Agricultural Systems最新文献

{"title":"The future of agricultural lands under the combined influence of shared socioeconomic pathways and urban expansion by 2050","authors":"Ali Sadian,&nbsp;Hossein Shafizadeh-Moghadam","doi":"10.1016/j.agsy.2024.104234","DOIUrl":"10.1016/j.agsy.2024.104234","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Food security is increasingly at risk due to rising demand, unprecedented population growth, and challenges such as climate change and urbanization. Consequently, modeling the combined effects of climate change and land conversion on agricultural lands is of strategic importance.</div></div><div><h3>OBJECTIVE</h3><div>This study simulates the combined effects of climate change and urban expansion on agricultural lands in western Iran, a region recognized as the food basket of the country.</div></div><div><h3>METHODS</h3><div>Land use maps for the past 30 years (1990–2020) were generated using Google Earth Engine and a scene-based approach. Urban expansion was simulated using the combination of multilayer perceptron, Markov chain and cellular automata. Data between 2000 and 2010 were used for calibration and data between 2010 and 2020 were used for validation. Subsequently, urban expansion by the year 2050 was simulated. To explore the influence of climate change, three climate models from the general circulation models were evaluated using mean absolute error, mean bias error, and root mean square error. The effects of precipitation deficiency on rainfed farming were investigated through three shared socioeconomic pathways (SSPs): SSP1–2.6, SSP2–4.5, and SSP3–7.0.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Land use maps were generated with overall accuracies ranging from 93 % to 96 %, while the urban expansion model demonstrated an accuracy of 85 %, as determined by the area under the curve. Simulated maps showed figure of merit and producer accuracy values of 63 % and 67 %, respectively. Urban areas expanded by 110 % between 1990 and 2020, with a projected 78 % increase between 2020 and 2050, resulting in the loss of 71 km² of agricultural land. Rainfed farming losses are projected to be insignificant by 2030 and 2040. However, by 2050, losses are estimated to range from 168 to 790 km² in the SSP1–2.6 scenario, 260 to 865 km² in SSP2–4.5, and 135 to 733 km² in SSP3–7.0, depending on precipitation thresholds of 250 mm and 300 mm.</div></div><div><h3>SIGNIFICANCE</h3><div>The study shows that while climate change can damage agricultural lands on a broad scale, urban expansion threatens high-quality irrigated and garden lands, posing serious risks to food security and sustainability. These insights are valuable for governments and decision-makers assessing the future risks posed by climate change and land conversion to agricultural lands.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104234"},"PeriodicalIF":6.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Organic farming enhances the synergy of the water-energy-food-ecology nexus","authors":"Meixi Pan,&nbsp;Zishu Tang,&nbsp;Guishen Zhao","doi":"10.1016/j.agsy.2024.104247","DOIUrl":"10.1016/j.agsy.2024.104247","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Synergies between water-energy-food-ecology (WEFE) nexus essentially embody the sound functioning of complex adaptive systems. As an effective way of promoting the transformation of agricultural systems, it is critical to compare the synergies between conventional and organic farming systems from a nexus perspective, and to explore the contribution of synergies to achieving sustainable development in agriculture.</div></div><div><h3>OBJECTIVE</h3><div>This study aims to develop an integrated framework to facilitate assessment of the complex dynamics and potential for coordinated development of the WEFE nexus under organic and conventional farming. We also try to explore the relationship between system synergies and gross ecosystem product values, identifying key factors that influence systematic synergy.</div></div><div><h3>METHODS</h3><div>In this study, we developed a comprehensive WEFE nexus evaluation index and applied it to a case study of the tea industry in Simao District, Pu'er City, Yunnan Province, China. We employed the coupled coordination degree (CCD) model and Pearson's correlation coefficient to compare the synergies between organic and conventional farming systems. Regression analysis and the co-effect gradation index were used to investigate the relationship between system synergy and gross ecosystem product values. Additionally, the random forest method was applied to identify factors dominating the synergies of the WEFE nexus, providing strategic directions for system optimization.