Bioeconomic Simulation Model Research Articles

Bio-economic analysis of irrigation schedules considering shallow groundwater: lessons from South Africa

Due to the pressure on South Africa’s irrigated agriculture to improve efficiency and optimal water use, irrigators must consider alternative water sources, such as root-accessible shallow groundwater tables, to supply the crop evapotranspiration requirement. Devising irrigation scheduling strategies that will optimize conjunctive water use is difficult because the contribution of shallow groundwater tables is not directly observed and is a function of irrigation management decisions; as a result, very few irrigators use these strategies. This paper aims to evaluate the profitability of using shallow groundwater tables as a source of irrigation water to satisfy crop evapotranspiration requirements. A bio-economic simulation model consisting of the soil–water–atmosphere–plant model and an economic accounting module was developed to calculate the profitability of conjunctive irrigation practices under different states of nature. The bio-economic simulation model was linked to a differential evolutionary algorithm to optimize the irrigation scheduling decisions. The results showed that irrigators could substantially increase profitability and water use efficiency if they consider the shallow groundwater table in their irrigation decision. About 51% of crop evapotranspiration could originate from shallow groundwater tables, reducing the irrigation requirements substantially without impacting crop yields. Sequential adaptive irrigation decision-making does not improve the bio-economic indicators much since using the shallow groundwater table mitigates the risk of undersupplying water. Therefore, conjunctive water use strategies using shallow groundwater tables economically benefit irrigators. However, a complex interplay exists between irrigation adjustments, crop yields and economic performance in different states, emphasizing the careful consideration of context-specific factors in irrigation management decisions.

The economic effect of cow-based reproductive management programs with a systematic use of reproductive hormones

Hormone-based reproductive management programs can be beneficial to improve dairy cow's reproductive performance. This study aimed to compare the economic impact of reproductive management programs using systematic hormonal treatments to individual cows with a specific DIM range with a reproductive management program using cow-specific hormonal treatment based on a veterinary diagnosis of ovarian dysfunction during a fertility check. An existing individual cow-based, dynamic, and stochastic bio-economic simulation model, mimicking the production dynamics of a 200 cow-herd in daily time steps, was extended with ovarian dysfunction and fertility inputs. Four hormone-based reproductive management programs were modeled. In the default reproductive management program, reflecting the current reproductive management of Dutch herds, lactating dairy cows are inseminated based on detection of estrus and non-cyclic dairy cows are treated with hormones based on a veterinary diagnosis of ovarian dysfunction during a fertility check. Hormone treatments prescribed by the veterinarian for anestrus, cystic, and sub-estrus cows were an 8-d progesterone-releasing intravaginal device (PRID)-Synch protocol (PRIDsynch), an Ovsynch protocol, and a PGF2⍺ treatment, respectively. The 3 other reproductive management programs reflected systematic hormonal treatments to cows at specific DIM and included a 1) Double-Ovsynch protocol for TAI with nonpregnant cows submitted to a resynchronization protocol (FTAI), 2) Double-Ovsynch protocol for TAI with nonpregnant cows detected in estrus or submitted to a resynchronization protocol (FTAI+ED), and 3) detection of estrus with cows not detected submitted to a PRIDsynch protocol (ED+TAI). All nonpregnant cows were submitted to a resynchronization protocol based on the absence (PRIDsynch) or presence (Ovsynch protocol) of a corpus luteum (CL). The annual mean net economic return (NER) was calculated for all reproductive management programs. Compared with the default reproductive management program, the highest NER was observed for the FTAI+ED reproductive management program with €23,764 higher net revenues, followed by the FTAI and the ED+TAI reproductive management programs with €19,550 and €14,314 higher net revenues, respectively. Overall, systematic hormone-based reproductive management programs gave higher costs due to more hormones administered and higher calving and feed costs due to more pregnant cows. Nevertheless, the additional revenues of milk and calves in the systematic hormone-based reproductive management programs outweighed the total cost. For instance, the FTAI+ED reproductive management program gave €8,953 higher total cost per year compared with the default but with €32,654 higher revenues. In summary, reproductive management programs where hormones were systematically used gave economic advantages over the current default reproductive management program in which hormones are administered to individual cows based on a veterinary diagnosis of ovarian dysfunction during a fertility check.

