Performance Of Dairy Farms Research Articles

Modelling solar photovoltaic systems on dairy farms for cost savings and GHG emission reduction

Rising on-farm electricity demand, coupled with surges in electricity prices, has increased costs associated with milk production. Additionally, the use of grid electricity with a high carbon footprint depreciates the environmental performance of dairy farming. We assessed the potential of photovoltaic (PV) systems installed on dairy parlours under different policy incentives to reduce electricity costs and the carbon footprint of dairy farms in Ireland. The HOMER Pro software was employed to model electricity consumption, generation and economic performance of four 15-year PV project scenarios for 11 Irish farms. Scenarios considering the targeted agricultural modernisation scheme II (TAMS) and the microgeneration support scheme are assessed. The results show that PV systems are a feasible option to power dairy farms when current energy prices and inflation rates are applied. Small systems eligible for TAMS grants presented an average discounted payback period of 5 years, making them a better option for farmers than larger projects, which had an average payback period of 8.5 years. The deployment of PV systems reduced the GHG intensity of electricity consumed at the farms by up to 29 %.

Unveiling Determinants of Successful Dairy Farm Performance from Dairy Exporting EU Countries

Unveiling Determinants of Successful Dairy Farm Performance from Dairy Exporting EU Countries

Low-performance diagnosis of covered anaerobic lagoons as a waste management strategy in the intensive dairy industry

ABSTRACT Covered anaerobic lagoons (CALs) are Latin America's main livestock waste treatment systems. Mexico has 680 CALs that present low biogas yields (0.05 m3 m−3 digester d−1) and low COD removal rates (< 60%). This work focused on diagnosing CAL´s low performance in dairy farms by determining and analyzing operational parameters. Seven CALs located in the main dairy basin of Mexico were analyzed. The sampling areas for each CAL were the supernatant, the active zone, settled sludge, and digester inlet and outlet. The variation of the process parameter values corroborated that CALs appeared stratified and not working as expected. The sludge zone, comprising 50–58% of total solids content and 1–15% of total CALs volume, showed an elemental compounds content suitable for organic fertilizer (340, 48, and 5 kg t−1 of C, N, and S, respectively). However, this zone contained, at least, 85% of the slowly hydrolysable material; the methanogenic potential was less than 87 mL CH4 g VS−1, and the C/N ratio ranged from 4.9 to 17, outside of the optimal range. The biogas produced did not exceed 60% of methane content and more than 3000 ppm of H2S. The sludge zone significantly influences the lagoon's dynamics since it is a nutrient sink. Furthermore, the lack of agitation is the leading cause for the low energy yield and the low removal of organic matter rate. This work provides valuable information to address the operational problems within the CALs improving our understanding that shall allow proposing reactivation alternatives.

Adopting Critical Success Factors (CSF) Methods to Achieve High Management Performance on Dairy Farms

It is known that the agribusiness sector in Brazil is made up of several companies operating in different business models in their environment. They operate in production side as no cyclical and the market side as cyclical, such as milk and dairy products. It is, however, characterized by dynamic and complex processes and competitive events, applied technologies, specialized labor problems, with risks and uncertainties inherent to the environment. Dairy farming is a relevant segment of this sector, both in terms of representativeness and wealth generation. Therefore, dairy farms need to be aware of innovations, and be effective in the pursuit of productive performance and profitability. The present study aims to evaluate the Critical Success Factors (CSF) involved in the search for high productive performance and profitability in dairy farms assisted by technical and managerial consulting of an agribusiness alpha company. The research has a qualitative approach and a descriptive conclusive character. Multiple case studies were carried out, and the units of analysis are 6 farms, their respective leaders (owners and managers), and consultants specialized in dairy farming from the company Alfa de Consultoria. The results demonstrate that the CSFs that promote high performance in dairy farms are: financial management; leadership and people management; process management; employee training and development; adequate sizing of the project; search for continuous improvement; management of indices and technical indicators; ensure animal comfort; technical assistance and consultancy.

