Accounting for Heterogeneity in Performance Evaluation of Norwegian Dairy and Crop-Producing Farms

The aim in this paper is to investigate economies of scale and scope among Norwegian dairy and crop producing farms, controlling for regional differences. Unlike previous studies in which a common technology was assumed, we estimate economies of scale and scope to account for different technologies for specialized and mixed (diversified) farms. Our analysis is based on translog cost functions using farm-level data for the period 1991-2014. The results suggest that both economies of scale and scope persist in Norwegian dairy and crop producing farms. We also find that dairy farms have an economic incentive to integrate dairy farming with crop production in all regions of Norway.

The landscape composition of organic and conventional, dairy and crop farms in two different geological regions in Denmark

The yield and quality decrease due to high somatic cell counts caused by mastitis, and this also negatively affects the profitability, efficiency, and sustainability of dairy farms. The main objective of this study was to investigate the effects of somatic cell counts on yield, milk chemicals, and the technical efficiency of Holstein dairy cows. A total of 165 lactating cows were involved in the research, and all cows were fed the diets as a total mixed ration three times a day. Milk samples were collected each day during milking and analyzed for chemical composition and somatic cell counts (SCC). The daily milk production of each cow was obtained from the SCR herd management program, which is integrated with the parlor. In conclusion, it was determined that for each group, the efficiency scores, SCC, and milk yield of cows varied between 0.80 and 0.99, 322.000 and 557.857 cells/mL, and 33.13 and 48.90 Kg/d, respectively; they also differed significantly in each group. Considering the findings, milk production can be increased by 7% without changing any input. Additionally, every 1% decrease in SCC will increase the efficiency of milk production by 0.55%. Cows with low technical efficiency (TE) scores produced 2.87 kg/d/cow less milk compared to animals with high TE. Reducing the SCC of the group with a low TE (456.878 cells/mL) to a SCC of high TE (438.869 cells/mL) will increase milk yield by 2.87 kg/d/cow on average. In conclusion, minimizing losses due to mastitis is paramount for enhancing dairy farm efficiency. This research underscores the interplay between TE and udder health, providing a comprehensive understanding of individual cow performance. Addressing inefficiencies and promoting udder health can significantly contribute to sustainable and economically viable dairy farming practices. Keywords: Dairy farm; Mastitis; Somatic cell count; Stochastic frontier analysis; Technical efficiency

The aim of this article is to investigate the relationship between size and farm growth. The existing theories of the association between size and farm growth give mixed results by countries and over time. This paper pursues a twofold objective: on one hand, to test the validity of Gibrat’s Law for French, Hungarian and Slovenian specialized dairy and crop farms during the pre- and post-accession period to the European Union membership. Dairy and crops farms are prevailing in the farming structure of these countries. Using Farm Accountancy Data Network datasets makes it necessary to avoid biases due to heterogeneous structures across the farming systems. Thus we use quantile regressions to control for farm size related heterogeneity in the samples. On the other hand, the main novelty of this paper is the comparative analysis of the relationship between farm size and farm growth between transition Hungarian and Slovenian and non-transition French farming sectors, characterized by rather different farm structures. The results reject the validity of Gibrat’s Law for crop farms in Hungary and to a lesser extent in France, and for French and Slovenian dairy farms. We provide evidence that smaller farms grew faster than larger ones over the studied period 2001-2007 for France, 2001-2008 for Hungary, and 2004-2008 for Slovenia. Conversely, the results for Slovenia suggest that the rate of growth of crop farms in terms of its land is independent from its size.