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results show that the sustainable development index and the degree of coordination of the organic tea farming system were 72.41 % and 53.40 % higher, respectively, than those of the conventional tea farming system. Hence, the organic tea farming system displayed clear advantages in terms of enhancing its synergy related to sustainability. Furthermore, the synergies of WEFE nexus were significantly positively correlated with the gross ecosystem product values (GEPV), and a 0.1 enhancement in the CCD of the organic farming is associated with an increase of approximately 4130 CNY of GEPV, which is 2.6 times the marginal benefit of conventional farming. We also find that the ecology and energy subsystems play dominant roles in influencing synergy, and thus improving ecosystem services and energy efficiency can be seen as key strategies for promoting multidimensional coordination.</div></div><div><h3>SIGNIFICANCE</h3><div>This study demonstrates the feasibility of applying the nexus concept to sustainable development in agriculture, illustrating the potential of organic farming for coordinated growth and the substantial benefits that can generated through synergistic optimization of integrated systems. Therefore, enhancing synergies of WEFE nexus can offer valuable insights for advancing the sustainable development of agricultural systems.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104247"},"PeriodicalIF":6.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stories, simulations and narratives: Collaboratively exploring food security and agricultural innovation in sub-Saharan Africa","authors":"Udita Sanga ,&nbsp;Maja Schlüter","doi":"10.1016/j.agsy.2024.104241","DOIUrl":"10.1016/j.agsy.2024.104241","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Food insecurity remains a global challenge, with differing narratives shaping interventions in sub-Saharan Africa. The “crisis narrative,” favored by aid agencies, links insecurity to production issues, advocating agricultural innovations. Meanwhile, the “chronic poverty narrative,” reflected in African policy, ties insecurity to farmer poverty, emphasizing livelihood and economic solutions. Narrative subjectivity can lead to uncritical privileging of certain understandings and solutions, necessitating a critical exploration of contexts, causes, and solutions to food insecurity in the region. Our research addresses the need to understand and illustrate the complex problem of food insecurity in the region.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study employs a mixed-method approach, combining collaborative storytelling, model exploration, and scenario analysis, to investigate food security, agricultural innovation, and climate adaptation in Mali, West Africa.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;We developed a three-stage methodology represented as a story arc: beginning (exposition and problem statement), development (action), and completion (solution), providing a cohesive narrative framework. The arc unfolds with the story exposition introducing characters, plot, and problem statement. The story development includes participant-led model simulations and modeler-led scenario analysis. The story completion integrates insights from model simulations and scenario analysis to develop the collective understanding of the narratives surrounding food (in)security.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;This study generates several insights that highlight the inherent complexities within agricultural innovation systems that emerge from the non-linear dynamic interaction of actors operating across scales that contribute to food insecurity. We redirect the focus of narratives of causes (and subsequent solutions) of food insecurity from solely climate-driven production losses and poverty to the complex interplay of climate, agroecology, innovation networks, risk perception, innovation beliefs, desires, and knowledge transmission. A shared narrative emerges, characterizing food security as a complex adaptive system influenced by factors such as climate-induced production variability, agroecological heterogeneity, network structures and climate risk perception. The study underscores the methodological value of collaborative storytelling and model simulation to enable a structured and reflective exploration of these complex systems. By transforming participants into co-creators of knowledge, this methodology fosters systems thinking, turning abstract systemic relationships into tangible, actionable insights.