Meta-analysis of prevalence and associated risk factors for lameness in dairy cattle across tropical Asian nations

Context Lameness in dairy cattle affects the health, welfare, performance, and profitability of dairy farms. Being multifactorial in nature, several animal and farm-level risk factors influence the severity and prevalence of lameness. Aims The objective of the present meta-analysis study was to provide a pooled estimate of the prevalence and identification of the potential risk factors associated with lameness in dairy cattle across tropical Asian countries. Methods A literature search reporting prevalence data for lameness in dairy cattle of tropical Asian countries including India, Bangladesh, Pakistan, Malaysia, Nepal and Thailand for the past 21 years (2000–2021) was collected by using appropriate keywords from the electronic databases. After removing outlier studies, a meta-analysis of 38 studies published in English was performed under a random-effects model by using the meta package (metaprop function) in R software. Key results The pooled estimate of the overall prevalence of lameness was 15.13% (95% confidence interval [CI] = 13.01, 17.53). High heterogeneity was found in the prevalence estimates among the published studies (P &lt; 0.05). The pooled prevalence for lameness-related lesions was, for overgrown hooves 34.42% (95% CI = 23.41, 47.42), interdigital 28 hyperplasia 9.73% (95% CI = 5.84, 15.78), white line disease 6.91% (95% CI = 3.54, 13.05) and sole ulcer 6.84% (95% CI = 3.87, 11.83). The prevalence of lameness was higher in cows at parity three (23.03%) and at parity greater than or equal to 4 (31.68%) than at first and second parity. Breed of an animal, lower body condition score (BCS), housing and floor type, and season also had a significant effect on the pooled prevalence of lameness (P &lt; 0.0001). Conclusions The findings of the present study showed that lameness is prevalent across the tropical Asian countries and there was a significant association of risk factors such as housing, floor, hygiene, and animal nutrition with lameness in dairy herds. Implications Meta-analysis study indicated that there is a need to adopt better management practices to minimise the incidence of lameness in dairy animals.

Analysis of the Technical and Economic Performance of Dairy Farms in Algerian Conditions: The Case of the Wilaya of Tizi-Ouzou

The aim of this work is to carry out a comparative study of the costs of milk production for the two zones, plain and mountain, in the region of Tizi-Ouzou. Sixty-four farmers living in both areas were interviewed and surveyed. With an average of 12.5 cows per farm, there was no difference in the number of dairy cows between the two zones (P=0.586). Fodder consumption was 24.8 and 30.9 kg/cow/day in the mountain and plain areas respectively, a significant difference (P&lt;0.0001). Milk productivity was not significantly different (P = 0.202) between farmers in the two areas. The average was 21.7 litres per cow per day. The total cost of producing one litre of milk was 41.05 AD/L and 35.5 AD/L respectively, a significant difference (P &lt; 0.0001) between the two areas. The cost of feeding differs significantly (P &lt; 0.0001) between the two areas. Feed is more expensive in the mountains than in the lowlands (37.62 vs. 30.81 AD/L). The cost of buying hay was the most significant difference in feeding costs (P = 0.002). These costs were 17.43 vs. 12.73 AD/ha, respectively. There is a small difference in the cost of farm labour (P=0.095). It is lower in the mountain area (0.64 vs. 1.19 AD/L) than in the plain area. The main item in the total cost of milk production is the cost of feed. It represents 90 % and 85 % respectively in the mountain and in the plain areas. Forage area is a limiting factor for the development of milk production in the Tizi-Ouzou region. In the context of territorial development, it is proposed to develop fodder and silage production in regions of Algeria where there is an abundance of arable land and to transfer this fodder to dairy basins such as Tizi-Ouzou to feed dairy farms.