The average net farm income is $66,412 for the 62 farms included in the 1999 annual report of the Southeastern Minnesota Farm Business Management Association. This is an increase of 1% from 1998. Even though gross cash farm income increased, cash expenses and depreciation also increased and inventory values changed little. Income is still at a high level compared to the early 1990s and the 1980s. (Net farm income is an accrual measure calculated by subtracting cash farm expenses and depreciation from total cash farm income and adjusting the difference for changes in other capital and inventory items.) After subtracting an opportunity cost for equity capital, unpaid labor and management earnings follow a similar but lower pattern. As in previous years, the income levels experienced by individual farms vary greatly from the overall average. The high 20% of these farms had an average net farm income of $222,349 in 1999; farms in the low 20%, -$10,442. This is an increase for both the high group and the low group. Average gross cash farm income in 1999 was $411,665 for these 62 farms. This is a 29% increase from 1998. Together, milk, corn, hog, and soybean sales were 75% of gross income in 1999. Compared to 1998, milk sales increased by 6%; corn sales by 26%; beef finishing sales by 26%. Hog sales increased by 270% from the extremely low levels in 1997. Soybean sales decreased by 9%. Government payments (of all types) more than doubled from an average of $23,322 in 1998 to $50,700 in 1999. (They were $12,907 in 1997.) Government payments were 12% of gross income in 1999, compared to 7% in 1998 and 4% in 1997. Average total cash expenses were $314,644 in 1999. This is an increase of 31% from the 1998 average. As a percentage of both cash expenses and depreciation, feed expenses were 20% in 1999, up from 1998. Seed, fertilizer, and crop chemicals were 15% of the total, down from 1998. Interest expense was 6% of the total, lower than in 1998. Real estate taxes amounted to 2% in 1999--slightly lower in percentage but slightly higher in absolute dollar level. Both the rate of return on assets (ROA) and the rate of return to equity (ROE) remained unchanged on average. However, ROA was slightly higher than ROE indicating that debt capital was earning less than it was costing. Average total equity (of the 49 sole proprietors) was $523,529 at the end of 1999, an increase of $45,645 during the year. (Assets were valued on a cost basis.) Except for a decline during 1993, average equity has improved steadily since 1986. At the end of 1999, the average debt-asset ratio was down slightly to 34%. In 1999, crop yields were lower than the record levels of 1998 for the Association. The average corn yield was 156 bushels per acre; soybeans were at 45 bushels per acre. Results by Type of Farm The 62 farms in the report are classified as a certain type (e.g., dairy) on the basis of having 70 percent or more of their gross sales from that category. Using this 70 percent rule, there are 9 crop farms, 16 dairy farms, and 7 crop and dairy farms, and 7 crop and hog farms. There are 18 farms which do not have a single source (or pair of sources) of income over 70%. The average crop and dairy farm had the highest average net farm income ($145,058) in 1999. The average dairy farm had the second highest net farm income. In terms of the rate of return to assets (ROA), dairy farms and crop and dairy farms have the highest ROA (10%) in 1999. (Assets are valued on a cost basis.) (There were less than 5 crop and hog farms in 1998, so that information is not reported.) Crop and hog farms had an average debt-asset ratio of 48% in 1999; crop farms averaged 46%; and other farms averaged less than 40%. The report provides additional information on profitability, liquidity, and solvency as well as other whole-farm information and detailed information on crop and livestock enterprises. Also reported are whole-farm financial condition and performance by county, sales size class, and type of farm and corn and soybean returns by county.

PurposeThe purpose of this paper is to explore the economic performance of Norwegian crop farms using a stochastic frontier analysis.Design/methodology/approachThe analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from 455 Norwegian farms specialized in crop production in eastern and central regions of Norway.FindingsThe results of the analysis show that the mean efficiency was about 78–81 percent. Farm management practices and socioeconomic factors were shown to significantly affect the economic performance of Norwegian crop farms.Research limitations/implicationsFarmers are getting different types of support from the government and the study does not account for the different effects of different kinds of subsidy on cost efficiency. Different subsidies might have different effects on farm performance. To get more informative and useful results, it would be necessary to repeat the analysis with less aggregated data on subsidy payments.Practical implicationsOne implication for farmers (and their advisers) is that many of them are less efficient than the estimated benchmark (best performing farms). Thus, those lagging behind the best performing farms need to look at the way they are operating and to seek out ways to save costs or increase crop production. Perhaps there are things for lagging farmers to learn from their more productive farming neighbors. For instance, those farmers not practicing crop rotation might be well advised to try that practice.Social implicationsFor both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize farmers appear to bring some benefits in terms of more efficient production that, in turn, increase the supply of some foods so possibly making food prices more affordable.Originality/valueUnlike previous performance studies in the literature, the authors estimated farm-level economic performance accounting for the contribution of both an important farm management practice and selected socioeconomic factors. Good farm management practices, captured through crop rotation, land tenure, government support and off-farm activities were found to have made a positive and statistically significant contribution to reducing the cost of production on crop-producing farms in the Central and Eastern regions of Norway.

Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA

The average net farm income is $77,672 for the 58 farms included in the 2000 annual report of the Southeastern Minnesota Farm Business Management Association. This is an increase of 17% from 1999. The median or middle income was $39,675, considerably lower than the average. Even though gross cash farm income decreased more than the decrease in cash expenses, net farm income increased because depreciation decreased and inventory values increased. Income is still at a high level compared to the early 1990s and the 1980s. As in previous years, the income levels experienced by individual farms vary greatly from the overall average. When the net farm incomes for the 58 farms in the report were ranked from lowest to highest, the resulting graph shows how much the incomes do vary. Several farms experienced negative incomes, and several experienced very high incomes. Most of the net farm income ranged from just below 0 to about $140,000. The median or middle income was $39,675. The high 20% of these farms had an average net farm income of $250,243 in 2000; farms in the low 20%, -$15,401. This was an increase for the high group and a decrease for the low group. Average gross cash farm income in 2000 was $352,354 for these 58 farms. This was a 14% decrease from 1999. Together, milk, corn, and soybean sales were 65% of gross income in 2000. Compared to 1999, milk sales decreased by 15% and corn sales by 14. Soybean sales increased by 10%. Government payments (of all types) averaged $50,496 in 2000. They were $50,700 in 1999, $23,322 in 1998, and $12,907 in 1997. Government payments were 14% of gross income in 2000, compared to 12% in 1999, 7% in 1998, and 4% in 1997. Average total cash expenses were $267,986 in 2000. This was a decrease of 15% from the 1999 average. As a percentage of both cash expenses and depreciation, feed expenses were 14% in 2000, down from 1999. Seed, fertilizer, and crop chemicals were 16% of the total, up from 1999. Interest expense was 8% of the total, higher than in 1999. Real estate taxes remained at 2% in 2000 although the absolute dollar level was slightly lower. Both the rate of return on assets (ROA) and the rate of return to equity (ROE) increased on average. ROE was slightly higher than ROA indicating that debt capital was earning more than it was costing. Average total equity (of the 46 sole proprietors) was $553,823 at the end of 2000, an increase of $39,719 during the year. (Assets were valued on a cost basis.) Except for a decline during 1993, average equity has improved steadily since 1986. At the end of 2000, the average debt-asset ratio was up slightly to 35%. In 2000, crop yields were again lower than the record levels of 1998 for the Association. The average corn yield was 154 bushels per acre; soybeans were up slightly to 49 bushels per acre. Results by Type of Farm The 58 farms in the report are classified as a certain type (e.g., dairy) on the basis of having 70 percent or more of their gross sales from that category. Using this 70 percent rule, there are 10 crop farms, 14 dairy farms, and 5 crop and hog farms. There were less than 5 crop and dairy farms so that data is not reported. There are 21 farms which do not have a single source (or pair of sources) of income over 70%. The average crop farm had the highest average net farm income ($111,775) in 2000. The average dairy farm had the second highest net farm income. In terms of the rate of return to assets (ROA), crop farms had the highest ROA (13%) in 2000. (Assets are valued on a cost basis.) Dairy farms had an average debt-asset ratio of 29% in 1999; crop farms averaged 30%. The report provides additional information on profitability, liquidity, and solvency as well as other whole-farm information and detailed information on crop and livestock enterprises. Also reported are whole-farm financial condition and performance by county, sales size class, and type of farm and corn and soybean returns by county.