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;Our study demonstrates the need to critically reevaluate the role of narratives in shaping agricultural innovation systems and their capacity to transform food systems t","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104241"},"PeriodicalIF":6.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Environmental effects of improved regional nitrogen cycling in crop-livestock systems – A generic modelling approach","authors":"W.J. Vonk ,&nbsp;A.G.T. Schut ,&nbsp;M.K. van Ittersum ,&nbsp;M. Grillot ,&nbsp;C.F.E. Topp ,&nbsp;R. Hendriks ,&nbsp;R. Hijbeek","doi":"10.1016/j.agsy.2024.104244","DOIUrl":"10.1016/j.agsy.2024.104244","url":null,"abstract":"<div><h3>CONTEXT</h3><div>More nutrient cycling may be achieved by using less external inputs (feed, fertilisers) and reduce losses to the environment, especially in intensive farming systems. Yet, changes in on-farm management may have unintended consequences at higher aggregation scales due to potential trade-offs.</div></div><div><h3>OBJECTIVE</h3><div>The objective of this study was to develop a multi-indicator and multi-level model which operates at farm and regional level to evaluate scenarios for improved nitrogen cycling.</div></div><div><h3>METHODS</h3><div>A new model, based on nitrogen flow analysis, was used to compare five scenarios with the current situation as reference. The model was applied to a case study region, the Dutch province Drenthe including typical arable, pig, poultry, and dairy farms. In the scenarios, the proportion of manure digested for biogas production, and imported amounts of synthetic fertiliser and feed into the region were varied, as single measures or in combination.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Modelling results showed that digestion of manure for biogas production reduced total regional nitrogen losses and produced renewable energy. A 20 % decrease in synthetic nitrogen fertiliser application reduced crop yields only slightly and improved the regional nitrogen use efficiency and nitrogen cycling, as manure availability in Drenthe was sufficient to meet a large proportion of the total crop nutrient requirements. Combining multiple measures was most effective in increasing nitrogen cycling (+65 %), leading to reduced greenhouse gas emissions (−49 %) and an improved net energy balance (+84 %) from agriculture in Drenthe, with the largest contribution coming from restricting feed import (resulting in a reduction of the total livestock herd in the region). However, when livestock was reduced, more synthetic nitrogen fertiliser was needed to maintain crop yields.</div><div>Our study also highlighted trade-offs: positive effects on nitrogen cycling, greenhouse gas emissions and nitrogen losses coincided with reduced food production and organic matter inputs to soils, with consequences for carbon stocks. Furthermore, results for the whole region were not always representative for each farm type.</div></div><div><h3>SIGNIFICANCE</h3><div>The results demonstrate that our systems approach, quantifying multiple indicators simultaneously at farm and region level, can provide a better understanding of benefits and trade-offs when aiming for an agricultural system which is productive, but with reduced emissions to the environment. The developed model is generic and can be applied to evaluate alternative nitrogen cycling scenarios in other European regions with only little parameterisation needed from publicly available data.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104244"},"PeriodicalIF":6.1,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of APSIM's soil nitrogen modelling capability for agricultural systems analyses","authors":"Kirsten Verburg ,&nbsp;Heather R. Pasley ,&nbsp;Jody S. Biggs ,&nbsp;Iris Vogeler ,&nbsp;Enli Wang ,&nbsp;Henrike Mielenz ,&nbsp;Val O. Snow ,&nbsp;Chris J. Smith ,&nbsp;Chiara Pasut ,&nbsp;Andrea D. Basche ,&nbsp;Di He ,&nbsp;Sotirios V. Archontoulis ,&nbsp;Donald S. Gaydon ,&nbsp;Neil I. Huth ,&nbsp;Dean P. Holzworth ,&nbsp;Joanna M. Sharp ,&nbsp;Rogerio Cichota ,&nbsp;Edith N. Khaembah ,&nbsp;Edmar I. Teixeira ,&nbsp;Hamish E. Brown ,&nbsp;Peter J. Thorburn","doi":"10.1016/j.agsy.2024.104213","DOIUrl":"10.1016/j.agsy.2024.104213","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Over the last 26 years, researchers globally have successfully applied the soil nitrogen (N) model in the Agricultural Production Systems sIMulator (APSIM) to simulate N cycling and its effects on crop production across a range of agricultural systems and environments. As the modelling community further expands its focus to include environmental impacts of farming, it needs the model to be fit for this broader purpose.