Agricultural Systems最新文献

{"title":"Climatic constraints behind spatial and temporal variability of wheat yields in the Pampa region of Argentina","authors":"María Elena Fernández-Long ,&nbsp;Santiago Alvarez Prado ,&nbsp;Daniel J. Miralles","doi":"10.1016/j.agsy.2024.104217","DOIUrl":"10.1016/j.agsy.2024.104217","url":null,"abstract":"<div><h3>Context</h3><div>The Argentine wheat belt region is centered on the humid pampas, i.e. the main wheat producing area of <!--> <!-->Argentina and is considered at global scale one of the most important in the world for its production of cereals and oilseeds crops. In this whole area wheat is exposed to contrasting climatic conditions.</div></div><div><h3>Objectives</h3><div>The main objective was to study, at a regional scale, the spatial and temporal variability of wheat yields in Argentina and its relationship with climatic conditions. We seek to answer 3 questions: (a) How does climate affect the spatial distribution of wheat yields in the Pampas region? (b) What are the main agroclimatic variables that modulate the temporal variability of wheat yields in the Pampas region? and (c) Based on what is identified in “b”, is it possible to determine areas of homogeneous behavior?</div></div><div><h3>Methods</h3><div>A large agrometeorological data base was contrasted with the yield of wheat recorded in the main area of wheat production in Argentina from 1985 to 2021. The CRONOTRIGO (<span><span>http://cronotrigo.agro.uba.ar/</span><svg><path></path></svg></span>) model was used to establish the phenology of different wheat cultivars for a range of optimal sowing dates and associate phenological stages to the climatic variables.</div></div><div><h3>Results and conclusions</h3><div>In the studied area average yield ranged from 1200 to 3600 Kg ha⁻¹, with the lowest and the highest yields in the north and southeast region, respectively. The combination of yield variability with agroclimatic variables allowed the identification of two mega-environments, mainly affected by the water deficits and by the VPD during the critical period, maximum temperatures, and heat shock events during grain-filling period at the west, and photothermal quotient during the critical period at the east. Mega-environment located at the east showed a lower climatic influence over yield variability than that located at the west of the wheat belt. This analysis allowed us to identify homogeneous regions in terms of agroclimatic variables affecting yield variability in wheat.</div></div><div><h3>Significance</h3><div>This information provides insight for understanding the spatio-temporal variation in wheat yield, which will help to reduce yield gaps by adjusting agronomic management according to the main environmental constraint.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104217"},"PeriodicalIF":6.1,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182025","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":"Work in agriculture: A blind spot in reducing pesticide use on dairy farms?","authors":"Julia Denantes ,&nbsp;Anne-Lise Jacquot ,&nbsp;Matthieu Carof ,&nbsp;Carole Ropars-Collet ,&nbsp;Aude Ridier","doi":"10.1016/j.agsy.2024.104221","DOIUrl":"10.1016/j.agsy.2024.104221","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Implementing alternative practices to the use of pesticides involves work issues that can limit adoption of these practices, particularly on dairy farms. Depending on how practices change, work organization may completely change, additional skills and knowledge may be required, and system complexity may increase, inducing a higher mental workload. This can result in an excessive total workload for already overburdened farmers.</div></div><div><h3>OBJECTIVE</h3><div>The present study examines whether work limits the implementation of alternative practices to reduce pesticide use on dairy farms, depending on the amount of change the practices require. Three work dimensions were considered: work organization, skills and knowledge, and physical and mental workload.</div></div><div><h3>METHODS</h3><div>We used nine semi-structured interviews with crop and livestock experts in Brittany (France's leading region for dairy production) to classify multiple categories of practices by their degree of difficulty and the type of work dimension involved, with a specific focus on three practices: using resistant crop varieties, mechanical weed control and extending crop rotations.