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This article describes the design of a new model-based assessment framework to identify and analyse possible future trajectories of agricultural development and their environmental consequences within the states of Mato Grosso and Para in Southern Amazonia, Brazil. The objective is to provide a tool for improving the information basis for scientists and policy makers regarding the effects of global change and national environmental policies on land-use change and the resulting impacts on the loss of natural vegetation, greenhouse gas emissions, hydrological processes, and soil erosion within the region. For this purpose, the framework combines the regional land-use models, LandSHIFT and alucR, the farm-level model, MPMAS, and the MONICA crop model, with a set of environmental impact models that are operating at the regional and watershed levels. As a first application of the framework, four scenarios with the time horizon 2030 were specified and analysed. Future land-use change will strongly depend on the interplay between the production of agricultural commodities, the agricultural intensification in terms of increasing crop yields and pasture biomass productivity, and the enforcement of environmental laws and policies. On the regional level, the scenarios with the highest increase in agricultural production in combination with weak law enforcement (Trend and Illegal Intensification) generated the highest losses in natural vegetation due to the expansion of agricultural area as well as the highest greenhouse gas emissions. Also, at the watershed level, these scenarios are characterised by the highest changes in river discharge and soil erosion that might lead to a further decline in soil fertility in the long term. Moreover, the analysis of the Sustainable Development scenario indicates that a shift in agricultural production patterns from livestock to crop cultivation, together with effective law enforcement, can effectively reduce land-use change and its negative effects on the environment. With the scenario analysis, we could illustrate that our assessment framework is capable to provide a large variety of valuable information to support the development of future land-use strategies in the study region.

The calculation of robust estimates of future greenhouse gas emissions due to agriculture is essential to support the framing of the Brazilian climate change mitigation policy. Information on the future development of land use and land cover change (LULCC) under the combination of various driving factors operating at different spatial scale levels, e.g., local land use policy and global demands for agricultural commodities, is required. The spatially explicit land use model, LandSHIFT, was applied to calculate a set of high-resolution land use scenarios for Southern Amazonia. The time frame of the analysis was 2010–2030. Based on the generated maps, emission coefficients were applied to calculate annual N2O, CH4, and CO2 emissions from agricultural soils (croplands and pastures). The results indicate that future land use pattern and the resultant greenhouse gas emissions in Southern Amazonia will be strongly determined by global and regional demands for agricultural commodities, as well as by the level of intensification of agriculture and the implementation of conservation policies.

A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) input management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with inter-annual variability, and predictions for all agronomic variables were significantly different from one model to another (p < 0.001). Uncertainty averaged 15% higher for low- versus high- input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41 and 23% for yield and ET respectively as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for non-irrigated sites). Differences in predictions due to model representation of light utilization were significant (p < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

An important part of agricultural adaptation is the timing of crop sowing dates, affecting yields and the level of risk incurred during a particular season. Cold stress is especially relevant in maize, Zea mays L., so that the timing of planting in the spring is a tactical response to short-term weather, but is also subject to strategic planning with regard to longer-term climate. Both factors compare the potential implications of cold stress to the additional yield obtainable through earlier planting. New cultivars suited to growing conditions in Europe and generally increasing spring temperatures have enabled earlier planting, but it is still dependent on short-term weather during the planting period. In the context of field-level decision-making, a panel regression is used to estimate the relationship between weekly local temperature and precipitation and planting dates at specific sites throughout Germany. Next, localised weather data and planting behaviour are linked to yields at the district (Landkreis) level to show the effects of planting date on yield. Based on these relationships optimal planting dates are explored with some associated costs and benefits. Results show a trend towards earlier planting that follows observed increasing spring temperatures and the availability of more cold-tolerant cultivars but this advance is buffered by the increasing severity of minimum temperatures during a critical period. Earlier planting potentially increases yield but this is offset by additional management costs and risk. A robust and simple depiction of farmer behaviour in climatic, technological and economic context can help to understand trends in crop management and productivity that effect agricultural landscapes.

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.

Crop models are an important tool to evaluate crop management strategies and simulate yield for present and future scenarios, however, much uncertainty is present within model parameters, approaches and input variables. Therefore, it is important to quantify the uncertainties in simulated yields as a function of input details from field management decisions and their effect on simulated regional soybean yield. To investigate this, the Model for Nitrogen and Carbon in Agroecosystems (MONICA), calibrated with experimental data, was used in this study. Four sources of uncertainty relevant to field management were considered in simulating soybean yields: technological level, soil type, sowing date and cultivar maturity group. The uncertainties in yield simulation were investigated for 14 sites in Southern Brazil, comparing results to governmental statistics for the crop seasons from 1989/1990 to 2013/2014. The MONICA model was able to simulated soybean grain yield efficiently after calibration of crop phases, growth and root-development parameters. The technological level (TL) was the yield factor with the highest coefficient of variation (CV) among the fourteen sites, with an average of 31.4%, while cultivar maturity group and sowing date both had a CV of 10%, and soil 4.4%. However, uncertainties varied with climate conditions in each crop season, with sowing date and cultivar maturity group both showing higher CV than technological level in some crop seasons. In some cases, for regional yield level, the model demonstrated varied performance by location. The most accurate performance was in simulating yields in the municipality of Passo Fundo (r = 0.87), while in Bage, the lowest accuracy was achieved (r = 0.07), across all factors. However, in Bage, when the lower yields simulated by MONICA, for all source of uncertainties were considered, simulated yields were close to those observed for most of the crop seasons. Based on these results, it is important to consider these different sources of uncertainty that stem from farmer decision-making in order to simulate regional soybean yield efficiently.