</div></div><div><h3>OBJECTIVE</h3><div>Accurately modelling N loss via different pathways demands more of the model and so, to inform and prioritise future development needs, we embarked on a detailed review of APSIM's soil N modelling capability.</div></div><div><h3>METHODS</h3><div>We conducted a comprehensive search of APSIM Soil N model verification studies and found 131 relevant publications across a wide range of systems, applications, and processes. We examined their approaches and findings, and distilled out the lessons learnt.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The model-data comparisons showed strong performance across all modelled processes, despite limited changes to the core of the soil N model since its inception. The model's relatively simple conceptual pool approach to modelling carbon (C) dynamics with N cycling linked via C:N ratios, has proven remarkably versatile. However, these conceptual pools have posed challenges relating to initialisation methods and the resulting sensitivity of predictions at different time scales, e.g. long-term C trajectories vs. short-term seasonal N dynamics. Correctly predicting timing of N loss on a daily timestep also proved challenging, but this level of resolution may not always be required. APSIM's adaptable code structure facilitated the creation of model prototypes (e.g., ammonia volatilisation and N in runoff) allowing testing of different conceptualisations ahead of formal release.</div></div><div><h3>SIGNIFICANCE</h3><div>APSIM is one of the most widely used agricultural systems models. This review, which covers model documentation, model-data comparisons, various approaches to parameterisation, and prototypes for additional processes, consolidates decades of research into insights about the model and its functioning. The review highlights the importance of model evaluations across a wide range of applications to ensure model robustness, to identify issues that may be masked in single studies, and to allow the emergence of solutions with broad applicability.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104213"},"PeriodicalIF":6.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany","authors":"Joseph MacPherson ,&nbsp;Anna Rosman ,&nbsp;Katharina Helming ,&nbsp;Benjamin Burkhard","doi":"10.1016/j.agsy.2024.104222","DOIUrl":"10.1016/j.agsy.2024.104222","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;The transition to digital agriculture is likely to lead to systemic changes that will affect production, consumption, governance, and the wider environment of agricultural systems. Nevertheless, the absence of sufficient evidence and ambiguities in perspectives create an ongoing lack of clarity regarding the potential impacts of digital agriculture. Therefore, to discern potential impacts while addressing system complexities, uncertainties, as well as normative aspects associated with this transition, future-oriented and participatory assessments are needed that actively involve diverse knowledge and values of affected stakeholders.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This research aims to explore the impacts and processes of agricultural digitalization according to stakeholders. The objectives are to identify key areas of impact that digital agriculture is likely to influence, identify and explore the causal pathways linking digital agriculture to impacts, and quantitatively examine the uncertainties of stakeholder perceptions associated with these impacts and causal pathways.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Through a participatory modelling procedure, diverse stakeholders from the German region of Brandenburg constructed a Bayesian Belief Network (BBN). The BBN facilitated the identification of the main impacts of digital agriculture and allowed for the modelling of uncertainties associated with these impacts.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;Stakeholders perceived several socioeconomic advantages of digitalization, particularly in terms of bolstering economic stability through improved risk management and enhanced resource use efficiency, validating existing claims in the literature. The perception seems to be influenced by highly variable yields and market uncertainties, as well as shortages in labour in the region. On the other hand, there was significant uncertainty among stakeholders concerning landscape diversification and its impact on biodiversity. This uncertainty arises from the potential profitability of cultivating marginal land under heightened digitalization-induced efficiency, posing a risk of diminishing natural habitat and landscape heterogeneity. Local historical trends toward landscape simplification as result of technology-driven efficiency improvements may be a cause for this perception.