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>First, experts emphasized certain alternative practices over others depending on the institution to which they belonged. Second, the need to balance the feeding system and an increase in herd size, which is not compatible with all alternative practices, were barriers specific to dairy farms. Third, all three practices we focused on were skill- and knowledge-intensive, but in different ways. Using resistant crop varieties requires access to the right information, while mechanical weed control raises issues of training, investment or outsourcing. Finally, extending crop rotations, considered as a redesign of the production system, requires obtaining the resources, time and autonomy to think about new rotations.</div></div><div><h3>SIGNIFICANCE</h3><div>This study shows that, in addition to work organization, other work dimensions are crucial for reducing pesticide use on dairy farms.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104221"},"PeriodicalIF":6.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182023","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":"Factors to scale out innovative organic farming systems: A case study in Flanders region, Belgium","authors":"Alba Alonso-Adame ,&nbsp;Siavash Farahbakhsh ,&nbsp;Jef Van Meensel ,&nbsp;Fleur Marchand ,&nbsp;Steven Van Passel","doi":"10.1016/j.agsy.2024.104219","DOIUrl":"10.1016/j.agsy.2024.104219","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Sustainability transitions in agri-food systems are expected to reduce their negative environmental and social impacts. On the other hand, Europe demands an increase in the agricultural land under organic farming by 2030. Innovations in agri-food systems, especially in the organic sector, could close the gap in sustainability transitions and the foreseen conversion to organic farming.</div></div><div><h3>OBJECTIVE</h3><div>In this study, we developed a participatory agent-based model combined with qualitative scenarios to understand which factors play a role in scaling out innovations in the organic sector and further study potential scenarios in the region of Flanders, Belgium.</div></div><div><h3>METHODS</h3><div>Agent-based modeling is a computational simulation environment able to represent complex systems where relevant actors behave and interact with each other. This modeling approach can be combined with qualitative scenarios to elucidate potential futures for a specific context.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>A strong trend for innovative and organic food, available groups of consumers in public institutions for collaboration, subsidies to start up, and a robust farm network can help farms to adopt a sustainable innovative collaboration with public institutions. However, land availability in the Flemish context may restrain this scaling out of farm innovation.</div></div><div><h3>SIGNIFICANCE</h3><div>Combining agent-based models with qualitative scenarios in a participatory approach can integrate the expertise of different stakeholders for sustainability transitions. Pragmatically, it can illustrate how a sustainability transition may take place under potential scenarios.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104219"},"PeriodicalIF":6.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143183154","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":"Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions","authors":"Jinhui Zheng ,&nbsp;Shuai Zhang","doi":"10.1016/j.agsy.2024.104215","DOIUrl":"10.1016/j.agsy.2024.104215","url":null,"abstract":"<div><h3>CONTEXT</h3><div>The comparison of agricultural models and the conduct of crop improvement research have garnered significant attention in recent times. One of the primary objectives in this field is to pinpoint and mitigate the uncertainties inherent in modeling the effects of climate on crop growth and productivity. Enhancing the precision and reliability of crop models has emerged as a critical concern.</div></div><div><h3>OBJECTIVE</h3><div>In this study, we calibrate and validate four wheat phenology models using wheat phenology data from 1990 to 2009. More importantly, we explain three significant sources of uncertainty in wheat phenology models, namely model structure, model parameters, and weather data inputs.</div></div><div><h3>METHODS</h3><div>This study examines four wheat models—the GLAM-Wheat model, APSIM-Wheat model, SPASS-Wheat model, and WOFOST model—to simulate phenological changes across 32 agricultural meteorological stations in the North China Plain. Additionally, the three main sources of uncertainty in the model are quantified using the Markov Chain Monte Carlo (MCMC) method.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results indicate that all four wheat phenological models effectively simulate the growth of wheat in the study area, with an average RMSE ranging from 4.4 to 5.2 days for the heading stage and from 4.7 to 5.6 days for the maturity stage. The uncertainty analysis encompasses parameters, squared bias, weather data inputs, and model structure. During the heading stage, the overall contributions of these uncertainties are 8.9 %, 40.8 %, 47.4 %, and 2.9 %, respectively. During the maturity stage, these contributions are 11.2 %, 51.2 %, 35.0 %, and 2.6 %, respectively. Weather data inputs are identified as the primary sources of uncertainty.