V.E. Cabrera

Determinants of Technical Efficiency Among Dairy Farms in Wisconsin

The US dairy sector is facing structural changes including a geographical shift in dairy production and a tendency toward the implementation of more intensive production systems. These changes might significantly affect farm efficiency, profitability, and the long-term economic sustainability of the dairy sector, especially in more traditional dairy production areas. Consequently, the goal of this study was to examine the effect of practices commonly used by dairy farmers and the effect of intensification on the performance of the farms. We used a sample of 273 Wisconsin dairy farms to estimate a stochastic production frontier simultaneously with a technical inefficiency model. The empirical analysis showed that at a commercial level the administration of bovine somatotropin hormone to lactating cows increased milk production. In addition, we found that production exhibits constant returns to scale and that farm efficiency is positively related to farm intensification, the level of contribution of family labor in the farm activities, the use of a total mixed ration feeding system, and milking frequency.

An economic decision-making support system for selection of reproductive management programs on dairy farms

Because the reproductive performance of lactating dairy cows influences the profitability of dairy operations, predicting the future reproductive and economic performance of dairy herds through decision support systems would be valuable to dairy producers and consultants. In this study, we present a highly adaptable tool created based on a mathematical model combining Markov chain simulation with partial budgeting to obtain the net present value (NPV;

Decision tree analysis of treatment strategies for mild and moderate cases of clinical mastitis occurring in early lactation

/cow per year) of different reproductive management programs. The growing complexity of reproductive programs used by dairy farms demands that new decision support systems precisely reflect the events that occur on the farm. Therefore, the model requires productive, reproductive, and economic input data used for simulation of farm conditions to account for all factors related to reproductive management that increase costs and generate revenue. The economic performance of 3 different reproductive programs can be simultaneously compared with the current model. A program utilizing 100% visual estrous detection (ED) for artificial insemination (AI) is used as a baseline for comparison with 2 other programs that may include 100% timed AI (TAI) as well as any combination of TAI and ED. A case study is presented in which the model was used to compare 3 different reproductive management strategies (100% ED baseline compared with two 100% TAI options) using data from a commercial farm in Wisconsin. Sensitivity analysis was then used to assess the effect of varying specific reproductive parameters on the NPV. Under the simulated conditions of the case study, the model indicated that the two 100% TAI programs were superior to the 100% ED program and, of the 100% TAI programs, the one with the higher conception rate (CR) for resynchronized AI services was economically superior despite having higher costs and a longer interbreeding interval. A 4% increase in CR for resynchronized AI was sufficient for the inferior 100% TAI to outperform the superior program. Adding ED to the 100% TAI programs was only beneficial for the program with the lower CR. The improvement in service rate required for the 100% ED program to have the same NPV as the superior 100% TAI program was 12%. The decision support system developed in this study is a valuable tool that may be used to assist dairy producers and industry consultants in selecting the best farm-specific reproductive management strategy.

A daily herd Markov-chain model to study the reproductive and economic impact of reproductive programs combining timed artificial insemination and estrus detection.

The objective of this study was to develop a decision tree to evaluate the economic impact of different durations of intramammary treatment for the first case of mild or moderate clinical mastitis (CM) occurring in early lactation with various scenarios of pathogen distributions and use of on-farm culture. The tree included 2 decision and 3 probability events. The first decision evaluated use of on-farm culture (OFC; 2 programs using OFC and 1 not using OFC) and the second decision evaluated treatment strategies (no intramammary antimicrobials or antimicrobials administered for 2, 5, or 8 d). The tree included probabilities for the distribution of etiologies (gram-positive, gram-negative, or no growth), bacteriological cure, and recurrence. The economic consequences of mastitis included costs of diagnosis and initial treatment, additional treatments, labor, discarded milk, milk production losses due to clinical and subclinical mastitis, culling, and transmission of infection to other cows (only for CM caused by Staphylococcus aureus). Pathogen-specific estimates for bacteriological cure and milk losses were used. The economically optimal path for several scenarios was determined by comparison of expected monetary values. For most scenarios, the optimal economic strategy was to treat CM caused by gram-positive pathogens for 2 d and to avoid antimicrobials for CM cases caused by gram-negative pathogens or when no pathogen was recovered. Use of extended intramammary antimicrobial therapy (5 or 8 d) resulted in the least expected monetary values.

