Farm-level Model Research Articles

Economic Sustainability Foraging Scenarios for Ruminant Meat Production—A Climate Change Adapting Alternative

Climate changes affect all agricultural production systems, directly or indirectly, including that of ruminant meat, through the limitation of forage resources sensitive to reduced water regimes and drought. The present paper assessed the economic sustainability of ruminant meat production in the context of climate change, with a particular focus on integrating bioeconomy principles through the use of drought-resistant crops such as sorghum and millet in livestock feed. This study included scenarios for two farm-level models, a sheep fattening farm and a cattle fattening farm, to determine the economic benefit and impact of integrating resilient crops in the total feed ration. The findings showed that the dry scenario system could offer economic and environmental advantages over traditional water-intensive crops like maize. The results demonstrated that replacing maize with sorghum or millet could result in a reduction in feed costs and enhanced economic benefit over the traditional feed system.

Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization

Improving a dynamic farm level model by coupling empirically estimated pre-crop values with crop rotation optimization

Bioenergy feedstock supply from wheat straw: A farm level model incorporating trade‐offs in crop choices, disease risk, and soil fertility

AbstractSecond‐generation biofuel (e.g., ethanol, renewable diesel) can be made from crop residues. However, the availability of residues for biofuel production is uncertain, because farmers have the option to grow different crops and use the residues for alternative purposes, such as livestock bedding and feed, or leave them in the field to improve soil quality. Taking Canadian wheat straw supply as an example, we develop a dynamic programming model to investigate a farmer's wheat straw supply decision in response to different wheat straw and grain prices. Our model considers crop choices between wheat and canola in the context of disease risk, the trade‐off between the immediate payoffs a farmer may receive from bailing and selling wheat straw, and the long‐term adverse effects that removing wheat straw from the soil surface may have on wheat and canola yields. The results from this study provide insights into how farm‐level supply decisions, in response to wheat straw price changes, affect soil quality dynamics and scale up to regional wheat straw supply for biofuel production. This information also has implications for land use change and the sustainability of feedstock supply for biofuels.

Modeling conversion to organic agriculture with an EU-wide farm model

This paper analyses the impacts of the Farm to Fork strategy (F2F) target of 25% organic farmland by 2030 in the EU using a farm level model. Two approaches are deployed to model conversion to organic agriculture. The first one, the endogenous approach, operates under the assumption that farm conversions to organic production result from assessing the utility difference between organic and conventional production systems. The exogenous approach relies on econometric estimation of the likelihood of farms to convert to organic driven by a combination of monetary and non-monetary drivers. The simulated impacts of the F2F target at the EU level vary depending on the chosen methodology. Gross income changes range from +3.8% under the endogenous approach to -1.3% under the exogenous approach. Both approaches forecast decreased production (-0.5% to -15%) for most crops and animal products upon achieving the organic target.

Cost-effectiveness of farm- vs. regional-level climate change mitigation policies

Abstract Agriculture is a significant source of global greenhouse gas (GHG) emissions, making reduction targets crucially needed. Worldwide, countries have set agricultural GHG reduction goals and discussed methods to reach them. A crucial aspect is, whether the policy target level is set at the individual farm or at a regional level. In this context, we assess the advantages regarding cost-effectiveness and GHG-reduction potential of targets at the regional level. First, we use the bioeconomic farm-level model FarmDyn to simulate the changes in income and GHG emissions of 65 Swiss dairy farms. Secondly, we develop an optimization algorithm to compare the efficiency and efficacy of these two target approaches. Our analysis reveals that regional targets, which consider the heterogenous abatement costs of the sector, are more cost-efficient than farm-level ones. Specifically, they enable a 10 per cent GHG reduction at 88 per cent lower costs, suggesting they might be a more cost-effective alternative to taxation.

Integrating Remote Sensing and Weather Variables for Mango Yield Prediction Using a Machine Learning Approach