</div></div><div><h3>SIGNIFICANCE</h3><div>This study quantifies the uncertainty within wheat phenology models, a critical step towards enhancing the precision and dependability of crop models. Such efforts hold substantial importance in shaping agricultural policies and refining management practices, ultimately aiding in tackling the challenges posed by impending climate change.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"224 ","pages":"Article 104215"},"PeriodicalIF":6.1,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142758956","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":"Feed the green for a sustainable and protein-efficient dairy production","authors":"Maria Wild ,&nbsp;Martin Komainda ,&nbsp;Katharina Bettin ,&nbsp;Karin Jürgens ,&nbsp;Johannes Isselstein","doi":"10.1016/j.agsy.2024.104216","DOIUrl":"10.1016/j.agsy.2024.104216","url":null,"abstract":"<div><h3>CONTEXT</h3><div>In modern intensive dairy farming, cows are increasingly held indoors and fed arable crops instead of grass to maximize individual animal performance. This leads to environmental issues such as high farm-level nutrient surpluses and loss of grassland plant species diversity as well as a growing competition between food and feed.</div></div><div><h3>OBJECTIVE</h3><div>We conducted this study to define a threshold of concentrate supplementation that ensures a net contribution to the protein supply and evaluate the environmental performance of dairy farms when this level of supplementation is shifted.</div></div><div><h3>METHODS</h3><div>In a first step, we calculated the hePCR (human-edible protein conversion ratio) of 52 dairy farms across a pedo-climatic gradient with varying feeding strategies. Based on farm management data and vegetation surveys, we analyzed the relationship between hePCR and different components of environmental and productive performance, with special interest on farm nutrient balances, grassland biodiversity and grass-based milk production.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Our results show that higher concentrate supplementation levels significantly reduce the efficiency of converting plant protein into food. A critical threshold was identified at a concentrate milk proportion of 30 % or 177 g of concentrate feed per kilogram of milk produced, beyond which net protein contribution shifts to net consumption. Furthermore, we show critical interlinkages between a high protein efficiency and an enhanced environmental performance of the farms, such as higher grassland Shannon diversity and reduced nutrient surpluses. Our study suggests grass-based dairy farming as an integrated solution for enhancing net protein output while simultaneously safeguarding critical ecosystem functions.</div></div><div><h3>SIGNIFICANCE</h3><div>We are in urgent need of sustainable agricultural practices that align an efficient food production with the reduction of negative environmental impacts. Our study is the first that shows direct positive interlinkages between the protein conversion efficiency of dairy farms and their environmental outcome as based on multi-annual management data and comprehensive vegetation surveys.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104216"},"PeriodicalIF":6.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757807","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":"Conserving cropland resilience space in alpine agro-pastoral ecotones: A quantitative study of Qinghai Province","authors":"Hongyu Wang ,&nbsp;Rongrong Ma ,&nbsp;Juan Wang ,&nbsp;Huizhong Zhang ,&nbsp;Wei Zhou","doi":"10.1016/j.agsy.2024.104211","DOIUrl":"10.1016/j.agsy.2024.104211","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Influenced by natural conditions, geographic location and socioeconomics, Qinghai Province is a typical alpine agro-pastoral ecotone; additionally, its food production system cannot cope with many risks, and exploring the suitability of resilient cropland spaces and rational utilization paths is of practical significance.</div></div><div><h3>OBJECTIVE</h3><div>The objectives of this study are to explore the influencing factors of the resilience space of cropland in the alpine agro-pastoral ecotone and to establish a method to quantify the quantity and quality of resilience space. Finally, recommendations for the development and utilization of cropland resilience space and cropland protection in Qinghai Province are presented.</div></div><div><h3>METHODS</h3><div>This study takes cropland system resilience as the theoretical basis and creates a conceptual model of the factors influencing cropland resilience space. The Cropland Resilience Space Index (CRSI) is constructed from the three factors of suitability, scale and aggregation and is used to quantify the spatial suitability of cropland resilience in Qinghai Province.