IMPACT OF CLIMATE INFORMATION ON REDUCING FARM RISK BY OPTIMIZING CROP INSURANCE STRATEGY

Our objective was to compare the economic and reproductive performance of programs combining timed artificial insemination (TAI) and different levels of AI after estrus detection (ED) using a daily Markov-chain model. A dairy herd was modeled with every cow following daily probabilistic events of aging, replacement, mortality, pregnancy, pregnancy loss, and calving. The probability of pregnancy depended on the combination of probability of insemination and conception rate (CR). All nonpregnant cows had a probability of pregnancy between the end of the voluntary waiting period and days in milk cutoff for AI. After the cutoff, cows were labeled as do not breed and replaced when milk production was below a minimum milk threshold. A similar model was created to represent a replacement heifer herd to simulate and adjust the supply and demand of replacements. The net value (NV) of a program was the sum of milk income over feed cost, replacement and mortality cost, income from newborns, and reproductive costs. The model was used to compare the NV of 19 programs. One program used 100% TAI (42% CR for first TAI and 30% for second-and-later services), whereas the other programs combined TAI with ED. The proportion of cows receiving AI after ED for the combined programs ranged from 30 to 80%, with levels of CR of 25, 30, and 35%. As the proportion of cows receiving AI after ED increased, the CR of cows receiving TAI decreased. The combined programs with CR of 35% for cows receiving AI after ED had the greatest NV and reproductive performance at all levels of ED. The program using 100% TAI had greater NV and better reproductive performance than all programs with 25% CR after ED inseminations, whereas it had very similar performance to combined programs with up to 60% of cows receiving AI after ED and 30% CR. The factor with the greatest relative contribution to the differences among programs was income over feed cost, followed by replacement and reproductive costs. Adjusting the days in milk cutoff for AI to match the supply and demand of heifer replacements improved the NV of all programs except for those with 25% CR after ED, which had either no change or a decrease in NV. In summary, the economic value of reproductive management programs combining TAI and ED depended on the proportion of cows receiving AI after ED and the resulting CR. Adjusting the heifer supply and demand increased the NV of programs with heifer surplus and decreased the NV of programs with heifer deficit.

Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms.

Predictability of seasonal climate variability associated with the El Nino Southern Oscillation (ENSO) suggests a potential to reduce farm risk by selecting crop insurance products with the purpose of increasing farm income stability. A hypothetical 50% peanut, 50% cotton, non-irrigated, 40 ha (100 ac) north Florida farm was used to study the interactions of different crop insurance products with ENSO-based climate information and levels of risk aversion under uncertain conditions of climate and prices. Crop yields simulated by the DSSAT suite of crop models using multiyear weather data combined with historical series of prices were used to generate long series of stochastic income distributions in a whole-farm model portfolio. The farm model optimized planting dates and simulated uncertain incomes for 50 alternative crop insurance combinations for different levels of risk aversion under different planning horizons. Results suggested that incomes are greatest and most stable for low risk-averse farmers when catastrophic (CAT) insurance for cotton and 70% or 75% actual production history (APH) for peanut are selected in all ENSO phases. For high risk-averse farmers, the best strategy depends on the ENSO phase: (1) 70% crop revenue coverage (CRC) or CAT for cotton and 65% APH for peanut during EL Nino years; (2) CAT for cotton and 65%, 70%, or 75% APH for peanut during neutral years; and (3) 65% to 70% APH, or CAT for cotton and 70% APH for peanut during La Nina years. Optimal planting dates varied for all ENSO phases, risk aversion levels, and selected crop insurance products.

Economics of resynchronization strategies including chemical tests to identify nonpregnant cows

When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.