Economic impact of highly pathogenic avian influenza outbreaks in Western Java smallholder broiler farms

Highly Pathogenic Avian Influenza (HPAI) H5N1 is considered endemic in most parts of Indonesia and constitutes an important risk for broiler production, especially in Western Java which has the highest poultry population in the country. Most broiler farms in Western Java are smallholder farms that operate under different business types: independent (i.e., revenues based on market price and live bird weight), price-contract (i.e., revenues based on a contract selling price and live bird weight) or makloon (i.e., revenues based on a management fee per sold bird). Many studies focus on the epidemiological impacts of HPAI at the regional level, and insights into the economic impact at the farm level are scarce, especially in the Indonesian context. Meanwhile, a single HPAI outbreak could disrupt smallholder broiler farmers’ primary source of income. Therefore, this study aimed to evaluate the economic impact of HPAI outbreaks under different response scenarios (i.e., no action, stamping out, and early selling) on typical Western Java smallholder broiler farms. Furthermore, the effect of different farm business types and the existence of a sick-bird market on the economic effects of HPAI outbreaks were evaluated. We developed a dynamic stochastic bio-economic simulation model to simulate epidemiological and economic impacts of HPAI outbreaks on a typical Western Java smallholder broiler farm during one production round. Our results show that the economic consequences of HPAI outbreaks for independent and price-contract farms are considerable, ranging from, on average, 1.2–62.7 million Indonesian Rupiah (IDR) losses (€76.9 to €3919), depending on the moment of and response to infection, compared to an expected gross margin of 5.3 million IDR (€331) under normal circumstances. The economic loss for makloon farms was substantially lower than for other business types, reducing their incentive to implement biosecurity. The economic impacts were sensitive to changes in a diverse set of parameters, including disease transmission rate, detection threshold, and stamping-out compensation. The losses in a scenario with stamping out were higher than in other scenarios, especially when stamping out happened near the end of the production round. Moreover, reacting to an outbreak by selling chickens early gave the lowest economic losses, incentivizing farmers to engage in behavior with a high disease transmission risk. Therefore, the results of this study suggest that it is important to consider the economic perspective of individual farmers when designing HPAI mitigation programs. Financial incentives for farmers to control HPAI differ largely between farm business types.

The economics of sensor-based management of dairy cow suboptimal mobility.

Suboptimal mobility (SOM) is a costly health condition in dairy production. Current SOM management is based on visual SOM detection by farm staff. This often leads to cows with severe SOM being detected and promptly treated, whereas the detection and subsequent treatment of cows with mild SOM is delayed or nonexistent resulting in prolonged cases of mild SOM being treated only at half-year routine hoof trimming. Using automatic SOM detection sensors may improve early detection of mild SOM allowing for improved SOM management. However, the economic value of these sensors used for sensor-based SOM management are not well known. The objective of this study was to evaluate the added economic value of automatic SOM detection sensors. A recently developed bioeconomic simulation model was extended to simulate a farm without and with automatic SOM detection sensors and farm economic performance comparisons were drawn. Moreover, for the farm with sensors, novel sensor-based SOM management strategies were designed. Within these sensor based-management strategies multiple scenarios with different sensor performance in terms of sensitivity, specificity, and mobility score detection were simulated. A new alert prioritization method was also introduced. Results from this study provide insights on the economic tradeoffs in production losses and additional labor costs for the different sensor-based management strategies, sensor performances, and alert prioritization. Simulations show that the added economic value of automatic SOM detection sensors are sensitive to the sensor-based management strategies, sensor performance, and the introduced alert prioritization method. Thirty-nine of the 80 simulated scenarios obtained a positive mean net economic sensor effect: the highest was €6,360 per year (€51/cow per yr). Based on evidence from our scenarios we suggest that twice-yearly routine hoof trimming with the addition of automatic SOM detection sensors should be replaced with cow specific hoof trimmer treatments following SOM detection by the sensor. Earlier detection and subsequent treatment of mild SOM resulted in economic gains when the alert prioritization method was introduced. Implementing automatic SOM detection sensor systems allows for many options to alter SOM management where improvements in farm economic performance can be achieved in combination with improved cow mobility. The implications for future research are discussed.