Accurate pre-harvest yield forecasting of mango is essential to the industry as it supports better decision making around harvesting logistics and forward selling, thus optimizing productivity and reducing food waste. Current methods for yield forecasting such as manually counting 2–3% of the orchard can be accurate but are very time inefficient and labour intensive. More recent evaluations of technological solutions such as remote (satellite) and proximal (on ground) sensing have provided very encouraging results, but they still require infield in-season sampling for calibration, the technology comes at a significant cost, and commercial availability is limited, especially for vehicle-mounted sensors. This study presents the first evaluation of a ”time series”—based remote sensing method for yield forecasting of mango, a method that does not require infield fruit counts and utilizes freely available satellite imagery. Historic yield data from 2015 to 2022 were sourced from 51 individual orchard blocks from two farms (AH and MK) in the Northern Territory of Australia. Time series measures of the canopy reflectance properties of the blocks were obtained from Landsat 7 and 8 satellite data for the 2015–2022 growing seasons. From the imagery, the following vegetation indices (VIs) were derived: EVI, GNDVI, NDVI, and LSWI, whilst corresponding weather variables (rainfall (Prec), temperature (Tmin/Tmax), evapotranspiration (ETo), solar radiation (Rad), and vapor pressure deficit (vpd)) were also sourced from SILO data. To determine the relationships among weather and remotely sensed measures of canopy throughout the growing season and the yield achieved (at the block level and the farm level), six machine learning (ML) algorithms, namely random forest (RF), support vector regression (SVR), eXtreme gradient boosting (XGBOOST), RIDGE, LASSO and partial least square regression (PLSR), were trialed. The EVI/GNDVI and Prec/Tmin were found to be the best RS and weather predictors, respectively. The block-level combined RS/weather-based RF model for 2021 produced the best result (MAE = 2.9 t/ha), marginally better than the RS only RF model (MAE = 3.4 t/ha). The farm-level model error (FLEM) was generally lower than the block-level model error, for both the combined RS/weather-based RF model (farm = 3.7%, block (NMAE) = 33.6% for 2021) and the RS-based model (farm = 4.3%, block = 38.4% for 2021). Further testing of the RS/weather-based RF models over six additional orchards (other than AH and MK) produced errors ranging between 24% and 39% from 2016 to 2020. Although accuracies of prediction did vary at both the block level and the farm level, this preliminary study demonstrates the potential of a ”time series” RS method for predicting mango yields. The benefits to the mango industry are that it utilizes freely available imagery, requires no infield calibration, and provides predictions several months before the commercial harvest. Therefore, this outcome not only presents a more adoptable option for the industry, but also better supports automation and scalability in terms of block-, farm-, regional, and national level forecasting.

Model Collaboration between Farm Level Models with Application on Dutch Dairy and Arable Farms Regarding Circular Agricultural Policy

The ambition of the Dutch Ministry of Agriculture is to stimulate the transition to circular agriculture. The objective of this paper is to develop and apply a farm level model toolbox for circular-agriculture policy assessment. Transition to circular agriculture affects farm management practices and outcome in the field of finance and economics, soil quality, use of finite resources, emissions, and biodiversity. Based on this, there is a need for an integrated assessment at farm level. Therefore, Bio Economic Farm Models should be at the core of the model toolbox. Model collaboration enables answering more complex questions and enlarges the scope of the analysis. Challenges of model collaboration are among others overlapping modules, different approaches (optimisation versus simulation), and existence of different networks of model developers and users. It is argued that a governance structure and networking will foster model collaboration. To stimulate transition to more circular agriculture practices and as a demonstration, the model toolbox was applied to assess the economic and environmental impacts of a tax on N from mineral fertiliser on a representative dairy and arable farm in a region in the Netherlands. It was found that a tax on N from mineral fertiliser has relatively large income effects, while the impacts on various environmental indicators are relatively limited.

Farm-level modelling of bioeconomic, greenhouse gas emissions and feed-food performance of pasture-based dairy-beef systems

CONTEXT.Global demand for grass-based beef systems is increasing with a growing proportion of beef output in many countries originating from the dairy herd. Concurrently, concerns around the environmental impact of food, particularly that derived from ruminants, and food security is growing in prominence. Therefore, a comprehensive analysis of the sustainability of dairy-beef systems is required. OBJECTIVESThe objectives of this study were to (1) augment an existing farm-level bioeconomic model to permit greenhouse (GHG) emissions and feed-food analysis of dairy-beef systems and (2) use this model to asses the performance of grass-based dairy-beef steer production systems. METHODSThe developed farm-level model was used to evaluate production systems finishing males as steers on three contrasting soil types and differing with respect to sire breed (early-maturing, late-maturing and dairy), age at slaughter (20, 24 and 28 months of age) and finishing diet (grazed grass or grass silage, each supplemented with concentrates). RESULTS AND CONCLUSIONSResults highlighted a wide range in farm net margins (from €134 to €515 per hectare) with systems finishing off pasture in the second grazing season being most profitable. These systems also had lowest GHG emissions intensities. Steer systems finishing off pasture during the third grazing season were the only net producers of human-edible protein, whilst all systems were net consumers of human-edible energy. Production systems on higher quality land were more profitable, had lower GHG emissions but used more tillable land than systems on poorer quality land. SIGNIFICANCEUnder the conditions modelled in this study, inverse relationships were found between system profitability, human-edible protein and energy efficiency and greenhouse gas emissions implying trade-offs may need to considered in respect of sustainability metrics.

