Cláudia M. Viana, Dulce Freire, Patrícia Abrantes
et al.
Agriculture provides the largest share of food supplies and ensures a critical number of ecosystem services (e.g., food provisioning). Therefore, agriculture is vital for food security and supports the Sustainable Development Goal (SDGs) 2 (SDG 2 - zero hunger) as others SDG's. Several studies have been published in different world areas with different research directions focused on increasing food and nutritional security from an agricultural land system perspective. The heterogeneity of the agricultural research studies calls for an interdisciplinary and comprehensive systematization of the different research directions and the plethora of approaches, scales of analysis, and reference data used. Thus, this work aims to systematically review the contributions of the different agricultural research studies by systematizing the main research fields and present a synthesis of the diversity and scope of research and knowledge. From an initial search of 1151 articles, 260 meet the criteria to be used in the review. Our analysis revealed that most articles were published between 2015 and 2019 (59%), and most of the case studies were carried out in Asia (36%) and Africa (20%). The number of studies carried out in the other continents was lower. In the last 30 years, most of the research was centred in six main research fields: land-use changes (28%), agricultural efficiency (27%), climate change (16%), farmer's motivation (12%), urban and peri-urban agriculture (11%), and land suitability (7%). Overall, the research fields identified are directly or indirectly linked to 11 of the 17 SDGs. There are essential differences in the number of articles among research fields, and future efforts are needed in the ones that are less represented to support food security and the SDGs.
Lukas Blickensdörfer, M. Schwieder, Dirk Pflugmacher
et al.
Monitoring agricultural systems becomes increasingly important in the context of global challenges like climate change, biodiversity loss, population growth, and the rising demand for agricultural products. High-resolution, national-scale maps of agricultural land are needed to develop strategies for future sustainable agriculture. However, the characterization of agricultural land cover over large areas and for multiple years remains challenging due to the locally diverse and temporally variable characteristics of cultivated land. We here propose a workflow for generating national agricultural land cover maps on a yearly basis that accounts for varying environmental conditions. We tested the approach by mapping 24 agricultural land cover classes in Germany for the three years 2017, 2018, and 2019, in which the meteorological conditions strongly differed. We used a random forest classifier and dense time series data from Sentinel-2 and Landsat 8 in combination with monthly Sentinel-1 composites and environmental data and evaluated the relative importance of optical, radar, and
Abstract China's economy is currently moving from a stage of high-speed growth to a stage of high-quality development. Agricultural green total factor productivity is of great significance for promoting the high-quality development of the economy. Based on the panel data of China's provincial agriculture, this paper uses the Super-SBM model to calculate China's agricultural green total factor productivity based on carbon emissions. On this basis, this paper uses the nuclear density estimation method to investigate its dynamic evolution trend and the panel data model to empirically study the influencing factors of China's agricultural green total factor productivity. The results show that China's agricultural carbon emissions show an inverted-“U” trend, but the overall growth rate shows a gradual declining trend. The main concentration areas are transferred from the eastern region to the central region, and agricultural fertilizer is the main source of such emissions. China's agricultural green total factor productivity overall shows a fluctuating growth trend, and the differences between provinces show an increasing trend. Agricultural factor endowments and regional characteristics affect China's agricultural green total factor productivity, and there are regional differences in these effects. An important way to improve China's agricultural green total factor productivity and promote the coordinated development of agricultural regions and the high-quality development of the economy is by promoting clean agricultural production, strengthening the research and development of agricultural science and technology, expanding the agricultural opening-up level, and promoting the deep integration of industrialization and agricultural modernization.