Quantifying the consequences of disturbances on wood revenues with Impulse Response Functions

Forest disturbances in Europe are very likely to increase in frequency and intensity. Assessing their economic consequences is required to identify feasible adaptation strategies. Such economic calculations depend on estimates for the reduction in revenues after disturbance events. These losses can be caused by both a lower wood quality as well as an oversupply on the wood markets. Despite its importance, data-driven approaches to quantify the consequences of disturbances on wood revenues in Central Europe are rare. We applied econometric time series analysis with Structural Vector Autoregressive (SVAR) models to harvest and sales data from Hesse, Germany. Additionally, we derived estimates for reductions in wood revenues for integration in bioeconomic simulation models. Our analyses indicate that the observed losses in wood revenues for spruce after disturbances are mainly due to an oversupply on the wood markets, rather than a loss in wood quality. In addition, the results suggest that calamities of transregional extent or multiple disturbances in subsequent years are likely to reduce wood revenues beyond the assumptions often used in bioeconomic simulation models. Although our results for beech were more ambiguous, they indicate that losses in revenues for beech after disturbances in the past were mainly due to a reduced wood quality. Our study highlights the importance of taking a differentiated view on the consequences of disturbances on wood revenues, considering their spatial extent and species-specific mechanisms.

Simulating the mechanics behind sub-optimal mobility and the associated economic losses in dairy production

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2–5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2–5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied considerably between the 5th and 95th percentiles: €−6174 and €19,499, and had a mean of €6281. This loss was composed of additional indirect culling due to SOM (∼65%) and changes in the overall herd milk production (∼35%) because of additional younger replacement heifers entering the herd due to increased culling rates. The bio-economic model presented novel results with respect to indirect economic losses arising due to SOM. The results can be used to stimulate farmer awareness and promote better SOM management.

Assessing the population-level conservation effects of marine protected areas.

Marine protected areas (MPAs) cover 3–7% of the world's ocean, and international organizations call for 30% coverage by 2030. Although numerous studies show that MPAs produce conservation benefits inside their borders, many MPAs are also justified on the grounds that they confer conservation benefits to the connected populations that span beyond their borders. A network of MPAs covering roughly 20% of the Channel Islands National Marine Sanctuary was established in 2003, with a goal of providing regional conservation and fishery benefits. We used a spatially explicit bioeconomic simulation model and a Bayesian difference‐in‐difference regression to examine the conditions under which MPAs can provide population‐level conservation benefits inside and outside their borders and to assess evidence of those benefits in the Channel Islands. As of 2017, we estimated that biomass densities of targeted fin‐fish had a median value 81% higher (90% credible interval: 23–148) inside the Channel Island MPAs than outside. However, we found no clear effect of these MPAs on mean total biomass densities at the population level: estimated median effect was –7% (90% credible interval: –31 to 23) from 2015 to 2017. Our simulation model showed that effect sizes of MPAs of <30% were likely to be difficult to detect (even when they were present); smaller effect sizes (which are likely to be common) were even harder to detect. Clearly, communicating expectations and uncertainties around MPAs is critical to ensuring that MPAs are effective. We provide a novel assessment of the population‐level effects of a large MPA network across many different species of targeted fin‐fish, and our results offer guidance for communities charged with monitoring and adapting MPAs.