Possible Impact of Risk Management Strategies with Farm Model on a Mixed Farm Type

Abstract Background: Farm-level models have become an important tool for agricultural economists as there is a growing demand for microsimulation and analysis of farms at the individual level. Objectives: In this paper, we present a mathematical model with the main objective of assessing the effectiveness of production and various possible strategies for agricultural holdings by reducing risks. At the same time, we were also interested in the environmental impacts of such strategies. The latter was measured using the indicator of GHG emissions. Methods/Approach: The model applied is based on linear programming and upgraded with QRP for risk analysis. The approach was tested on medium size mixed agricultural holding, which often faces challenges in light of the structural changes taking place in Slovenia. Results: The results suggest that such a farm could improve financial results with a more efficient risk management strategy. With a slightly modified production plan, the expected gross margin (EGM) can be increased by up to 10% at more or less the same risk. However, if the farmer is willing to diversify the production plan and take a higher risk (+23%), the farm’s EGM could increase by up to 18%. This kind of change in the production plan would also generate 17% more GHG emissions in total, calculated as kg equivalent of CO2 at the farm level, as both BL and C scenarios have the same relative ratio at 3.12 GHG CO2 eq. /EUR. Conclusions: Through this research, we concluded that diversification has a positive potential on a mixed farm, and the farm could achieve better financial results. With flexibility in management, the farmer could also achieve higher risk management efficiency and better farm results.

Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output

Surrogate modeling can overcome computational and data-privacy constraints of micro-scale economic models and support their incorporation into large-scale simulations and interactive simulation experiments. We compare four data-driven methods to reproduce the aggregated crop area response simulated by farm-level modeling in response to price variation. We use the isometric log-ratio transformation to accommodate the compositional nature of the output and sequential sampling with stability analysis for efficient model selection. Extreme gradient boosting outperforms multivariate adaptive regressions splines, random forest regression, and classical multinomial-logistic regression and achieves high goodness-of-fit from moderately sized samples. Explicitly including ratio terms between price input variables considerably improved prediction, even for highly automatic machine learning methods that should in principle be able to detect such input variable interaction automatically. The presented methodology provides a solid basis for the use of surrogate modeling to support the incorporation of micro-scale models into large-scale integrated simulations and interactive simulation experiments with stakeholders.

Turbine-level clustering for improved short-term wind power forecasting

At the present time, new types of data are collected at a turbine level, and can be used to enhance the skill of short-term wind power forecasts. In particular, high resolution measurements such as wind power and wind speed are gathered using SCADA systems. These data can be used to build turbine-tailored forecasting models, but at a higher computational cost to predict the production of the overall wind farm compared to a single farm-level model. Thus, we explore the potential of the DBSCAN clustering algorithm to group wind turbines and build forecasting models at a cluster-level to find a middle ground between forecasting accuracy and computational cost. The proposed approach is evaluated using SCADA data collected in two Irish wind farms.

Farm-Level Modelling of Bioeconomic, Greenhouse Gas Emissions and Feed-Food Performance of Pasture-Based Dairy-Beef Systems

Farm-Level Modelling of Bioeconomic, Greenhouse Gas Emissions and Feed-Food Performance of Pasture-Based Dairy-Beef Systems