Effect of bovine subclinical mastitis on milk production and economic performance of Brazilian dairy farms

This review summarized the significant results from Brazilian studies published in peer-reviewed scientific papers about the effect of bovine subclinical mastitis (SM) on economic performance and milk production. Different approaches were considered for (i) disease detection (indirect measurement of somatic cell count (SCC) and directly using microbiological culture) and (ii) milk sampling strategy (mammary quarters, composite cow samples, and bulk milk tank). Globally, bovine mastitis is the most common disease of dairy herds, and the subclinical presentation is the most frequent. Dairy farmers usually underestimate the economic losses associated with SM because no visual changes in milk and quarters, udder, and systemic symptoms are observed. SM reduces milk yield and quality, reducing dairy herds’ profitability. The estimation of losses depends on the causative pathogen, the lactation stage, and the parity of affected cows. Thus, estimating the economic caused by SM in milk production and economic performance in dairy herds can be used to decide which mastitis control strategies to adopt. Mastitis control involves adopting specific measures associated with the characteristics of each herd, the period of the highest frequency of cases, the transmission form, and the profile of the pathogens involved in cases of intramammary infection. Thus, using individual SCC, the microbiological identification of pathogens causing SM, adopting efficient drying-off protocols, and other management practices are essential for mastitis control, improved milk quality, and greater profitability of dairy herds.

Milk yield residuals and their link with the metabolic status of dairy cows in the transition period

The transition period is one of the most challenging periods in the lactation cycle of high-yielding dairy cows. It is commonly known to be associated with diminished animal welfare and economic performance of dairy farms. The development of data-driven health monitoring tools based on on-farm available milk yield development has shown potential in identifying health-perturbing events. As proof of principle, we explored the association of these milk yield residuals with the metabolic status of cows during the transition period. Over 2 yr, 117 transition periods from 99 multiparous Holstein-Friesian cows were monitored intensively. Pre- and postpartum dry matter intake was measured and blood samples were taken at regular intervals to determine β-hydroxybutyrate, nonesterified fatty acids (NEFA), insulin, glucose, fructosamine, and IGF1 concentrations. The expected milk yield in the current transition period was predicted with 2 previously developed models (nextMILK and SLMYP) using low-frequency test-day (TD) data and high-frequency milk meter (MM) data from the animal's previous lactation, respectively. The expected milk yield was subtracted from the actual production to calculate the milk yield residuals in the transition period (MRT) for both TD and MM data, yielding MRTTD and MRTMM. When the MRT is negative, the realized milk yield is lower than the predicted milk yield, in contrast, when positive, the realized milk yield exceeded the predicted milk yield. First, blood plasma analytes, dry matter intake, and MRT were compared between clinically diseased and nonclinically diseased transitions. MRTTD and MRTMM, postpartum dry matter intake and IGF1 were significantly lower for clinically diseased versus nonclinically diseased transitions, whereas β-hydroxybutyrate and NEFA concentrations were significantly higher. Next, linear models were used to link the MRTTD and MRTMM of the nonclinically diseased cows with the dry matter intake measurements and blood plasma analytes. After variable selection, a final model was constructed for MRTTD and MRTMM, resulting in an adjusted R2 of 0.47 and 0.73, respectively. While both final models were not identical the retained variables were similar and yielded comparable importance and direction. In summary, the most informative variables in these linear models were the dry matter intake postpartum and the lactation number. Moreover, in both models, lower and thus also more negative MRT were linked with lower dry matter intake and increasing lactation number. In the case of an increasing dry matter intake, MRTTD was positively associated with NEFA concentrations. Furthermore, IGF1, glucose, and insulin explained a significant part of the MRT. Results of the present study suggest that milk yield residuals at the start of a new lactation are indicative of the health and metabolic status of transitioning dairy cows in support of the development of a health monitoring tool. Future field studies including a higher number of cows from multiple herds are needed to validate these findings.

Evaluating the effects of grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms