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>The results revealed that there are 441,000 ha of cropland resilience space in Qinghai Province, low values of the suitability factor are distributed mainly in the edge areas of the Kunlun Mountains and the Qilian Mountains, and the scale factor and aggregation factor are generally high in the west and low in the east. The average CRSI value in Qinghai Province is 0.445, and that in Haixi Prefecture is 0.469, which is higher than the average value. Priority should be given to the development of cropland resilience space in the eastern part of Dulan County and Golmud city based on utilization sustainability and ecological stability.</div></div><div><h3>SIGNIFICANCE</h3><div>This research will help enrich research methods for the sustainable utilization of cropland and help promote the transformation of cropland protection policies from rigid to resilient. The quantitative results of the cropland resilience space in the alpine agriculture and animal husbandry intertwined zone can provide a reference basis for rationally formulating the policies of cropland protection and resilience space development and utilization.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104211"},"PeriodicalIF":6.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757806","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":"Estimating environmental efficiency in dairy production using by-production technology","authors":"Yashree Mehta ,&nbsp;Marion Reichenbach ,&nbsp;Bernhard Brümmer ,&nbsp;Eva Schlecht","doi":"10.1016/j.agsy.2024.104200","DOIUrl":"10.1016/j.agsy.2024.104200","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Milk production in developing countries is characterized by low per animal yield and disproportionately high GHG emissions. Specific policy recommendations are necessary to improve the technical as well as environmental efficiency of dairy production, especially for small farms. However, limited financial resources owned by producers lead to high transaction costs of introducing change. Both, concentrate feed and roughage, that is dry nonconcentrates, are used in milk production. These inputs are also responsible for methane emissions through enteric fermentation. Especially cellulose-rich dry nonconcentrates are fueling methane emissions through enteric fermentation.</div></div><div><h3>OBJECTIVE</h3><div>Based on a systems approach including nutritional foundations, we estimated technical and environmental efficiency of dairy producers in the rural-urban interface of Bengaluru, India. We studied the shortfall in milk production and excess methane emissions for each dairy farm and their drivers.</div></div><div><h3>METHODS</h3><div>Using panel data of 245 dairy producers, we fitted the production frontier for estimating technical efficiency and treated the emission generating technology like a cost frontier for estimating environmental efficiency – using stochastic frontier analysis. This study uses the parametric application of by-production technology.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>High heterogeneity in enteric methane emissions at low levels of milk yield are related to excessive feeding of dry nonconcentrates by producers. Plotting excess methane emissions beyond the fitted frontier against the share of concentrates in the total feed ration indicated that a global low is reached at around 40 % concentrates in the cows' diet. Therefore, farmers should intensify production by increasing the share of concentrate feed in dairy cattle rations to this level. Also, we suggest promoting the construction of cattle sheds and increasing the proportion of cows with high milk production potential in the herd to improve environmental efficiency.</div></div><div><h3>SIGNIFICANCE</h3><div>With economic growth as well as an increase in population, the demand for dairy products in developing countries will increase and lead to an expansion of dairy production. GHG emissions such as methane from livestock rearing will have to be managed and adequate policy measures will have to be implemented to reduce their share in global GHG emissions. By integrating animal nutrition perspectives and environmental efficiency from economics into a systems approach, we propose specific recommendations for public policy in terms of the target group of producers and the correct proportion of concentrate feed and dry nonconcentrates in total feed rations for dairy cattle in India. This will ensure that producers reach their full potential in milk production and environmental sustainability.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104200"},"PeriodicalIF":6.