Steepness is a slippery slope

AbstractThe Beverton–Holt and Ricker stock‐recruit functions were derived in terms of two pre‐recruit mortality parameters. Mace, &amp; Doonan, (1988, A generalized bioeconomic simulation model for fish population dynamics) reparameterized the stock‐recruit function in terms of steepness, which combines pre‐recruit mortality with post‐recruit biological parameters defining unfished spawning biomass per recruit. Their parameterization explicitly assumes a stable age distribution at unexploited conditions, but also implicitly assumes that unexploited spawning biomass per recruit is time invariant. Temporal variation could occur in either pre‐recruit mortality rates or post‐recruit biological parameters, but different dynamics are produced. The former results in variation in both the stock‐recruit curve and the population equilibria, whereas variation in the latter only changes the points of equilibria on the curve. Thus, variation in either pre‐ or post‐recruit parameters will result in variation in steepness, maximum sustainable yield and associated management reference points. Empirical measures of components of post‐recruit productivity (maturity and mass at age, e.g.) are available for many managed fish stocks and often exhibit temporal variability. Yet use of the steepness parameterization requires an analyst to specify one set of post‐recruit biological parameters to define unexploited spawning biomass per recruit for the full time series. This misspecification leads to misperception of biological reference points and has implications for meta‐analyses of steepness, and interpretation of the dynamic concept. Returning to the original parameterization allows the isolation of any temporal or inter‐population variation in pre‐ and post‐recruit productivity and reduces the potential for mechanistic bias in stock‐recruit parameters.

Financial Analysis of Herd Status and Vaccination Practices for Porcine Reproductive and Respiratory Syndrome Virus, Swine Influenza Virus, and Mycoplasma hyopneumoniae in Farrow-to-Finish Pig Farms Using a Bio-Economic Simulation Model.

This study aimed (1) to quantify the effects of positive status and vaccination practices for porcine reproductive and respiratory syndrome virus (PRRSv), swine influenza virus (SIV) and Mycoplasma hyopneumoniae (MHYO) on the profitability of farrow-to-finish pig farms and (2) to examine the financial impact of vaccination status in PRRSv and SIV positive farms. Data from 56 Irish farrow-to-finish pig farms were used for this study. Production effects associated with herd status for the three pathogens were incorporated into the Teagasc Pig Production Model (TPPM), a bio-economic stochastic simulation model for farrow-to-finish pig farms. In the analysis, farms negative (–) for either PRRSv, SIV or MHYO were assumed as baseline when presenting results for farms positive (+) for each pathogen. While all MHYO(+) farms used vaccination against the pathogen, not all PRRSv(+) or SIV(+) farms vaccinated against the disease. For all scenarios, a 728-sow farrow-to-finish farm with weekly farrowing batches was simulated. Financial risk analysis was conducted by Monte Carlo simulation within the TPPM using the Microsoft Excel add-in @Risk. Mortality rates, feedstuff costs and price per kg of meat produced were included as input stochastic variables and annual net profit was set as stochastic output variable. Positive farms sold fewer pigs and produced less kg of meat than negative farms and had increased feed usage during the weaner and finisher stages. Variable costs increased in positive farms due to increased feed costs, more dead animals for disposal and healthcare costs. Annual mean profit was lower by 24% in vaccinated PRRSv(+), 14.6% in unvaccinated PRRSv(+), 36.7% in vaccinating SIV(+), 12.8% in unvaccinated SIV(+), and 41% in MHYO(+) farms. Negative farms were first order stochastically dominant over positive farms, indicating that for a given level of profit, the financial risk is lower by avoiding respiratory pathogens. Similarly, unvaccinated farms were second order stochastically dominant over vaccinating farms suggesting that farms that do not vaccinate are less affected by the disease. Results from this study provide further evidence to encourage farmers to undertake improved disease control measures and/or to implement eradication programs.

The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach

Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain-fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fertility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation procedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub-humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2. The study finds that the use of improved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results suggest that the application of fertilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is projected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.