Perhaps the earliest systematic study that investigated the cyclical nature of hog prices was that conducted by Gerlich in 1911 using German data from the late nineteenth and early twentieth century. Using data from the late twentieth and early twenty-first century, we model the behaviour of the German hog-feed price ratio. A dynamic unobservable time series model which incorporates both a stochastic trend and a stochastic cycle is developed and applied to these data. We find a significant four-year cycle and provide evidence that it is becoming more volatile. , Oxford University Press.

Climatic change is likely to have an influence on arable farms in Central Europe. We use a modelling approach to assess the effects of weather and its long term development due to climate change on short-term decisions like planting and harvesting, as well as yields. Two models are coupled, a farm management model FarmActor and the crop growth model system Expert-N to investigate the interplay between management and crop growth on a daily basis. We examine different methods of adapting expectations concerning the timing of cropping actions and annual yields to actual observed weather and yield data. Our study focuses on the two major crops winter wheat and silage maize in the Swabian Alb in southwestern Germany. Results show that the model can satisfactorily reproduce the development of planting and harvesting as well as yields that have occurred in the past. Different methods of expectation formation only show minor differences in their effect on action dates and yields. Future climatic change is likely to shift the timing of field actions. Assuming no change in technology (e.g. cultivars), summer crops may be seeded earlier while winter crops could tend to be sown later; harvest may occur earlier and yields might slightly decrease while showing more volatility. This modelling approach has the potential to increase the knowledge about risk profiles of short time agricultural management actions and to improve the land use modelling part of coupled earth system models.

Management is the most important handle to improve crop yield and resilience under climate change. The aim of this study was to evaluate how irrigation, sowing date, cultivar maturity group and planting density can contribute for increasing the resilience of soybean (Glycine max (L.) Merr.) under future climate in southern Brazil. Five sites were selected to represent the range of Brazilian production systems typical for soybean cultivation. Yields were obtained from a crop-model ensemble (CROPGRO, APSIM and MONICA). Three climate scenarios were evaluated: baseline (1961–2014), and two future climate scenarios for the mid-century (2041–70) with low (+2.2°C, A1BLs) and high (+3.2°C, A1BHs) deltas for air temperature and with atmospheric [CO2] of 600 ppm. Supplementary irrigation resulted in higher and more stable yields, with gains in relation to a rainfed crop of 543, 719, 758 kg ha–1, respectively, for baseline, A1BLs and A1BHs. For sowing date, the tendencies were similar between climate scenarios, with higher yields when soybean was sown on 15 October for each simulated growing season. Cultivar maturity group 7.8 and a plant density of 50 plants m−2 resulted in higher yields in all climate scenarios. The best crop-management strategies showed similar tendency for all climate scenarios in Southern Brazil.

SUMMARYAgriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique region's predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades.

Crop simulation is a modern tool used to mimic ordinary and extraordinary agriculture systems. Under the premise of continuing foreseeable climatic shift we combine adaptive field-level management decisions with their effects on crop performance. Price projections are used to examine yield and price effects on gross margins of the predominant crops in two specific regions of Southwest Germany into the coming decades. After calibration and validation to historic records, simulated future weather is used to explore how farmer behavior and performance of wheat, barley, rapeseed and maize could develop under anticipated global change. This development is examined based on a comparison of historic and projected gross margin variance. Simulations indicate that when yield levels increase, the relative variability of gross margins may decline in spite of some increasing variability of yields. The coefficient of variance of gross margins decreases even more due to the independence of price and yield fluctuations. This shows how the effects of global change on yields could be offset by economic conditions.

The stoneroller (Campostoma anomalum) is one of the more important fish in Great Smoky Mountains National Park because of its abundance and habits. Although esteemed locally as a food and a bait fish, the stoneroller is exploited but little since the fishing regulations which govern the utilization of game fishes afford it a large measure of protection. Distribution is controlled by gradient with an upper limit of 4.4 percent. Stonerollers limit reproduction of rainbow trout (Salmo gairdneri) by destroying trout redds. Artificial reduction of stoneroller populations is not considered a necessary management procedure.