Agricultural Systems

Current impact factor: 2.91

Impact Factor Rankings

2016 Impact Factor Available summer 2017
2014 / 2015 Impact Factor 2.906
2011 Impact Factor 2.899

Additional details

5-year impact 3.43
Cited half-life 8.20
Immediacy index 0.53
Eigenfactor 0.01
Article influence 1.04
ISSN 1873-2267

Publications in this journal


  • No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: This research evaluates the economic and environmental impacts of land allocation to crops and extensive livestock production in the Argentine Pampas using compromise programming. Two economic indicators (gross margin, direct costs) and five environmental indicators (organic carbon input to soil, nutrient balances, agrochemicals impact and soil erosion) are considered. The tradeoff between economic and environmental objectives is assessed and the preferred land allocation schemes determined by the multicriteria model are compared to the current land use in the region of Pergamino, North of Buenos Aires, Argentina.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: Farmer decision making models often focus on the behavioral assumptions in the representation of the decision making, applying bounded rationality theory to shift away from the generally criticized profit maximizer approach. Although complex on the behavioral side, such representations are usually simplistic with respect to the available choice options in farmer decision making and practical constraints related to farming decisions. To ascertain the relevance of modeling different facets of farmer decision making, we developed an agent-based model of farmer decision making on crop choice, fertilizer and pesticide usage using an existing economic farm optimization model. We then gradually modified the model to include practical agronomic constraints and assumptions reflecting bounded rationality, and assessed the explanatory power of the added model components. The assessments were based on comparisons to the real world data and to the results of the previous model stages, and included two model versions differing with assumptions on the farmers' rationality. Thus, we assessed the sensitivity of the model to its behavioral assumptions. The results indicated that contrary to expectations, implementation of the practical constraints improved the model performance more than the modifications in the behavioral assumptions.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: There is evidence that the decreased precipitation attributed to climate change is the main cause of the low sugarcane (Saccharum spp.) yields obtained in Mexico in recent years. The main objective of the present study was to establish whether temporal patterns in climate change affect sugarcane yields and irrigation requirements. In order to address these questions, we identified trends in climate parameters related to production in different agroecoregions where sugarcane is produced under different irrigation regimes. These regions were differentiated by means of geographic information systems and multivariate statistical analysis (principal components analysis), which were applied to data supplied by the 54 registered sugar mills in Mexico. Use of parametric models (linear regression) and non-parametric models (Mann-Kendall) enabled us to establish temporal trends in soil water availability and changes in productivity. Irrigation regime and production were related to climate patterns in the 7 agroecoregions identified. The temporal models revealed different trends in water availability, which affected crop yield. Decreased soil moisture was most evident in the agroecoregions where the crops were not irrigated or were managed with supplementary irrigation, and this affected the crop yields. The findings indicate the need to develop short and mid-term irrigation management policies for agroecoregions in Mexico.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: The present study aims at forecasting hard wheat (Triticum turgidum L. var. durum) yield at seven prediction dates (planting and six 30-day intervals after planting) prior to harvest in northern Greece. It is based on (a) reported crop yields at two relatively high spatial resolution regional levels (three NUTS2 (Nomenclature of Units for Territorial Statistics) and 16 NUTS3 regions) and (b) crop agroclimatic indicators simulated with CERES-Wheat, at four planting dates, for the years 1979–2006. Principal component analysis (PCA) was applied to explore major patterns of joint variability in a number of simulated crop agroclimatic indicators at the selected prediction dates during growing season. Stepwise regression and hindcast were employed for the selection of the modes identified by PCA as predictors in multivariate linear models used for yield forecast. Yield forecasting skill varied to a large extent by the spatial scale, planting date and timing of forecast. When the simulation results were aggregated to the larger spatial level (NUTS2), the cross-validated forecasting skill was rated as moderate in Central Macedonia (CM) (R2 = 43%) and Thrace (THR) (R2 = 35.9%) and as low in West Macedonia (WM) (R2 = 21.5%). Soil water availability to plants was the most important indicator. Except for THR, these forecasts were achieved three months before harvest in CM and four in WM. Compared with the NUTS2 level, yield predictions at the higher resolution spatial level (NUTS3) worsened in 11 and 12 out of 16 NUTS3 regions in terms of R2 and RMSE, respectively. The results demonstrate the potential of this approach and the suitability of CERES-Wheat for regional crop yield forecasting in northern Greece.