A simulation study to investigate the added value in using differential somatic cell count as an additional indicator for udder health management in dairy herds

Mastitis is one of the most costly diseases in dairy herds worldwide. Somatic cell count (SCC) is widely used as an indicator for subclinical intramammary infections (IMI) that may eventually cause mastitis in dairy herds. Differential somatic cell count (DSCC) has recently been introduced as an additional indicator for IMI. The objective of this study was to investigate the value of using DSCC as an additional indicator to select cows for testing and subsequent intervention for subclinical mastitis during the lactation. We parameterized an existing bio-economic simulation model for dairy herds to include DSCC. Then, we simulated three Danish dairy cattle herd situations with different pathogen distributions where the main pathogens were 1) Staphylococcus aureus, 2) Streptococcus agalactiae, and 3) Streptococcus uberis. In these herds, we simulated two different selection strategies for testing (bacterial culture) for subclinical IMI and various intervention strategies for test positive cases. The first selection strategy considered only SCC; cows were selected for testing if they had a low SCC measurement followed by two high SCC measurements. In the second selection strategy, cows additionally had to have a high DSCC measurement. Results showed that both selection strategies led to a similar net income and to a similar number of clinical and subclinical cases for all investigated intervention strategies. However, when using DSCC in the selection of animals, the number of treatment days and the number of cows culled in relation to IMI was reduced: The median annual number of treatment days was reduced by 25–38 days in herd 1, by 25–42 days in herd 2, and by 30–48 days in herd 3, depending on the intervention strategy. The median annual number of cows culled in relation to IMI was reduced by up to 8 cows (10 cows in herd 3) for one of the intervention strategies. Subject to limitations associated with model assumptions, these results suggest that considering DSCC when selecting cows for testing can reduce IMI related culling and the use of antibiotics without changing in-herd prevalence nor resulting in economic loss.

Dairy Farm Management when Nutrient Runoff and Climate Emissions Count

We provide a theoretical framework and detailed bioeconomic simulations to examine privately and socially optimal dairy farm management in the presence of nutrient runoff and greenhouse gas emissions. Dairy farms produce milk by choosing herd size, diet, fertilization and land allocation between crops, as well as (discrete) manure storage and spreading technologies and the number of milking seasons. We show analytically that a critical radius emerges for the choice of land use between silage and cereal cultivation and fertilizer types (mineral and manure). Both privately and socially optimal manure application rates decrease with application distance. We characterize the optimal climate and water policy instruments for dairy farming. A detailed bioeconomic simulation model links farm management decisions with their impacts on climate and water quality. We numerically solve the social and private optima and the features of optimal climate and water policy instruments. We show that using only climate instruments provides considerable water co‐benefits, and in the same vein, the use of water quality instruments provides considerable climate co‐benefits. Climate policies lead to a reduction in herd size, as measures relating to manure management and spreading are relatively inefficient at reducing climate emissions. There is much more leeway for adapting to water policies than to climate policies, because dairy farms have multiple measures to reduce their nutrient loads.

Land use change in Australian mixed crop-livestock systems as a transformative climate change adaptation

Mixed crop-livestock farming systems provide food for over half of the global population. However, some important food exporting countries, like Australia, are predicted to be vulnerable to climate change and may require transformative adaptations if they are to continue their role in food exportation. This paper assesses the potential impacts of projected climate change by 2030 (0.4–1.6° increase in mean temperature) on Australian mixed crop-livestock systems and examines the consequences of shifts in land allocations to cropping and grazing, in these systems, as an adaptation option. Farm bio-economic simulation models were developed for these mixed enterprise systems in several regions of Australia. These models were based on biophysically coupled crop, pasture, and livestock simulation models that in turn drew on site-based downscaled climate projection datasets. The farm models calculated farm profitability and risk measures. A range of land use changes was investigated. At drier locations facing adverse climate change, results showed a transition towards a greater emphasis on livestock production could be beneficial when assessed against multiple criteria of farm profit, downside financial risk, and environmental damage. We highlight some industry and government actions and policies that could facilitate these preferred adaptation strategies at such locations.