Poland, one of the candidate countries for European Union membership, is currently experiencing acute structural problems within its agriculture sector. This article analyses technical efficiency and its determinants for a panel of individual farms in Poland specialized in crop and livestock production in 2000. Technical efficiency is estimated with stochastic frontier analysis (SFA) and confidence intervals are constructed. Determinants of inefficiency are also evaluated. The SFA results are compared with results using Data Envelopment Analysis (DEA). On average, livestock farms are more technically efficient than crop farms. For both specializations, the size–efficiency relationship is positive, that is large farms are more efficient. The SFA findings are generally supported by the DEA results. Soil quality and the degree of integration with downstream markets are highly important determinants of efficiency. The use of factor markets (land and labour) is important for crop farms, while livestock farms can rely on family labour and own land. Also, education is a constraint to efficiency particularly for crop farms.

Maintaining valid financial records is critical to making informed decisions about its operations. With no clear record-keeping incentives, farmers should miss important data on income, expenses, profitability, and financial health. The main objective of this research study was to assess the effects of proper financial record-keeping and sustainable growth of agribusiness enterprises in Rwanda (case study: Moozay’s Dairy and Crop Farm Limited). The specific objectives of this study included assessing the effect of purchase bookkeeping on sustainable growth of agribusiness enterprises in Rwanda, analyzing the effect of sales bookkeeping on sustainable growth of agribusiness enterprises in Rwanda, and determining the effect of cash bookkeeping on sustainable growth of agribusiness enterprises in Rwanda. This study is based on agency theory, double-entry bookkeeping theory, and accounting theory. A mixed-methods research design of descriptive and correlational studies was employed to collect both quantitative and qualitative information for analysis. The primary data were gathered by distributing questionnaires to a randomly chosen group of employees working in the finance department at Moozay's Dairy and Crop Farm Ltd. The targeted population of this study was equal to 100 respondents, and the sample population of this study is equal to 80 respondents. The data analysis procedure involved both quantitative and qualitative processes. Purchase book keeping (β=0.248, t=2.654), sales book keeping (β=0.330, t=3.321), and cash book keeping (β=0.337, t=3.331) indicate that for every one-unit increase in these variables, there is a corresponding increase in the sustainable growth of agribusiness enterprises in Moozay's Dairy and Crop Farm Limited. Furthermore, the significance values (Sig. 0.010, 0.001, 0.001) associated with these financial record-keeping variables are all notably below the typical significance level (0.05), demonstrating their strong statistical significance and emphasizing their significant roles in contributing to the sustainable growth of agribusiness enterprises in Rwanda. Also, the findings showed an R value of 0.855, an R square value of 0.731, and an adjusted R square value of 0.720, which indicates that the model adequately accounts for 73.1% of the variation in project success. Overall, Moozay's Dairy and Crop Farm Limited's research revealed strong agreement among respondents about the significant impact of financial record-keeping, which includes purchase, sales, and cash book keeping, on the sustainable growth of agribusiness enterprises in Rwanda. The study recommended Moozay's Dairy and Crop Farm Limited to provide periodic training and capacity building for staff to enhance cash bookkeeping practices in agribusinesses in Rwanda. Moozay’s Dairy and Crop Farm Ltd. is recommended to set policy action to enforce financial record keeping. Additionally, lawmakers should pass and strictly enforce a law on financial records in agribusinesses and other businesses. Moozay’s Dairy and Crop Farm Ltd. should be highly concerned about purchase bookkeeping as one of the tools to improve and increase its financial performance.