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: The System of Rice Intensification (SRI) is claimed to make rice more sustainable by increasing yields while reducing water demand. However, there remains a shortage of high quality data to test these assertions, and a major research gap exists concerning the wider social and economic implications of SRI techniques.Using primary data we developed a model to simultaneously analyse social, economic and environmental sustainability (greenhouse gas (GHG) emissions, ground water abstracted, energy use, costs, profit, gender, employment quality and employment quantity) to compare SRI to conventional flooded-rice production systems (control). Data was based on farmer-recall questionnaires in Andhra Pradesh, India. Analysis was per hectare and per kg of paddy.SRI offered substantial environmental and economic benefits: >60% yield gain; GHG emissions, ground-water, fossil energy down by 40%, 60%, and 74% kg-1 respectively. SRI costs reduced significantly ha-1, and returns after costs increased by over 400%ha-1.However, the socio-economic benefits accrued to the farmer at the expense of landless labourers. Employed labour demand (hha-1) reduced to 45% of control, with the greatest decline in female employment - rural India's most vulnerable sector. SRI reduced casual labour remuneration per hectare by 50%. Doubling rates of pay maintain total casual-labour remuneration, and only reduces SRI farm returns by 10%. Yet with no policy support it is unlikely that the private economic benefits of SRI will be shared to landless labourers.Internalising environmental externalities (electricity and GHG) impacted control farms more than SRI farms, including producing negative economic returns when electricity was charged at INR4.7unit-1 for control farms. Increasing the farm gate price for paddy by 10% increased control farm returns by 38%, yet even with this substantial increase control farm returns were only a third of SRI returns without a price increase.Identifying and understanding the trade-offs associated with SRI is essential for policy management - while it is not possible to eliminate all trade-offs, identifying them allows for the mitigation of losers.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: The article addresses the competition between supplier-owned-firms (Cooperatives) and investor-owner-firms (IOFs) when procuring raw commodities of different quality from agricultural producers. The cooperative pays a (partial) pooling price to all its members and retains no surplus, whereas the IOF pays farmers prices based on their quality and maximizes its profits. When there is an IOF duopsony, farmers gain no profits. In the case of a mixed duopsony, the low-quality producer delivers to the Cooperative, while medium and high quality producers sell to the IOF. This adverse selection is due to the pooling within the Cooperative. In the case of a Coop duopsony, producers randomize their outlet decisions. The mixed duopsony is an equilibrium market structure when reservation prices of consumers are sufficiently similar. Cooperatives will challenge the monopsonistic price setting of an IOF due to the farmers being residual claimants. Both the market share of cooperatives and the extent of payment differentiation inside a cooperative have a positive effect on the prices received by farmers.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: Using a ‘stocks and flows’ model of Australian cropping we show that the expansion of aggregate cropping area has effectively masked landscape degradation impacts associated with continual production activity on “ageing” land. We estimate yield loss from combined land degradation to have increased to 9%, though the aggregate impact has effectively been masked by the introduction of new land. The model tracks the vintage of land since its first introduction to the agricultural system and calculates landscape degradation for four modes (dry-land salinity, irrigation salinity, acidification, and soil structure decline) according to historical production and ameliorating activities on each vintage. The model is calibrated with over 140 years of varied historical data from the 1850s. Modelled farm-gate production volumes also incorporate technological factors, such as genetic and other yield increases. Despite the introduction of many technological advances in the cropping industry through the middle of the 20th century, production yields of Australian cereal grain remained relatively unchanged for decades. This can be explained by the rapid ageing and degradation of the cropping land due to a period of halted expansion. This perspective has important implications for future scenarios of the Australian cropping industry, which are unlikely to maintain land expansion at the long-term average of about 2% pa. Without major change, land degradation in our model results in yield loss of nearly 30% by 2060.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: The Three-North Shelter Forest Program (TNSFP), which is the largest ecological afforestation program worldwide, was launched in 1978 and will last until 2050 in the Three-North regions (accounting for 42.4% of China's territory). As a dominant component in the TNSFP, shelterbelts or windbreaks play an important role in preventing from wind damage and erosion and providing appropriate microclimate conditions for crop growth, thus improving crop yields. However, how shelterbelts influence crop yields at the regional scale has not yet been determined because there are certain difficulties in identifying the effects of shelterbelts on crop yields due to other factors such as climatic factors, crop seeds, fertilizer and management measures. In this study, a new approach is used to estimate the effects of shelterbelts on crop yields while overcoming these difficulties. The specific processes used in this study are detailed as follows. First, the climatic potential productivity, which is a combination of solar radiation, temperature and precipitation, was estimated using the multi-sensor remote data. All farmland in the region was divided into high, middle and low climatic potential productivity zones. Second, the crop (i.e., maize) yield across the Northeast China was estimated using the harvest index method and MODIS data. Third, according to the effectively protected distance, the levels of protection provided by the shelterbelts to the farmland at the regional scale were calculated by combining the stand