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747355","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":"Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials","authors":"Hongkui Zhou ,&nbsp;Fudeng Huang ,&nbsp;Weidong Lou ,&nbsp;Qing Gu ,&nbsp;Ziran Ye ,&nbsp;Hao Hu ,&nbsp;Xiaobin Zhang","doi":"10.1016/j.agsy.2024.104214","DOIUrl":"10.1016/j.agsy.2024.104214","url":null,"abstract":"<div><h3>Context</h3><div>Predicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability.</div></div><div><h3>Objective</h3><div>This study aims to evaluate the performance of feature- and image-based yield prediction models for yields with multiple varieties to compare their capabilities and determine an appropriate timing for early yield prediction.</div></div><div><h3>Methods</h3><div>This study combines unmanned aerial vehicle (UAV)-based multispectral remote sensing imagery with machine learning and deep learning-based algorithms to develop rice yield prediction models across multiple varieties. The performances of both feature- and image-based models are evaluated. The feature-based models considered in this study include random forest (RF), deep neural network (DNN), and long short-term memory (LSTM) algorithms, and the image-based models are convolutional neural network (CNN) architectures, including both two-dimensional (2D) and three-dimensional (3D) CNN models. To assess the performance of the multi-variety crop yield prediction models thoroughly, this study considers two sampling scenarios: stratified sampling and group sampling.</div></div><div><h3>Results and conclusions</h3><div>The results show that the image-based deep learning models outperform the feature-based machine learning models, which indicates their superior robustness in multi-variety scenarios and highlights their significant potential of directly extracting spatiotemporal features from images for yield prediction. The results indicate that the multi-temporal 2D CNN model (i.e., the CNN-M2D model) can achieve the best yield prediction performance among all models, achieving RRMSE = 8.13 % and R² = 0.73. The prediction results also demonstrate good consistency with the observed data, indicating an efficient capturing of spatial pattern variations in yield across different varieties. Based on the results, with the crops progressing along the growth stages, the accuracy of the yield prediction models improves gradually, achieving the best prediction performance during the flowering to grain-filling stage. Finally, according to the results, the optimal lead time for predicting rice yield is approximately one month before harvest.</div></div><div><h3>Significance</h3><div>Our study can provide a reference for the research community in yield prediction and high-yield variety selection in breeding trials.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104214"},"PeriodicalIF":6.1,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747356","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":"Stakeholder perspectives on ecosystem services in agricultural landscapes: A case study in the North China Plain","authors":"Jiali Cheng ,&nbsp;Andries Richter ,&nbsp;Wen-Feng Cong ,&nbsp;Zhan Xu ,&nbsp;Zhengyuan Liang ,&nbsp;Chaochun Zhang ,&nbsp;Fusuo Zhang ,&nbsp;Wopke van der Werf ,&nbsp;Jeroen C.J. Groot","doi":"10.1016/j.agsy.2024.104187","DOIUrl":"10.1016/j.agsy.2024.104187","url":null,"abstract":"<div><h3>CONTEXT</h3><div>Intensive agriculture is under pressure from changing demands from society, prompting the need to redesign agricultural landscapes to provide multiple ecosystem services (ESs). However, implementation of changed practices requires positive engagement from stakeholders. Therefore, their perspective on ecosystem services needs to be known.</div></div><div><h3>OBJECTIVE</h3><div>This study investigates stakeholders' perspectives on multiple ESs in Quzhou County, an area in the North China Plain used for intensified cereal production. We aim to elucidate perspectives within and across diverse stakeholder groups (farmers, companies, citizens, academics, village and township heads, and county government staff).</div></div><div><h3>METHODS</h3><div>Employing the Q methodology, we identified differences in perspectives within stakeholder groups and we compared the similarities and differences of those perspectives across stakeholder groups. We also investigated how farmers' personal and household characteristics were related to the perspectives they held.</div></div><div><h3>RESULTS AND CONCLUSIONS</h3><div>Significant differences in preference emerged among stakeholder groups. Academics assigned higher importance to regulating and supporting services than other stakeholder groups and companies assigned less importance to cultural services. We identified 18 distinct perspectives across seven stakeholder groups. These perspectives showed a combination of preferences for at least two different ES categories. Most of the perspectives prioritize provisioning services whereas only few perspectives prioritize supporting services.