Modelling alternative management scenarios of economic and environmental sustainability of beef finishing systems

The livestock industry, and particularly beef production, is recognised as an important source of greenhouse gas (GHG) emissions linked to climate change. The complexity of beef systems means that appropriate GHG mitigating strategies depend on local conditions, requiring tailored entry points to be identified and evaluated. Using Scotland as a case study, here we combine a bio-economic simulation model and farm-level carbon footprinting tool to study the environmental impact of a range of beef production scenarios, and trade-offs generated between mitigating emissions and increasing farm profitability. To measure the environmental impact of finishing duration, type and gender selection of beef fattening systems, emissions were grouped into five categories: (1) land and crops, (2) enteric emissions, (3) manure, (4) feed and bedding, and (5) fuel and electricity. Results suggest that more intensive shorter duration systems have the lowest environmental impact of all the systems investigated. However, medium duration (i.e. 18–24 months) pasture-based beef production systems in Scotland were found to achieve a balance between financial returns and environmental performance.

How Integrated Ecological-Economic Modelling Can Inform Landscape Pattern in Forest Agroecosystems

The purpose of this review is to analyse recent advances in ecological-economic modelling designed to inform desirable landscape composition and configuration. We explore how models capture the economic and ecological consequences of landscape pattern, and potential feedbacks to the responses by policy or landholders. Modelling approaches are becoming increasingly interlinked, coupling components of empirical-statistical modelling, spatial and bioeconomic simulation, land-use optimization and agent-based models. We analyse recent methodological advances and find that only few examples capture feedbacks between landscape pattern and decision-making. We outline how future hybrid models could build on these recent advances by inter alia an improved representation of landscape patterns, refining the theory behind decision-making, incorporating uncertainty and reducing model complexity. We conclude that coupling recent developments in land-use optimization and agent-based models may help bridge gaps between modelling philosophies as well as parsimony vs. complexity. This fruitful field of research could help to improve understanding on the role of landscape pattern in social-ecological systems.

A strain-, cow-, and herd-specific bio-economic simulation model of intramammary infections in dairy cattle herds

Intramammary infections (IMI) in dairy cattle lead to economic losses for farmers, both through reduced milk production and disease control measures. We present the first strain-, cow- and herd-specific bio-economic simulation model of intramammary infections in a dairy cattle herd. The model can be used to investigate the cost-effectiveness of different prevention and control strategies against IMI. The objective of this study was to describe a transmission framework, which simulates spread of IMI causing pathogens through different transmission modes. These include the traditional contagious and environmental spread and a new opportunistic transmission mode. In addition, the within-herd transmission dynamics of IMI causing pathogens were studied. Sensitivity analysis was conducted to investigate the influence of input parameters on model predictions. The results show that the model is able to represent various within-herd levels of IMI prevalence, depending on the simulated pathogens and their parameter settings. The parameters can be adjusted to include different combinations of IMI causing pathogens at different prevalence levels, representing herd-specific situations. The model is most sensitive to varying the transmission rate parameters and the strain-specific recovery rates from IMI. It can be used for investigating both short term operational and long term strategic decisions for the prevention and control of IMI in dairy cattle herds.

Addition of meloxicam to the treatment of bovine clinical mastitis results in a net economic benefit to the dairy farmer