age and the growth status of the shelterbelts using a time series of Landsat images. Finally, the levels of protection and the corresponding maize yields in pixels were extracted and averaged to identify the effects of shelterbelts on crop yields. The results of this study indicated that shelterbelts could enhance crop yields at the regional scale. The contribution rates of shelterbelts to increasing maize yields were found to be 4.68%, 4.28% and 9.45% in the high, middle and low climatic potential productivity zones, respectively. In Northeast China, the average level of protection of farmland was 18.28%, which was obviously lower than the optimal level of protection (i.e., approximately 80%); thus, many shelterbelts must be planted in the future. The findings of this study provide a sound theoretical foundation for increasing crop yields by planning shelterbelts in farmland regions similar to those in Northeast China.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: Shifting agricultural land management to one that promotes the use of biological control alongside insecticide spray, requires two key elements: 1) habitat for natural enemies provided by non-crop area within the agricultural landscape, and 2) judicious use of insecticides by farmers. However, barriers to adoption may include low levels of knowledge regarding pests and natural enemies, and the difficulties associated with coordinating among community members. The acts of practicing cautious spraying and of leaving aside land as non-crop habitat (NCH) for natural enemies incur private costs for benefits shared by the community. In this study we tie together several objectives within the analysis of a framed field experiment built around pest management decisions in a smallholder agricultural context. We present results from a within-subjects design (multiple treatments per game group) that estimates a decision model which links behavior back to characteristics of the game, the players, and their group. We examine the relative role these sets of characteristics have in explaining variations in the modeled decisions, including how farmers respond to a nudge (a flat subsidy for allowing land to sit as non-crop habitat) to promote natural enemy services on the land. We observe that group-level characteristics appear to have the greatest roles in explaining how decisions by individuals are made. That is to say, the decision of whether to adopt greater use of NCH has more to do with those around the farmer than the traits of the farmer him or herself. From a policy point of view, it is a signal that (if such game outcomes have any bearing on farm-level decisions) conventional data tools (such as household-level data products from household surveys or censuses) may be inadequate in evaluating appropriate incentives, and newer approaches that identify social networks and neighbor/community interactions may be important.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: Alternative agricultural systems, such as organic farming (OF), are promising options to sustain both agriculture productivity and environmental health. However, the adoption of OF by farmers is occurring more slowly than is advocated. A key factor limiting farmers is an inability to predict socio-economic consequences of converting to OF. To overcome this, we developed a novel method of integrated assessment of agricultural systems (IAAS) and applied it to scenarios of development of OF in the Camargue region, South of France. In collaboration with the local stakeholders, we characterized the agricultural systems at different spatial scales and defined scenario related to the future of agriculture and to OF. We then used agent-based modeling with farmers and bio-economic modeling with local stakeholders for scenario assessment. We examined the effects on the development of OF systems of key factors such as the ongoing reform in the European Common Agricultural Policy and the effects of regulations for decreased use of pesticides. The policy reform implied trends towards a diversification of crops and greater possibility for conversion to OF. Development of OF at the regional level led to improved environmental performance, but caused a decrease in profitability of the rice supply chains. In light of the observed trade-off between rice production and OF development, objectives and options towards more sustainable agricultural systems were discussed with farmers and local stakeholders. Stakeholders' assessment of the framework provided insights on the positive and specific aspects of the IAAS methodology requiring improvement. The complementarities of agent-based and bio-economic modeling provide stakeholders with a better-informed assessment of diverse scenarios, for the development of more sustainable agricultural systems.
    No preview · Article · Mar 2016 · Agricultural Systems
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    ABSTRACT: Interest in models that integrate biophysical and economic components of agri-environmental systems has increased, largely in recognition of the multiple services provided by agri-environmental systems and reflecting the complexity of ‘multi-functional’ agriculture. We discuss the challenges of bio-economic modelling projects where biophysical and social-science research is integrated. Specific interdisciplinary challenges arise from, for example, differences in language and system understanding between disciplines, limited rewards for interdisciplinary research in the current academic merit system, and the time demands of interdisciplinary projects. Drawing on the authors' collective experiences in developing and applying bio-economic models, we discuss ways to overcome these challenges. Important lessons for future integrated modelling projects are to invest enough time at the start of the project to align research expectations, recognising the central role of communication, and training research ‘integrators’ who can facilitate collaboration within interdisciplinary teams.
    No preview · Article · Mar 2016 · Agricultural Systems