Grass management technologies (grass measuring devices and grassland management decision support tools) have been identified as important tools to improve the performance of pasture-based dairy farms. They have the potential to significantly improve the efficiency and sustainability of dairy systems by increasing milk production through enhanced pasture growth and utilization, which would reduce the need for supplementary feeds, along with increased output, therefore increasing farm profitability and environmental sustainability. Despite the several potential benefits of grass management technologies, there is a lack of empirical research around the effects of these technologies on the performance of pasture-based dairy systems. The current study aimed to fill this knowledge gap by using a 2018 nationally representative survey of Irish dairy farms and a propensity score matching approach to determine the effects of adopting grass management technologies on the physical, environmental, and financial performance of Irish pasture-based dairy farms. The findings showed that dairy farms utilizing grass management technologies had, on average, higher farm physical, environmental, and financial performance (in terms of grazed pasture use, total pasture use, length of the grazing season, milk yield, milk solids, greenhouse gas emissions per kilogram of fat- and protein-corrected milk, gross output, and gross margin) compared with dairy farms not utilizing these technologies. However, when controlling for selection bias, we can only attribute a positive causal effect of grass management technology adoption on the use of grazed pasture per cow, grazing season length, milk yield per cow, and milk solids per cow. This might be due to dairy farmers not yet using the technologies to their full potential, 2018 being an unusual year in terms of weather (and therefore not being able to capture the full range of farm performance benefits), or because grass management technologies need to be adopted in association with other technologies and practices to achieve their expected performance outcomes. Future research should include updated farm-level data to capture the weather and learning effects and so be able to determine the impact of grass management technologies on a wider range of performance indicators.

The water, land and carbon footprint of conventional and organic dairy systems in the Netherlands and Spain. A case study into the consequences of ecological indicator selection and methodological choices

Dairy farming systems are multifunctional processes that provide milk but also beef, veal and manure. These outputs provided by dairy farms are important foods for humans but their production require natural resources like water and land, and release emissions to the water and air contributing to climate change. Many studies quantified the environmental performance of dairy farms by using a life cycle assessment (LCA) or environmental footprint calculation. This study provides a better understanding of how different methodological decisions (e.g., the choice of system boundary, GHG metric, allocation procedure for multifunctionality, and multi-environmental indicators) influence the environmental performance calculation. From a footprinting point of view, the water footprints (WFs) (i.e., green, blue and grey), land footprints (LFs) and carbon footprints (CFs) of milk, beef and veal produced in two conventional (Dutch and Spanish) and an organic Dutch dairy system are estimated. Here the system boundaries are expanded so calve systems are included. Next, the use of different indicators is discussed, e.g., green WFs and the GWP100 or GWP20. The Dutch conventional system has relatively small footprints due to high efficiency. Green, blue and grey WFs per kg of milk are 0.62, 0.09 and 0.14 m3. The Spanish system has green, blue and grey WFs per kg of milk of 0.67, 0.15 and 0.09 m3; the Dutch organic system of 0.84, 0.13 and 0.26 m3. The Spanish system has the largest LF and CF, caused by feed import from countries with relatively low yields and transport greenhouse gas emissions. Dutch systems use more locally produced feed. Due to lower efficiency, the organic system has larger footprints than the Dutch conventional system. Expanding system boundaries to include calves results in an 8 to 15% CF increase. Green water dominates total WFs, an aspect excluded in LCA studies. For grey WFs, earlier studies only included nitrogen. However, if also pesticides would be included, results might be less favourable for systems relying on feed crops instead of grasslands. Also, water quality standards influence grey WFs. The study emphasizes that indicator choice influences final results. Indicators like animal welfare, biodiversity or pesticide use give different outcomes which might be more favourable for organic production.

Associations between dairy farm performance indicators and culling rates under policy-driven herd size constraints.

This article aimed to study cross-sectional associations between the performance of dairy farms and their corresponding culling proportions under the herd size constraint as imposed in 2018 by the new phosphate regulation in the Netherlands. To this end, production data from 10,540 Dutch dairy farms were analyzed to capture the inflow and outflow of both primiparous and multiparous cows. Farm performance was measured by 10 indicators structured in four areas of longevity, production, reproduction, and udder health. Farm culling proportions were represented by the overall culling (OC) and the number of culled primiparous cows in relation to (i) the total number of producing cows (PC), (ii) the number of producing primiparous cows (PPC), and (iii) the number of culled producing cows (POC). Spearman's rank correlation and weighted logistic regression were adopted to study associations. In 2018, on average, 28% of producing cows were culled (OC). The number of primiparous cows culled represented 4.5% of the total number of producing cows (PC) and the mean proportion of culled primiparous cows was 18.8% of the total number of producing primiparous cows (PPC), and, of the total number of producing culled cows, 15% were primiparous cows (POC). However, the variance around the mean, and among individual farms, was high (SD 4-15% for all four culling proportions). Results from rank correlation showed very low-rank conformity (<12%) between the areas of production, reproduction, and udder health to the culling proportions. Results from logistic regression showed that higher farm levels of production and higher percentages of cows with poor udder health were associated with more overall culling but with less primiparous culling. For reproduction indicators, the associations were similar for overall and primiparous culling. However, except for the average age of culled animals, the odds ratios for indicators were close to 1 (range: 0.92-1.07 and 0.68-1.07 for OC and PPC, respectively), indicating only weak associations to culling proportions. In conclusion, although the introduction of phosphate regulation resulted in an increased outflow of cattle, corresponding culling proportions were not associated with the level of farm performance measured in terms of production, reproduction, or udder health.