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;This study contributes to a growing body of future-oriented research assessing the impacts of digital agriculture through engaging stakeholder knowledge and values. While there is theoretical potential for digitalization to enhance biodiversity, realizing such positive impacts is improbable without improved communication and policy incentives, given the historical trend of efficiency-driven pathways. This study introduces a novel approach to assessing the impacts of agricultural digitalization through the application of a participatory Bayesian b","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104222"},"PeriodicalIF":6.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing off-season maize production through tailored nitrogen management and advanced cultivar selection techniques","authors":"Bruno Rodrigues de Oliveira ,&nbsp;Rafael Felippe Ratke ,&nbsp;Fábio Steiner ,&nbsp;Abdulaziz A. Al-Askar ,&nbsp;Jorge González Aguilera ,&nbsp;Amr H. Hashem ,&nbsp;Mohamed S. Sheteiwy ,&nbsp;Renato Lustosa Sobrinho ,&nbsp;Mohamed A. El-Tayeb ,&nbsp;Hamada AbdElgawad ,&nbsp;Luis Morales-Aranibar ,&nbsp;Luciano Façanha Marques ,&nbsp;Alan Mario Zuffo","doi":"10.1016/j.agsy.2024.104239","DOIUrl":"10.1016/j.agsy.2024.104239","url":null,"abstract":"<div><h3>Context</h3><div>Climate change can trigger excessive rainfall, making mechanized soybean harvesting unfeasible. The off-season maize cultivation can benefit from soybean-maize rotation system, inoculated with <em>Bradyrhizobium</em> spp. strains, as a potential biological source of nitrogen (N). To meet the nutritional demand of maize crops, N fertilization management is essential. Recent research has sought to understand how maize cultivars respond to mineral N application.</div></div><div><h3>Objective</h3><div>In this work, we used a modern methodology to select maize cultivars with greater response to different application inputs of mineral N fertilizer, including N derived from soybean crop residues.</div></div><div><h3>Methods</h3><div>We used the Manhattan distance to verify the similarity between the responses of four maize cultivars (30F53VYHR, AG8700 PRO3, B2433PWU, and SYN7G17 TL) that were either unfertilized or fertilized with 40, 80, 120, and 160 kg N ha⁻¹. The Technique for Order of Preference by Similarity to the Ideal Solution method was applied to select the most responsive cultivar.</div></div><div><h3>Results and conclusions</h3><div>Among the four maize cultivars, SYN7G17TL and AG8700PRO3 are more responsive to N fertilizer application in medium and high-fertility agricultural soils, respectively. When soil fertility levels are disregarded, the AG8700PRO3 cultivar has greater potential response to N fertilization, agreeing with previous studies.</div></div><div><h3>Significance</h3><div>The proposed approach is easy to use and adapt and provides an appropriate mechanism for selecting maize cultivars sown in areas with soybean residues, thus contributing to more sustainable planting as it adequately assesses nitrogen management.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104239"},"PeriodicalIF":6.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterization of common bean production regions in Brazil using machine learning techniques","authors":"Ludmilla Ferreira Justino ,&nbsp;Alexandre Bryan Heinemann ,&nbsp;David Henriques da Matta ,&nbsp;Luís Fernando Stone ,&nbsp;Paulo Augusto de Oliveira Gonçalves ,&nbsp;Silvando Carlos da Silva","doi":"10.1016/j.agsy.2024.104237","DOIUrl":"10.1016/j.agsy.2024.104237","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;PROBLEM&lt;/h3&gt;&lt;div&gt;Understanding the interactions between genotype, environment, and management is crucial for guiding the development of new cultivars and defining strategies to maximize yield under specific environmental conditions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study aimed to classify and characterize homogeneous production regions for common beans in Brazil by leveraging simulated yield data and machine learning techniques. The goal was to identify the environmental factors that define these homogeneous regions and to develop a spatiotemporal sowing calendar for rainfed (wet and dry seasons) and irrigated (winter season) production.