Recently, it has been shown that the addition of meloxicam to standard antimicrobial therapy for clinical mastitis (CM) improves the conception rate of dairy cows contracting CM in the first 120 d in milk. The objective of our study was to assess whether this improved reproduction through additional treatment with meloxicam would result in a positive net economic benefit for the farmer. We developed a stochastic bio-economic simulation model, in which a dairy cow with CM in the first 120 d in milk was simulated. Two scenarios were simulated in which CM cases were treated with meloxicam in conjunction with antimicrobial therapy or with antimicrobial therapy alone. The scenarios differed for conception rates (31% with meloxicam or 21% without meloxicam) and for the cost of CM treatment. Sensitivity analyses were undertaken for the biological and economic components of the model to assess the effects of a wide range of inputs on inferences about the cost effectiveness of meloxicam treatment. Model results showed an average net economic benefit of €42 per CM case per year in favor of the meloxicam scenario. Cows in the no-meloxicam treatment scenario had higher returns on milk production, lower costs upon calving, and reduced costs of treatment. However, these did not outweigh the savings associated with lower feed intake, reduced number of inseminations, and the reduced culling rate. The net economic benefit favoring meloxicam therapy was a consequence of the better reproductive performance in the meloxicam scenario in which cows had a shorter calving to conception interval (132 vs. 143 d), a shorter intercalving interval (405 vs. 416 d), and fewer inseminations per conception (2.9 vs. 3.7) compared with cows in the no-meloxicam treatment scenario. This resulted in a shorter lactation, hence a lower lactational milk production (8,441 vs. 8,517 kg per lactation) with lower feeding costs in the meloxicam group. A lower culling rate (12 vs. 25%) resulted in lower replacement costs in the meloxicam treatment scenario. All of the scenarios evaluated in the sensitivity analyses favored meloxicam treatment over no meloxicam. This study demonstrated that improvements in conception rate achieved by the use of meloxicam, as additional therapy for mild to moderate CM in the first 120 d in milk, have positive economic benefits. This inference remained true over a wide range of technical and economic inputs, demonstrating that use of meloxicam is likely to be cost effective across many production systems.

Simulação bioeconômica de sistemas produtivos em atividades de produção de bovinos de corte com ênfase em manutenção e recuperação de pastagens

A determinação correta dos custos de produção de bovinos de corte continua a ser um assunto relativamente confuso e, portanto, difícil para os produtores aplicar em prática. No presente estudo, foi utilizado um modelo de simulação bioeconômica, que descreve diversos sistemas de produção pecuária, para determinar a lucratividade e enfatizar o impacto das atividades de manutenção e recuperação de pastagens. Foram avaliados oito sistemas produtivos na região do Cerrado; um sistema padrão, denominado Modal, e sete sistemas Melhorados, denominados de 1 a 7, utilizando diferentes técnicas e atividades como manejo de pastagem, manejo nutricional, reprodução e saúde. Em todos os cenários simulados, o tamanho da propriedade foi uniforme (1500 hectares), onde 20% da área foi destinada à reserva ambiental. A rentabilidade dos cenários simulados foi de R$ 41.997 para o Modal, R$ 389.924 no Melhorado 1, R$ 395.427 no Melhorado 2, R$ 341.509 no Melhorado 3, R$ 454.198 no Melhorado 4, R$ 433.390 Melhorado 5, R$ 47.221 Melhorado 6 e R$ 348.587 Melhorado 7. Os resultados indicam que com um aumento no nível de adoção de tecnologias na propriedade, os custos de produção também aumentaram, no entanto, houve um aumento considerável das receitas, conduzindo a uma melhoria na utilização dos recursos tecnológicos. A utilização de atividades vinculadas às pastagens apresentou um impacto positivo, possibilitando uma maior rentabilidade mesmo havendo um aumento nos custos de produção, como visto nos cenários Melhorados 4 e 5, com um gasto de R$ 0,21 e R$ 0,27 em pastagem por cabeça/dia respectivamente. Os gastos com pastos provaram ser viáveis para os produtores, melhorando a produtividade e rentabilidade da atividade.

Modeling perceptions of climatic risk in crop production.

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.

Trade-offs between non-farm income and on-farm soil and water conservation investments of smallholder farmers in the semi-arid tropics of India

This paper has examined the trade-off between non-farm income and on-farm soil and water conservation (SWC) investment by smallholder farmers in the semi-arid tropics (SAT) of India. A dynamic bio-economic simulation model has been used to assess the impact of improved off-farm employment opportunities on household welfare, land degradation and labour allocation for SWC activities. The simulation results has revealed that improved non-farm employment opportunities increase the household welfare but reduce the households’ incentives to deploy labour for soil and water conservation measures, leading to higher levels of soil erosion and rapid land degradation. The study has suggested that there is the need of other complementary policy interventions to protect the natural resource base because improvement in non farm income opportunities does not produce a win-win solution in the watershed in the SAT region.