Economic Performance of Dairy Sheep Farms in Less-Favoured Areas of Greece: A Comparative Analysis Based on Flock Size and Farming System

Dairy sheep farming is a significant agricultural sector in Mediterranean countries, providing income and employment opportunities in less-favoured areas (LFAs). The economic performance of dairy sheep farms is of significant interest to LFAs. However, relevant literature is scarce. The objectives here were to evaluate the economic performance of dairy sheep farms in LFAs of Greece and perform a comparative analysis based on flock size and farming system. In total, 19 and 26 dairy sheep farms for two production periods were used. Farm technical (flock size, production, grazing and nutritional management) and economic (income and variable costs) data were collected. The economic performance of farms was estimated using Happy Goats, a decision support tool for small ruminant farming. Estimated economic parameters were analysed by flock size (≤150 ewes vs. &gt;150 ewes) and farming system (intensive/semi-intensive vs. semi-extensive). Results showed that 37% and 31% of farms were operating with losses in each production period, respectively. Based on nutritional management, ewes produced about 50 kg less milk per milking period. Smaller and semi-extensive farms had significantly (p &lt; 0.05) lower incomes and variable costs. A significantly (p &lt; 0.05) lower average gross margin was reported for smaller compared to larger farms. Results suggest a better economic perspective for larger flock sizes.

Relationships among total mixed ration nutritional components and reproductive performance in high-producing dairy herds

The main objective of the present study was to determine whether composition of total mixed ration influences reproductive performance in high-producing commercial dairy farms. Dairy producers and nutritional consultants from 48 dairy farms located in Wisconsin agreed to provide reproductive data and dietary information on high milk production pens during the main breeding period for the previous 12 mo. Dietary components (percentage of dry matter) were crude protein (CP), rumen degradable (RDP) and undegradable (RUP) protein, neutral detergent fiber (NDF), nonfiber carbohydrates (NFC), starch, and fat. Reproductive data were service rate (SR), overall pregnancy per artificial insemination (P/AI) and P/AI at the first service, 21-d pregnancy rate (PR), days open, and percentage of cows pregnant by 150 d in milk (PREG150). Participating herds had lactating Holstein cows (range = 143 to 2,717) housed in freestall facilities. Statistical analyses were performed with CORR and GLIMMIX of SAS (SAS Institute Inc.). Daily average milk production of herds was 38.9 ± 0.60 kg/d (30.0 to 50.4 kg/d). Overall SR was 58.5% (39–73) and P/AI was 36.1% (22–49). Overall 21-d PR was 20.3% (10–42%). Correlation between SR and PR was 0.59, whereas correlation of overall P/AI and P/AI at first service with PR were both 0.72. Similarly, for PREG150, correlation with overall P/AI (0.63) and P/AI at first service (0.66) were greater than with SR (0.48). There was large variation in diet composition, with CP varying from 16.0 to 18.7%, NDF from 24.9 to 35.1%, NFC from 31.7 to 46.6%, starch from 20.1 to 30.8%, and fat from 3.1 to 6.7%. Overall, there were no detectable associations of CP, RDP, and RUP with reproductive measures. The strongest relationship was a decrease in reproductive performance with increasing dietary NFC including overall P/AI (−0.48), P/AI at first service (−0.51), and PREG150 (−0.33). Starch also had a negative relationship with P/AI at first service (−0.35). Conversely, greater NDF was positively associated with P/AI at first service (0.34). Fat content was also positively associated with P/AI at first service (0.34). When NFC was divided in tertiles (<40, 40 to 42.2, and >42.2% NFC), the highest tertile had lower overall P/AI (39 vs. 36 vs. 31%), P/AI at first service (43 vs. 40 vs. 33%), and PREG150 (54 vs. 53 vs. 47%). In conclusion, farms with greater dietary NFC may have compromised reproductive performance. Correspondingly, herds with greater NDF content may achieve high milk production with potentially positive associations with reproduction. Other relationships of dietary components on reproduction were not as obvious in this herd-level analysis.