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;The CSM-CROPGRO-Dry Bean model was used to simulate the yield of common beans in Brazilian municipalities during the wet (rainfed) season (sowing between August and December - 275 municipalities), dry (rainfed) season (sowing between January and April - 251 municipalities) and winter (irrigated) season (sowing between April and July - 59 municipalities), utilizing soil data, daily climate data (1980 to 2016), management information (sowing date and irrigation/rainfed), and genetic coefficients to reflect the performance of the BRS Estilo cultivar related to phenology, growth and yield components. To create homogeneous environmental groups and associate them with specific environmental features, we applied machine learning techniques, including K-means clustering and decision tree analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS&lt;/h3&gt;&lt;div&gt;According to the results, we identified three distinct homogeneous regions — high, medium, and low yields — for each cultivation season (wet, dry, and winter). During the wet season, regions with yields between 2326 and 3500 kg ha&lt;sup&gt;−1&lt;/sup&gt; were classified as high-yield, those between 1404 and 2325 kg ha&lt;sup&gt;−1&lt;/sup&gt; as medium-yield, and those between 500 and 1403 kg ha&lt;sup&gt;−1&lt;/sup&gt; as low-yield. In the dry season, high-yield regions had yields ranging from 2492 to 3500 kg ha&lt;sup&gt;−1&lt;/sup&gt;, medium-yield regions from 1484 to 2491 kg ha&lt;sup&gt;−1&lt;/sup&gt;, and low-yield regions from 500 to 1483 kg ha&lt;sup&gt;−1&lt;/sup&gt;. For the winter season, high-yield regions achieved yields between 2972 and 3500 kg ha&lt;sup&gt;−1&lt;/sup&gt;, medium-yield regions between 2252 and 2971 kg ha&lt;sup&gt;−1&lt;/sup&gt;, and low-yield regions between 634 and 2251 kg ha&lt;sup&gt;−1&lt;/sup&gt;. For rainfed seasons (wet and dry), the water stress (WSPD) had a greater impact on yield than air temperature and global solar radiation. While in the winter season, air temperature was the most relevant factor. Overall, in the wet season, delayed sowing contributed to increased yield, especially in the state of Paraná. In the dry season, delayed sowing caused a reduction in yield, particularly in the Midwest and Southeast regions. In the winter season, yield varied less significantly between sowing dates, except in the state of Mato Grosso, where harmful increases in air temperature were observed in the later","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104237"},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing simulations of biomass and nitrous oxide emissions in vineyard, orchard, and vegetable cropping systems","authors":"Mu Hong ,&nbsp;Yao Zhang ,&nbsp;Lidong Li ,&nbsp;Keith Paustian","doi":"10.1016/j.agsy.2024.104243","DOIUrl":"10.1016/j.agsy.2024.104243","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Nitrous oxide (N&lt;sub&gt;2&lt;/sub&gt;O) is a potent greenhouse gas with a high global warming potential. Specialty crop (SC) systems, including vineyards, orchards, and vegetable farms, are among the highest value crops grown and emit N&lt;sub&gt;2&lt;/sub&gt;O. Knowledge regarding N&lt;sub&gt;2&lt;/sub&gt;O emissions from SCs remains limited, necessitating simulations using process-based models. However, model calibration and validation for SC biomass dynamics and N&lt;sub&gt;2&lt;/sub&gt;O emissions are lacking.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study aimed to: 1) conduct calibrations and validations of DayCent®, a process-based model, for SC biomass dynamics in eight SC systems with diverse pedological and climatic conditions; 2) evaluate N&lt;sub&gt;2&lt;/sub&gt;O emission simulations based only on calibrating crop-specific parameters; and 3) simulate N&lt;sub&gt;2&lt;/sub&gt;O emissions of each SC production region in California as a case study.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;A comprehensive dataset of 408 biomass carbon (C) and nitrogen (N) and 185 N&lt;sub&gt;2&lt;/sub&gt;O emission observations from global field studies in eight SC systems was compiled. The DayCent model was calibrated and validated for SC biomass dynamics and N&lt;sub&gt;2&lt;/sub&gt;O emissions across various management treatments, soils, and climates. Current yield-scaled N&lt;sub&gt;2&lt;/sub&gt;O emissions were simulated for each SC among major production regions in California.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;Calibration on crop-specific parameters of DayCent based on the biomass collection can generally improve model performance for simulating biomass C (MRE = 0.148, RRMSE = 0.368, R&lt;sup&gt;2&lt;/sup&gt; = 0.913, IA = 0.976), N (MRE = 0.549, RRMSE = 0.711, R&lt;sup&gt;2&lt;/sup&gt; = 0.298, IA = 0.730), and N&lt;sub&gt;2&lt;/sub&gt;O emissions (MRE = 1.226, RRMSE = 0.971, R&lt;sup&gt;2&lt;/sup&gt; = 0.407, IA = 0.772), compared with using original crop-specific parameterization for biomass C (MRE = 0.309, RRMSE = 0.847, R&lt;sup&gt;2&lt;/sup&gt; = 0.589, IA = 0.865), N (MRE = 1.327, RRMSE = 0.996, R&lt;sup&gt;2&lt;/sup&gt; = 0.001, IA = 0.366), and N&lt;sub&gt;2&lt;/sub&gt;O