The future of farming In the mid-20th century, food production from agriculture sharply increased worldwide; however, this was achieved through heavy use of agrochemicals. Extensive collateral damage from excessive use of pesticides, herbicides, and fertilizers has occurred to the wider environment. This has led to biodiversity loss, pesticide resistance and the emergence of new pests, pollution and decline of freshwater supplies, and soil degradation and erosion, as well as direct harm to health. In a Review, Pretty examines the alternative approaches that can achieve sustainable intensification of farming systems by integrating pest management with agroecological systems to minimize costs, maximize yields, restore ecosystem services, and ensure environmental enhancement. Science, this issue p. eaav0294 BACKGROUND The mid-20th century brought agricultural transformation and the “Green Revolution.” New crop varieties and livestock breeds—combined with increased use of inorganic fertilizers, manufactured pesticides, and machinery—led to sharp increases in food production from agriculture worldwide. Yet this period of agricultural intensification was accompanied by considerable harm to the environment. This imposed costs on economies and made agricultural systems less efficient by degrading ecosystem goods and services. The desire for agriculture to produce more food without environmental harm, and even to make positive contributions to natural and social capital, has been reflected in many calls for more sustainable agriculture. Sustainable intensification (SI) comprises agricultural processes or systems in which production is maintained or increased while progressing toward substantial enhancement of environmental outcomes. It incorporates these principles without the cultivation of more land and loss of unfarmed habitats and with increases in system performance that incur no net environmental cost. SI seeks to develop synergies between agricultural and landscape-wide system components and is now a priority for the Sustainable Development Goals of the United Nations. The concept is open; emphasizes outcomes rather than means; can be applied to any size of enterprise; and does not predetermine technologies, production type, or design components. SI can thus be distinguished from earlier manifestations of intensification because of the explicit emphasis on a wider set of environmental as well as socially progressive outcomes. Central to SI is an acceptance that there will be no perfect end point. No designed system is expected to succeed forever, and no single package of practices is able to fit the dynamics of every ecosystem. ADVANCES Three nonlinear stages in transition toward sustainability have been proposed to occur: efficiency, substitution, and redesign. Although both efficiency and substitution are important, they are not sufficient for maximizing coproduction of favorable agricultural and beneficial environmental outcomes without redesign. Whereas efficiency and substitution tend to be additive and incremental within current production systems, redesign should be the most transformative. Redesign presents social and institutional as well as agricultural challenges. It is now clear that SI is spreading to increasing numbers of farmers and is being practiced on a growing area of farmland. By 2018, it was estimated from these initiatives that across some 100 countries, 163 million farms had crossed an important substitution-redesign threshold by using SI methods in at least one farm enterprise, and over an area approaching 453 million ha of agricultural land. This is equivalent to 29% of all farms worldwide and 9% of agricultural land. OUTLOOK Pest management exemplifies the need for continuing active intervention for SI; the job is never done. Ecological and economic conditions will change, and agroecosystems will have to be adaptable in order to deliver a range of ecosystem services, including food production but also water and soil conservation, soil carbon storage, nutrient recycling, and pest control. Cooperation—or at least individual actions that collectively result in additive or synergistic benefits—is needed for SI to have a transformative impact across landscapes. Farmers will have to be given the confidence to innovate in a flexible way as conditions change. Every example of successful redesign for SI at scale has involved the prior building of social capital. Widespread adoption of IPM needs new knowledge economies for agriculture. Technologies and practices are growing, but new knowledge needs to be collectively created and deployed and needs to give equal emphasis to ecological and technological innovations. The concept and practice embodied in the SI model of agriculture will be a process of adaptation, driven by a wide range of actors cooperating in new agricultural knowledge economies. SI at the landscape scale. In most landscapes worldwide, SI requires engagements by large numbers of farmers to deliver both productivity improvements and benefits to ecosystem services. Redesign will be a continuing effort of transformation and improvement. IMAGE: FRANS LANTING STUDIO/ALAMY STOCK PHOTO Redesign of agricultural systems is essential to deliver optimum outcomes as ecological and economic conditions change. The combination of agricultural processes in which production is maintained or increased, while environmental outcomes are enhanced, is currently known as sustainable intensification (SI). SI aims to avoid the cultivation of more land, and thus avoid the loss of unfarmed habitats, but also aims to increase overall system performance without net environmental cost. For example, large changes are now beginning to occur to maximize biodiversity by means of integrated pest management, pasture and forage management, the incorporation of trees into agriculture, and irrigation management, and with small and patch systems. SI is central to the Sustainable Development Goals of the United Nations and to wider efforts to improve global food and nutritional security.