Potential improvement in the performance of dairy farms in South Africa

ABSTRACT Over 2003–2021, the pasture harvested on South African pasture-based dairy farms increased markedly. This increased production and consumption of pasture has helped to reduce the cost of producing milk in South Africa relative to dairying in other countries and delivered comparatively high levels of profit. National milk production has grown steadily. Over this same time, pasture as a proportion of the total diet of dairy herds has decreased significantly: supplements make up the major share of the diet. This change to dairy herd diets puts upward pressure on the average cost of feeding the herd and on the cost of production. The focus of this paper is on whether dairy farmers would be better off if they significantly increased the proportion of pasture in the total diet of their herds and relied less on supplementary feed. It is shown that progressively increasing the pasture component and proportion in the diet of dairy herds, from an industry average of 41% to 57%, could increase profit. Results were a 26% increase in profit (return on capital), a 59% increase in profit margin per litre, and a 7% decrease in cost of production per litre. If this change in production system to increased use of pasture and less use of supplementary feeds was replicated across the entire South African pasture-based dairy industry, farmers in the industry would be significantly more profitable and their businesses would be more resilient than under the current feeding regimes that are used.

Unraveling feed and nutrient use efficiencies in grassland-based dairy farms

Grassland-based dairy farms are important for the provisioning of milk and ecosystem services. However, the key factors and interactions that influence the feed use efficiency of grassland-based dairy farms in practice are not well known and understood, and as a consequence no well-targeted recommendations can be provided. This paper addresses the question ‘what are the main factors that determine the variations in calculated feed efficiency and N and P use efficiencies on dairy farms subjected to agri-environmental regulations’. Monitoring and modeling data from ~12000 grassland-based dairy farms in The Netherlands over a 4 year period (2017–2020), collected through the KringloopWijzer model, were analyzed and the data from 2020 were statistically analyzed in detail. Farms greatly differed in milk production intensity (range &amp;lt; 10 to &amp;gt;25 Mg per ha per yr) and in the amount of purchased feed. The 5 and 95 percentile values of frequency distribution of the calculated annual mean feed efficiency at herd level were 0.9 and 1.3 kg milk per kg feed dry matter, respectively. Feed efficiency was statistically related to milk yield and number of young stock per cow, the share of concentrates and silage maize in the ration, and the net energy content of silage grass. At herd level, the 5 and 95 percentile values of the calculated annual mean N use efficiency increased with feed efficiency from 21 to 28%, and those of the annual mean P use efficiency from 32 to 40%. Contrary to expectations, mean surpluses of N and P at farm level remained more or less constant with feed efficiency and the intensity of milk production, but the amounts of purchased feed and manure export strongly increased with the intensity of milk production. The N and P surpluses and use efficiencies at farm level were sensitive to accounting for the externalization of feed production and manure utilization. The modeled ammonia and methane emissions per kg milk produced were relatively low on farms with high feed efficiency. In conclusion, feed use and N and P use efficiencies are key indicator for the profitability and environmental performance of dairy farms. Differences between farms in these key indicators were large, and these differences were related to a limited number of explanatory variables. Our study provides lessons for improving the profitability and environmental performance of grassland-based dairy farms.