emissions (MRE = 1.454, RRMSE = 1.102, R&lt;sup&gt;2&lt;/sup&gt; = 0.326, IA = 0.728), which also outperformed the 2019 refined IPCC Tier 1 method (MRE = 4.085, RRMSE = 1.835, R&lt;sup&gt;2&lt;/sup&gt; = 0.031, IA = 0.448). Yield-scaled annual N&lt;sub&gt;2&lt;/sub&gt;O emissions averaged over the major production areas in California were 0.45, 0.18, 0.28, 0.46 kg N&lt;sub&gt;2&lt;/sub&gt;O_N MgC&lt;sup&gt;−1&lt;/sup&gt; yr&lt;sup&gt;−1&lt;/sup&gt; for vineyards, almond, peach, and walnut orchards, and 1.02, 1.41, 1.18, and 1.37 kg N&lt;sub&gt;2&lt;/sub&gt;O_N MgC&lt;sup&gt;−1&lt;/sup&gt; yr&lt;sup&gt;−1&lt;/sup&gt; for lettuce, broccoli, cauliflower, and tomato cropping systems, respectively. The case study identified high-emission regions and highlighted the spatial and temporal N&lt;sub&gt;2&lt;/sub&gt;O emission variations at the county level.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;This is one of the very few comprehensive studies that compiled the largest dataset of biomass and N&lt;sub&gt;2&lt;/sub&gt;O emissions from SC systems, as wel","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104243"},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of silvopasture, paddock trees and linear agroforestry systems on agricultural productivity: A global quantitative analysis","authors":"Thomas P. Baker ,&nbsp;Jacqueline R. England ,&nbsp;Shaun T. Brooks ,&nbsp;Stephen B. Stewart ,&nbsp;Daniel Mendham","doi":"10.1016/j.agsy.2024.104240","DOIUrl":"10.1016/j.agsy.2024.104240","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Agroforestry provides numerous benefits to agricultural landscapes, including timber production, carbon sequestration and enhanced biodiversity. Critically, agroforestry also influences the productivity of pasture, crops, and livestock. The magnitude and direction of the effect, however, is highly variable due to factors including the type of agroforestry (e.g., windbreak, alley, silvopasture), the condition of the trees (e.g., height, age, species), planting location and climatic conditions. However, currently there is limited information that quantifies how variation in these drivers affects the influence of agroforestry on agricultural yields.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVES&lt;/h3&gt;&lt;div&gt;In this quantitative review we aimed to determine the magnitude of the effect that silvopasture, paddock trees and linear agroforestry systems have on agricultural productivity relative to a treeless comparison. In addition, we attempted to understand how the effect of agroforestry varied with factors such as tree condition, climate, and weather.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;A global literature review was conducted examining two key agroforestry types (non-intercropped linear systems such as windbreaks and hedges, and dispersed pasture systems such as paddock trees and silvopasture). Agricultural productivity responses of these systems compared to a treeless control were extracted and the size of the effect was compared to a range of conditions of the agroforestry systems e.g. tree density, age, distance to tree, as well as a range of climate and weather conditions.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;For the agroforestry types examined we found a strong evidence base for the effects on crop/pasture growth for linear agroforestry types (windbreaks, alleys) and on pasture growth in paddock tree/silvopasture systems (crop growth was not examined in these systems). There was limited information on the effects of linear agroforestry systems on livestock production. Linear agroforestry features were generally beneficial for crop and pasture growth. By comparison, silvopasture systems resulted in a reduction in both pasture and livestock productivity, although such systems are likely to provide other benefits for mitigating risk. Tree condition was a major factor driving effect size, the most prominent drivers being paddock size in linear configurations, and tree density in silvopasture systems. Climate variables also influenced the effect of agroforestry on productivity, indicating that both local and seasonal climate variation needs be considered when predicting effect sizes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;This study provides important baseline information for valuing the effects linear and dispersed agroforestry types have on farm productivity and predicting under what conditions these effects will be optimised. Such information will aid in designing and implementing effective agroforestry systems.&lt;/div&gt;&lt;/d","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104240"},"PeriodicalIF":6.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}