A “digital revolution” in agriculture is underway. Advanced technologies like sensors, artificial intelligence, and robotics are increasingly being promoted as a means to increase food production efficiency while minimizing resource use. In the process, agricultural digitalization raises critical social questions about the implications for diverse agricultural labourers and rural spaces as digitalization evolves. In this paper, we use literature and field data to outline some key trends being observed at the nexus of agricultural production, technology, and labour in North America, with a particular focus on the Canadian context. Using the data, we highlight three key tensions observed: rising land costs and automation; the development of a high-skill/low-skilled bifurcated labour market; and issues around the control of digital data. With these tensions in mind, we use a social justice lens to consider the potential implications of digital agricultural technologies for farm labour and rural communities, which directs our attention to racial exploitation in agricultural labour specifically. In exploring these tensions, we argue that policy and research must further examine how to shift the trajectory of digitalization in ways that support food production as well as marginalized agricultural labourers, while pointing to key areas for future research—which is lacking to date. We emphasize that the current enthusiasm for digital agriculture should not blind us to the specific ways that new technologies intensify exploitation and deepen both labour and spatial marginalization.
J. F. Velasco-Muñoz, J. F. Mendoza, J. A. Aznar-Sánchez
et al.
Abstract In the current context of resource scarcity, global climate change, environmental degradation, and increasing food demand, the circular economy (CE) represents a promising strategy for supporting sustainable, restorative, and regenerative agriculture. A review of the literature on CE confirms the initial hypothesis that the theoretical CE framework has not yet been adapted to the field of agriculture. Therefore, this paper overcomes this gap in two ways: i) by adjusting the general CE framework to the agricultural sector's specificities, and ii) by analyzing the scope of the indicators available for measuring agricultural production systems’ circularity performance in supporting decision-making processes. Accordingly, the different elements in the theoretical CE framework are adapted to agricultural production systems. One major contribution of this paper is the definition of CE applied to agriculture. In addition, the principles of CE are adapted to the field, and CE strategies for agricultural activity are defined. Forty-one circularity indicators for application in agricultural systems were also comprehensively assessed to determine their strengths and weaknesses. Building on the key findings, future research paths and changes at the institutional and normative levels are proposed to facilitate CE implementation in agricultural production systems. For example, internationally recognized standards and adequate units of measurement must be defined, to develop meaningful studies and determine agricultural activities’ circularity performance.
T. T. Santos, Leonardo L. de Souza, A. Santos
et al.
Agricultural applications as yield prediction, precision agriculture and automated harvesting need systems able to infer the culture state from low cost sensing devices. Proximal sensing using affordable cameras combined to computer vision have seen a promising alternative, strengthened after the advent of convolutional neural networks (CNNs) as an alternative for challenging pattern recognition problems in natural images. Considering fruit growing monitoring and automation, a fundamental problem is the detection, segmentation and counting of individual fruits in orchards. Here we show that for grape wines, a crop presenting large variability in shape, color, size and compactness, grape clusters can be successfully detected, segmented and tracked using state-of-the-art CNNs. In a dataset containing 408 grape clusters from images taken on field, we have reached a F1-score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We have also shown as clusters can be identified and tracked along video sequences recording orchards rows. We also present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. The presented pipeline for annotation, training, evaluation and tracking of agricultural patterns in images can be replicated for different crops and production systems. It can be employed on the development of sensing components for several agricultural and environmental applications.
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.
Access to credit is essential for transforming South Africa’s agricultural sector, particularly in enhancing value chain sustainability. This study investigated the role of credit access in supporting smallholder farmers’ value chain sustainability, as part of a broader project focused on developing a credit risk model for South African farmers. Data were collected from 223 SAFDA farmers in KwaZulu-Natal and Mpumalanga using a structured questionnaire. The average treatment effects (ATEs) of a propensity score matching (PSM) model was used to estimate the impacts of credit on the following four key variables: farm ownership, farm size, farm income, and farm assets. The results showed that farm ownership was associated with credit access, as ownership provided 1.84 times the chances of loan approval. Additionally, farm income increased by ZAR 2,849,398 for credit recipients compared to non-recipients. This income boost enhances market linkages and food value chain sustainability. This study rejects the hypothesis that credit access has no impact on smallholder farmers, highlighting its vital role in promoting agricultural development and value chain growth. It is recommended that policymakers enhance credit access and risk mitigation strategies to further support smallholder farmers. To improve access to credit for smallholder farmers in South Africa, we recommend the following measures: (1) establishing credit guarantee schemes in partnership with financial institutions to reduce lending risks; and (2) implementing financial education programs for smallholder farmers to enhance their debt management skills. Credit access has the potential to promote positive change across economic, social, and environmental aspects, improving not only the livelihoods of smallholder farmers but also contributing to broader sustainable development goals.