Influence of climate stress on technical efficiency and economic downside risk exposure of EU dairy farms

AbstractThis paper assesses the influence of heat and drought stress on the economic performance of the European dairy sector. Climatic data from the Gridded Agro-Meteorological data in Europe were combined with dairy enterprise data from the Farm Accountancy Data Network, resulting in a data set of 4412 farms in 22 European Union (EU) countries over the period 2007–2013. Since the performance of dairy farms is influenced by the context in which they operate, farms were grouped into areas representing similar climatic conditions through the use of a latent class analysis. Technical efficiency (TE) and economic downside risk were used as performance indicators against which the effect of climatic stress factors was evaluated. TE was estimated using a ‘true-fixed’ effect stochastic frontier model. Economic downside risk was based on gross margin deviations. Regression analysis suggests a significant negative effect of drought and heat stress on both TE and the downside gross margin difference in most climatic classes, with few exceptions. Results imply that both drought and heat stress-related issues need to be considered when designing adaptation strategies to address threats to the economic performance of the EU dairy sector.

The Effect of Robotic Milking Systems on Economic Performance of Dairy Farms with a Simulation Model

The most remarkable technology brought to dairy farms by the digital transformation in agriculture is undoubtedly robotic milking systems (RMS). Knowing the economic impact of this technology is essential for farmers to adopt. For this purpose, in the study; a simulation model was created that gives possible economic analysis results as a result of the use of RMS by using the current economic analysis results of dairy farms. For the economic analysis of dairy farms, data obtained from face-to-face surveys from 148 dairy farms were used. Assumptions used in the simulation model for comparing RMS and conventional milking systems (CMS) were 8.66% increase in milk yield, 58.46% increase in investment costs, 36.66% increase in energy consumption, 1.33% increase in feed costs and 27.84% decrease in labor input. The economic analysis of the dairy farms was made again with these new input and output values obtained. While the simulation results show that the use of RMS is a preferable investment that increases profitability for 10-60 head and 121 + head groups; it shows that it will be an investment that negatively affects profitability for the 61-120 head group. The simulation model was used by taking the average values of the data belonging to the dairy farm groups. A dairy farmer considering an RMS investment can be able to obtain a result specific to his farm if he combines the simulation model with his own economic analysis results.

Inseparability of Dairy Farming Technologies and Their Impacts on Milk Production Systems in Brazil

Several studies have analyzed the impact of one technology on the productive performance of dairy systems. However, analyses that consider the impact of joint technologies are scarce. This study was aimed to analyze the inseparability of a set of technologies and their impacts on dairy farm performance. Questionnaires were applied on-site with 155 dairy farmers in Paraná State, Brazil. We collected 17 technological variables related to milk production and variables related to dairy farms’ production. Data analysis was performed in three steps: descriptive analysis, exploratory factor analysis (EFA), and multiple regression. Descriptive analysis was applied to characterize the sample, EFA was applied to generate factors related to the technological variables, and multiple regression was used to compare technological factors with productivity variables – farm performance. Four factors were defined: (i) Forage and farm structure, (ii) management, (iii) genetics, breeding strategies, and concentrate feeding, and (iv) animal health. The four factors significantly explained the differences in milk productivity between dairy farms. Technologies grouped under the factor of genetics were the most important in explaining milk yield per lactating cow, land productivity, and dairy herd reproductive efficiency.

Linking entrepreneurial orientation and managerial capacity to performance in dairy farms

Abstract: The main objective of the study is to develop and test a model that emphasizes the relationship between entrepreneurial orientation (EO) and managerial capacity (MC) to the performance of dairy farms. This article also examines the effect of these variables on performance expectations as well as the succession planning of farms. Cross-sectional data were collected out of a sample of 158 dairy farmers in 2017 in Brazil. A path analysis approach has been applied to validate the hypothesized model. Results show that between EO dimensions, innovativity has the greatest impact on performance as well as on MC. MC is not significantly related to the current performance on dairy farms, but it is so to the performance expectation, which in turn has an impact on the succession planning of farms. The age of the farmers moderates the relations hypothesized in the model, especially between innovativity, proactivity, and MC in the farms’ performance.

Effects of market orientation on farmer resilience and dairy farm performance in emerging economy

Effects of market orientation on farmer resilience and dairy farm performance in emerging economy