A simple formulation and solution to the replacement problem: A practical tool to assess the economic cow value, the value of a new pregnancy, and the cost of a pregnancy loss

Our objectives were to assess (1) the economic value of decreasing the interval between timed artificial insemination (TAI) services when using a pregnancy test that allows earlier identification of nonpregnant cows; and (2) the effect of pregnancy loss and inaccuracy of a chemical test (CT) on the economic value of a pregnancy test for dairy farms. Simulation experiments were performed using a spreadsheet-based decision support tool. In experiment 1, we assessed the effect of changing the interbreeding interval (IBI) for cows receiving TAI on the value of reproductive programs by simulating a 1,000-cow dairy herd using a combination of detection of estrus (30 to 80% of cows detected in estrus) and TAI. The IBI was incremented by 7d from 28 to 56 d to reflect intervals either observed (35 to 56 d) or potentially observed (28 d) in dairy operations. In experiment 2, we evaluated the effect of accuracy of the CT and additional pregnancy loss due to earlier testing on the value of reproductive programs. The first scenario compared the use of a CT 31 ± 3 d after a previous AI with rectal palpation (RP) 39 ± 3 d after AI. The second scenario used a CT 24 ± 3 d after AI or transrectal ultrasound (TU) 32 d after AI. Parameters evaluated included sensitivity (Se), specificity (Sp), questionable diagnosis (Qd), cost of the CT, and expected pregnancy loss. Sensitivity analysis was performed for all possible combinations of parameter values to determine their relative importance on the value of the CT. In experiment 1, programs with a shorter IBI had greater economic net returns at all levels of detection of estrus, and use of chemical tests available on the market today might be beneficial compared with RP. In experiment 2, the economic value of programs using a CT could be either greater or less than that of RP and TU, depending on the value for each of the parameters related to the CT evaluated. The value of the program using the CT was affected (in order) by (1) Se, (2) Sp, (3) pregnancy loss, (4) proportion of Qd, (5) percentage of cows AI in estrus, and (6) cost of CT. A change of 1% in the Se of the CT was 1.8 times more important than a similar change in Sp or pregnancy loss, and 13.7, 55.0, and 305.8 times more important than similar changes in Qd, cows inseminated in estrus, and cost of CT. We conclude that the major effect of using a CT is the potential of decreasing the IBI. Moreover, inaccuracy of the CT and additional pregnancy loss due to earlier testing resulted in smaller economic differences than when using RP or TU 8d later.

A large Markovian linear program to optimize replacement policies and dairy herd net income for diets and nitrogen excretion

This study contributes to the research literature by providing a new formulation for the cow replacement problem, and it also contributes to the Extension deliverables by providing a user-friendly decision support system tool that would more likely be adopted and applied for practical decision making. The cow value, its related values of a new pregnancy and a pregnancy loss, and their associated replacement policies determine profitability in dairy farming. One objective of this study was to present a simple, interactive, dynamic, and robust formulation of the cow value and the replacement problem, including expectancy of the future production of the cow and the genetic gain of the replacement. The proven hypothesis of this study was that all the above requirements could be achieved by using a Markov chain algorithm. The Markov chain model allowed (1) calculation of a forward expected value of a studied cow and its replacement; (2) use of a single model (the Markov chain) to calculate both the replacement policies and the herd statistics; (3) use of a predefined, preestablished farm reproductive replacement policy; (4) inclusion of a farmers assessment of the expected future performance of a cow; (5) inclusion of a farmers assessment of genetic gain with a replacement; and (6) use of a simple spreadsheet or an online system to implement the decision support system. Results clearly demonstrated that the decision policies found with the Markov chain model were consistent with more complex dynamic programming models. The final user-friendly decision support tool is available at http://dairymgt.info/ → Tools → The Economic Value of a Dairy Cow. This tool calculates the cow value instantaneously and is highly interactive, dynamic, and robust. When a Wisconsin dairy farm was studied using the model, the solution policy called for replacing nonpregnant cows 11 mo after calving or months in milk (MIM) if in the first lactation and 9 MIM if in later lactations. The cow value for an average second-lactation cow was as follows: (1) when nonpregnant, (a)

Effect of diets containing a controlled-release urea product on milk yield, milk composition, and milk component yields in commercial Wisconsin dairy herds and economic implications.

897 in MIM = 1 and (b)