ready the innovations are has been largely overlooked in earlier frameworks on farmer participation. The developed guidance generally recommends increasing participation with higher levels of technology readiness and use. In addition, farmer consultation in early research design should also be strengthened, to co-design locally relevant innovations. The overall innovation systems' landscape must be considered when researching and scaling new innovations. The guidance, experiences and lessons provided in this paper will be useful to various scholars, researchers, development agents, funders and project managers who aim to design, test and scale agricultural innovations in a more inclusive and responsible way. [Display omitted] • Despite rapid developments in agricultural innovations, there has been limited adoption by smallholder farmers. • There is limited guidance on choosing an appropriate level of farmer participation in the innovation processes. • A list of participatory approaches for design, testing and scaling of innovations was created. • A guidance for choosing a suitable level of participation based on 'innovation readiness and use' was developed. [ABSTRACT FROM AUTHOR]

Institutional change strategies that prioritize transparency, fairness, and respect will advance inclusive agri-food systems innovation. This short communication posits that institutional change strategies are a linchpin, necessary for International Agricultural Research Centers to implement models promoting inclusive agri-food systems innovation. Models promoting inclusive innovation are pivotal for generating and advancing research for development actions tailored to specific contexts. Context-specific approaches demonstrate superior adaptability compared to universal actions. They contribute to the effectiveness of agriculture research for development, particularly in times of increasing volatility and inadequate progress toward global sustainability and equity goals. Targeted institutional change strategies foster three sister strategies that bolster inclusive innovation: navigating trade-offs; enhancing partnerships; and diversifying measurement. The paper stems from consultations within the Independent Science for Development Council (ISDC) and ISDC's related advising to CGIAR. Institutional change is the linchpin among four strategies identified by ISDC for driving inclusive agri-food systems innovation in Agriculture Research for Development. [Display omitted] • Context specific actions are needed to address gaps in progress toward global sustainability and equity goals. • Inclusive agri-food system innovation models generate and promote context-specific actions. • Strategies exist to advance inclusive agri-food system innovation models in agricultural research for development (AR4D). • Institutional change strategies are a cornerstone for inclusive and effective AR4D. [ABSTRACT FROM AUTHOR]

Climate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. This paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. Our approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. Engaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. The findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced. [Display omitted] • Explores the challenges of steering a largescale climate services innovation project towards greater inclusivity. • Maximising customisation options for users and avoiding prescriptive interpretation enables greater flexibility of use. • Responsible and inclusive approaches to climate services design are valuable even if adopted once development has commenced. [ABSTRACT FROM AUTHOR]

Efficient allocation of agricultural scientific and technological talents (ASTTs) is crucial for agricultural innovation and economic development. This study aims to systematically evaluate ASTTs' allocation efficiency in provincial agricultural research institutions in China, aiding decision-making for local governments and research bodies. Utilizing data from 2009 to 2019 across 31 provinces, an output-oriented data envelopment analysis model measures ASTTs' allocation efficiency and analyzes its trends, regional differences, and spatial characteristics. Results show: (1) Provincial ASTTs' mean comprehensive technical efficiency (CTE) in China was 0.786, with room for improvement. (2) Enhanced CTE was driven by scale efficiency improvements, while pure technical efficiency declined, indicating a need for better management systems and technology applications. (3) Disparities in ASTTs' allocation efficiency among provinces decreased, with higher efficiencies in the East and Central-Southern China regions. At the provincial level, areas like Jiangsu, Shandong, Henan, and Sichuan demonstrated relatively high ASTTs allocation efficiencies. (4) Spatial agglomeration of ASTTs' allocation efficiency was localized in a few major agricultural provinces without a significant overall effect. These findings advocate for further optimization of ASTTs' regional layout and management mechanisms in China. [ABSTRACT FROM AUTHOR]

Introduction: This study delves into the intricate dynamics between fiscal policies supporting agriculture and the non-linear influence of agricultural science and technology innovation on enhancing agricultural resilience. We conducted research across 31 provinces (including autonomous regions and municipalities) in China from 2007 to 2021. Method: By constructing the evaluation index system of agricultural resilience, the entropy value method is used to measure the value of agricultural resilience, and then standard deviation ellipse and center of gravity migration analysis, benchmark regression model, heterogeneity analysis, threshold regression model are used to analyze the relationship between agricultural science and technology innovation, fiscal policies supporting agriculture and agricultural resilience. Result: (1) The analysis of the spatio-temporal evolution trend shows that the overall development of China's agricultural resilience is relatively stable, the resilience range is expanding, and the geographical area with the southeast as the center of gravity presents a stronger pulling effect; (2) The benchmark regression model shows that agricultural science and technology innovation has a significant positive effect on agricultural resilience; (3) Agricultural science and technology innovation plays a nonlinear role in increasing agricultural resilience when fiscal policies supporting agriculture are used as a threshold variable. (4) Heterogeneity analysis highlights stronger promotion of agricultural resilience through science and technology innovation in non-main producing areas and economically underdeveloped regions. Discussion: To address this, policymakers should leverage the resilience of the Southeast, boost innovation capacity, tailor innovation to local needs, and reinforce fiscal policies supporting agriculture. These insights provide valuable direction for policymakers in crafting effective measures to enhance agricultural resilience. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving agricultural landscape, the integration of precision techniques and data-driven approaches has become essential, driven by technological innovations, such as the Internet of Things (IoT), Artificial Intelligence (AI), and cutting-edge aerial and satellite technologies. Precision agriculture aims to maximize productivity by closely monitoring soil health and employing advanced machine learning methods for precise data analysis. This study explores the evaluation of soil quality, placing particular emphasis on leveraging remote sensing technology to collect comprehensive data and imagery to analyze soil conditions related to olive cultivation. By harnessing cloud platforms integrated with satellite data, several analytical tools are made available, offering valuable insights for informed decision-making and operational efficiency across various sectors. Furthermore, this study introduces an AI-driven application tailored to predict the soil moisture levels. This application facilitates in-depth analysis, feature extraction, and the prediction of different vegetation indices using time-series satellite imagery. The study's findings highlight the exceptional accuracy achieved by the decision tree and extra tree regression models, with soil moisture estimation reaching approximately 91%, underscoring the importance and effectiveness of the proposed method in advancing agricultural practices. [ABSTRACT FROM AUTHOR]

The study investigated factors influencing farmers' attitude towards adoption of drought-tolerant maize varieties. One hundred maize farmers were selected using a multi-stage sampling procedure and data were collected through structured questionnaires. Information was collected on the socioeconomic characteristics of the maize farmers, farmers' level of awareness of drought-tolerant maize varieties, farmers' attitudes towards agricultural innovation, constraints to adoption of droughttolerant maize varieties and, factors influencing farmers' attitude towards adoption of droughttolerant maize varieties. Data were analyzed using frequencies, percentages, Pearson Product Moment Correlation and Logit regression. The findings revealed that 76% of the maize farmers had limited awareness regarding the benefits and characteristics of drought-tolerant maize varieties. Also, the majority (74%) of the farmers lacked knowledge about the quality and advantages of these varieties. Despite positive attitude (mean = 4.28) which was measured at ordinal level, several constraints to adoption were identified, mainly low output of drought tolerant maize varieties (mean = 2.30). The findings also showed the substantial impact of two key factors (that is, sex and income) as factors influencing farmers' attitude to adopt drought-tolerant maize varieties. Farmers are yet to take advantage of drought-tolerant maize varieties as a mitigating strategy against climate change. The need for extension services and non-governmental organizations to step up awareness creation and training on best agronomic practices in growing the maize varieties is recommended. [ABSTRACT FROM AUTHOR]