Farm-Level Effects of Emissions Tax and Adjustable Drainage on Peatlands

Drained agricultural peatlands emit significantly higher amounts of greenhouse gas (GHG) emissions per hectare than mineral soils. GHG abatement costs for representative cereals (CF) and dairy (DF) farms in southwestern Finland were estimated by integrating an emission-based tax together with an option to invest in a subsidized adjustable drainage system on peat soils in a farm-level dynamic optimization model. With an average 10% share of peat soils from overall farm area, emissions tax rates over 15 (CF) and 19 (DF) €/tCO2e triggered adjustable drainage investments with a significant reduction in GHG emissions per ha, when assuming no crop-yield effect from the adjustable drainage. Abatement costs for emissions tax rates €12–50/tCO2e/ha were €16–44/tCO2e (CF) and €26–51/tCO2e (DF) for whole farm-soil emissions, depending on the share of peatlands on the farm, on the yield effects of adjustable drainage, and on crop prices. High emissions tax rates imply higher abatement costs since farms have a limited capability to adjust their production and land use. Thus, emissions reductions from peatlands can be achieved at reasonable costs when investing in adjustable drainage on peatlands. The income losses due to emissions tax, however, are high, but they can be compensated for farmers by lumpsum payments independent of their production decisions. Since existing agricultural policies such as the EU CAP system may have limited effectiveness on GHG emissions, the emissions tax and adjustable drainage on peatlands could promote GHG abatement significantly on farms and areas with abundant peatlands.

Are agricultural support policies harmful to the environment? Evidence from Japanese farm-level policy simulation

AbstractThis study presents the environmental impacts of agricultural policy instruments as evidence from anex-antefarm-level policy simulation model in Japan. Simulations did indicate that all types of agri-environmental payments achieved the environmental benefit for the land studied. Conversely, market price support does not inevitably increase nitrogen runoff or greenhouse gas emissions at any time since paddy fields themselves have the function of purifying water pollution and work as a biodiversity nursery. The direction and magnitude of the policy impacts are an empirical matter that should be considered carefully at a local level.

Risk, Risk Aversion, and Agricultural Technology Adoption ─ A Novel Valuation Method Based on Real Options and Inverse Stochastic Dominance

AbstractRisk and risk preferences belong to the key determinants of investment-based technology adoption in agriculture. We develop and apply a novel approach in which an inverse second order stochastic dominance approach is integrated into a stochastic dynamic farm-level model to quantify the effect of both risk and risk aversion on the timing and scale of agricultural technology adoption. Our illustrative example on short rotation coppice adoption shows that risk aversion leads to technology adoption that takes place earlier, but to a smaller extent. In contrast, higher levels of risk exposure lead to postponed but overall larger adoption. These effects would be obscured if technology adoption is not analyzed in a farm-scale context or considered as a now-or-never decision, the still dominant approach in the literature.

Creating a synthetic landscape: Spatial allocation of non-spatial single farm data

Spatially explicit farm data are often not available for farm level studies and models, limiting the accuracy of simulating environmental impacts and posing limitations to agent-based models that simulate spatial decisions under explicit consideration of neighborhood effects (e.g. as on a land market). For large regions, such as the whole EU, different approaches have been developed to address this problem, e.g. by downscaling economic model results to lower-level administrative regions or by spatially allocating farm types to homogenous spatial entities. For smaller regions and if interactions between farms have to be considered, these approaches are too coarse.Some studies make use of a synthetic landscape approach for this scale and type of analysis. Farms are placed on a grid of the area under study by ensuring that the localization of the farms fulfills the area claims of the farms. Available approaches use relatively simple techniques by considering only the grassland share in the total area of farms or farm types and use a relatively coarse resolution (1ha*1ha). In this article, we present a method for improving the synthetic landscape approach, by considering landscape parameters in the allocation of farms, using a finer resolution (25m*25m) and introducing allocation quality indicators that allow for an assessment of the overall allocation result. For the German case study region Ostprignitz-Ruppin (NUT3 administrative level), the approach delivered a relatively high overall allocation quality. We conclude that the farm maps generated by our method can be used by mathematical optimization models or agent-based models to improve their capability of simulating spatial decisions and effects. The transferability of the method to other regions is possible, subject to data availability and the type of analysis targeted.

Understanding uncertainty in the carbon footprint of beef production

Greenhouse gas (GHG) accounting models facilitate mitigation of emissions from livestock systems. Such models are approximations, and uncertainties in their output may stem from a) uncertainty or variability in input data, b) uncertainty resulting from model scope and allocation methods, or c) uncertainty in modelling approach used. While sources a) and b) vary depending on the modelled scenario, c), referred to as epistemic uncertainty, relates to the modelling process, and as such is inherent in the methodology used rather than the specific scenario. This study combines a farm-level model comprised of widely used GHG accounting methodologies with a typical northern hemisphere suckler beef production system, and employs Monte Carlo simulation to assess the sensitivity of the modelled GHG footprint to epistemic uncertainty in the model. Following a cradle-to-gate approach, an emissions intensity of 19.20 ± 2.49 kg CO2-eq kg live weight−1 was estimated for the modelled system. The study also highlights a discrepancy of 8.3% between deterministically and stochastically calculated emissions; this results from skewness in key modelling coefficients, primarily those relating to nitrous oxide emissions. Sensitivity analysis showed coefficients relating to emissions of nitrous oxide from land and methane from enteric fermentation were most influential in the modelled uncertainty, though coefficients relating to livestock feed production also contributed substantially. In conducting a root-cause analysis of uncertainty in GHG accounting from beef production, this study makes a novel contribution to the literature surrounding uncertainty in livestock emissions modelling. Developers of GHG accounting methodologies may use these insights to focus efforts on refining the most influential elements of these approaches, while researchers applying the models should be aware of the associated uncertainty. The latter should be quantified and effectively communicated where these models are used to support policy decisions.