</div></div><div><h3>SIGNIFICANCE</h3><div>This study exemplifies a bottom-up approach for systematically analyzing stakeholder perspectives on the relative importance of ESs derived from agricultural landscapes. The revealed differences and complexity of stakeholder perspectives can inform decision-making on the redesign of agricultural landscapes with stakeholder engagement. Recognizing areas of consensus and conflict can guide efforts to promote agroecologically sound practices and policies.</div></div>","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104187"},"PeriodicalIF":6.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747354","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":"Equationing Arabica coffee: Adaptation, calibration, and application of an agrometeorological model for yield estimation","authors":"Cleverson Henrique de Freitas ,&nbsp;Rubens Duarte Coelho ,&nbsp;Jéfferson de Oliveira Costa ,&nbsp;Paulo Cesar Sentelhas","doi":"10.1016/j.agsy.2024.104181","DOIUrl":"10.1016/j.agsy.2024.104181","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;CONTEXT&lt;/h3&gt;&lt;div&gt;Coffee cultivation is important to Brazil's economy, positioning the country as a global leader in production and export. Given the complex environmental and management factors affecting yields, particularly due to climate change, there is a pressing need from farmers and dealers for more precise crop estimation models.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;OBJECTIVE&lt;/h3&gt;&lt;div&gt;This study aimed to refine and calibrate an agrometeorological model, originally developed by Santos and Camargo (2006) and later adapted by Verhage et al. (2017a), to estimate Arabica coffee yield in the main producing regions of Minas Gerais and São Paulo. Additionally, sensitivity analysis was also performed to identify the most influential model parameters and variables.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;METHODS&lt;/h3&gt;&lt;div&gt;Yield data from 28 coffee-producing locations (2003−2020) and meteorological data alongside irrigation use were employed. Following calibration and adaptation, a sensitivity analysis was conducted to determine the model's response to variations in coffee plant parameters and environmental conditions. Local sensitivity analysis (LSA) focused on meteorological variables, while global sensitivity analysis (GSA) addressed coffee-related parameters.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;RESULTS AND CONCLUSIONS&lt;/h3&gt;&lt;div&gt;The adaptations proposed to the original model led to a significant refinement in the yield estimates, emphasizing the complex interactions between climatic variables and agricultural management practices. Key adaptations include the estimation of potential yield (Yp), the incorporation of temporal curves for root growth, leaf area index, available water capacity, and crop coefficient, as well as a water balance that accounts for irrigation and its effect on attenuating high canopy temperatures. Calibration improved the model's accuracy and precision, with the RMSE decreasing from 13.66 (819.6 kg ha&lt;sup&gt;−1&lt;/sup&gt;; 1 bag ha&lt;sup&gt;−1&lt;/sup&gt; = 60 kg ha&lt;sup&gt;−1&lt;/sup&gt;) to 8.65 (519.0 kg ha&lt;sup&gt;−1&lt;/sup&gt;) bags ha&lt;sup&gt;−1&lt;/sup&gt;, R&lt;sup&gt;2&lt;/sup&gt; improving from 0.62 to 0.65, d-index from 0.79 to 0.88, and NSE from 0.09 to 0.64. During the evaluation phase, with independent data, RMSE was 7.76 bags ha&lt;sup&gt;−1&lt;/sup&gt; (465.6 kg ha&lt;sup&gt;−1&lt;/sup&gt;), d-index 0.85, and R&lt;sup&gt;2&lt;/sup&gt; 0.55. Sensitivity analysis emphasized the importance of mean temperature and solar radiation on Yp, as well as the impact of irrigation practices and water deficit management under rainfed conditions. Additionally, factors specific to the coffee plant itself directly affect its yield.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;SIGNIFICANCE&lt;/h3&gt;&lt;div&gt;The findings underscore the importance of a multifactorial and adaptive approach to coffee cultivation, addressing the complexities and challenges posed by varying climatic conditions. This work offers valuable insights into optimizing coffee production, presenting the model as a tool for developing more resilient cultivation strategies and enhancing the sustainability of Brazilian Arabica coffee","PeriodicalId":7730,"journal":{"name":"Agricultural Systems","volume":"223 ","pages":"Article 104181"},"PeriodicalIF":6.1,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747432","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}