Reducing greenhouse gas emissions through the use of urease inhibitors: A farm level analysis

Ireland’s ambitious plan to expand primary agricultural production and its commitment to reduce its non-ETS GHG emissions by 20% compared with 2005 emission levels constitute a considerable challenge for Irish farmers. Nitrous oxide emissions produced as a result of the application of artificial fertiliser accounts for 16% of Ireland’s agricultural GHG emissions (Teagasc, 2017a). The use of urea combined with NBPT has the potential to reduce GHG emissions from agriculture when compared with conventional fertilisers. This paper presents a farm level model which maximizes farm gross margin subject to constraints on production factors (labour, land etc.) as well as agronomic constraints (stocking rate, fertilisers, feed, etc.). The aim of this paper is to compare the farm gross margin of two dairy farm types under a base scenario with the results of five other scenarios which consider varying levels of emissions reduction targets and the potential of urea combined with NBPT as an abatement technology. Results inferred that there is a potential for urea combined with NBPT to offset nitrous oxide emissions from fertiliser application and hence contribute to Ireland’s GHG reduction target.

Brexit: How Will UK Agriculture Fare?

SummaryThere is little doubt that Brexit would have significant implications for UK agriculture, a sector with strong trade links to the EU and strong reliance on CAP income support. This article reports preliminary results from employing a Computable General Equilibrium Model, a Partial Equilibrium Model and Farm Level Models to explore selected trade and domestic policy scenarios post‐Brexit. These allow for the estimation of changes in producer prices, production and farm incomes against a baseline scenario of continued EU membership. Under a Free Trade Agreement with the EU, agricultural impacts are relatively modest. By contrast, unilateral removal of import tariffs has significant negative impacts on prices, production and incomes. Adoption of the EU's WTO tariff schedule for all imports favours net importers (e.g. dairy) and harms net exporters (e.g. sheep). Given the strong dependence of most UK farms on direct payments, their removal worsens negative impacts of new trade arrangements and offsets positive impacts. Impacts vary across different types and sizes of farm, but also regionally. However, the period of adjustment to new trade and domestic policy conditions may prove very challenging for a large number of farm businesses.

Brucellosis Risk in Urban and Agro-pastoral Areas in Tanzania.

Epidemiology of human and animal brucellosis may depend on ecological conditions. A cross-sectional study was conducted to compare prevalence and risk factors of bovine brucellosis, and risky behaviours for the human infection between urban and agro-pastoral areas in Morogoro region, Tanzania. Cattle blood sampling and interviews using a structured questionnaire were conducted with farmers. Rose-Bengal test was conducted for the cattle sera, and positive samples were confirmed with competitive ELISA. Farm-level sero-prevalences were 0.9% (1/106, 95% CI 0.0-5.9%) and 52.9% (9/17, 95% CI 28.5-76.1%) in urban and agro-pastoral areas, respectively. The animal-level-adjusted prevalences were 0.2% (1/667, 95% CI 0.0-1.1%) and 7.0% (28/673, 95% CI 5.7-8.4%) in those areas. The final farm-level model including both areas found two risk factors: history of abortion in the herd (P<0.01) and cattle grazing (P=0.07). The animal-level risk factors in agro-pastoral areas were age (P=0.04) and history of abortion (P=0.03). No agro-pastoral farmer knew about Brucella vaccine. Agro-pastoralists generally had poorer knowledge on brucellosis and practiced significantly more risky behaviours for human brucellosis such as drinking raw milk (17.6%, P<0.01) and blood (35.3%, P<0.01), and helping cattle birth (100%, P=0.04) than urban farmers (0, 0 and 79.2%, respectively). Intervention programs through education including both human and animal health particularly targeting agro-pastoralists would be needed.