Does the recoupling of dairy and crop production via cooperation between farms generate environmental benefits? A case-study approach in Europe

Growing environmental concerns have prompted governments to make sustainable choices in agricultural resource use. Evaluating the sustainability of agricultural systems is a key issue for the implementation of policies and practices aimed at revealing sustainability. This study aimed to evaluate the performance of Norwegian dairy farms, accounting for marginal effects of environmental (exogenous) variables. We adopted the dynamic parametric approach within the input distance function framework to estimate the performance of Norwegian dairy farms, focusing on the technical efficiency and determinates. For comparison, we also estimated the static parametric model, which was used by previous studies. We used unbalanced farm-level panel data for the period 2000–2018. The result shows a mean technical efficiency score of 0.92 for the dynamic model and 0.87 for the static models. The empirical result shows that the previous studies that focused on the static model reported a biased result on the performance of dairy farms. The dynamic efficiency score suggests that Norwegian dairy farms can reduce the input requirement of producing the average output by 8% if the operation becomes technically efficient. The environmental variables have a different effect on the performance of the farmers; thus, policymakers need to place special focus on these variables for the sustainable development of the dairy sector.

Background: Full consideration of the performance and efficiency of hospital costs necessitates the application of economic analysis techniques. The aim of this study was to assess the efficiency of hospitals in Kermanshah University of Medical Sciences through Stochastic Frontier Analysis (SFA) method. Methods: The performance of Kermanshah hospitals (n=7) was assessed and analyzed by Stochastic Frontier Analysis (SFA) method during 2005-2011. Inpatient admission was considered as the output variable, while the number of medical doctors, nursing staff other personnel, active beds and outpatient admission were considered as the input variables. Frontier 4.1 software was used to analyze the data. Results: Based on the results of performance evaluation using Cobb-Douglas production function, the mean efficiency score of the hospitals in the SFA method was 0.63. Also, the efficiency capacity in these hospitals could be promoted up to 37 percent. Conclusion: Based on the results of Stochastic Frontier Analysis, downsizing the manpower in hospitals plays a major role in reducing hospital costs and improving their performance. Finally, it is necessary to investigate the effect of factors such as quality of service and patient satisfaction on hospital performance.

BACKGROUND:Given that the need to pay attention to measuring efficiency is considered as one of the main pillars of improving the level of efficiency in hospitals, so this study was carried out aimed to determine the mean technical efficiency (The technical efficiency is bound by zero and one and a score of less than one means that the theatre is inefficient as it could) score in terms of type and activity of the hospital, input-oriented and output-oriented attitude, returns to scale (In economics, returns to scale and economies of scale are related but different concepts that describe what happens as the scale of production increases in the long run, when all input levels including physical capital usage are variable (chosen by the firm). The concept of returns to scale arises in the context of a firm's production function. It explains the behavior of the rate of increase) in hospitals of Iran using data envelopment analysis (DEA) (DEA is a nonparametric method in operations' research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision-making units) and stochastic frontier analysis (SFA) (SFA is a method of economic modeling. It has its starting point in the stochastic production frontier models simultaneously introduced by Aigner, Lovell and Schmidt[1977] and Meeusen and Van den Broeck[1977] MATERIALS AND METHODS:The present study was carried out with a systematic review of all studies conducted on measuring efficiency of hospitals in Iran from March 21, 2001 to December 21, 2017 using DEA and SFA. Eleven databases were searched using appropriate keywords and 470 articles were found and evaluated using a checklist, and finally, 24 articles were entered into the meta-analysis process. Meta-analysis was performed using random effect model and fixed-effect model, and study heterogeneity was investigated using Q-Cochran test and I2 index. Furthermore, the main reasons of study heterogeneity were identified due to meta-regression.RESULTS:The average technical efficiency score of hospitals using DEA and SFA method was obtained equal to 0.885 and 0.809, respectively. Furthermore, with regard to the DEA method, 0.885, 0.891.0.952 and 0.913 was obtained for input-oriented and output-oriented, general and specialized care hospitals and constant returns respectively. With regard to SFA method, 0.733, 0.664, 0.641, 0.802, was obtained, and the inputs and outputs affect measuring the efficiency.DISCUSSION:In contrast, the DEA method can investigate several input and output simultaneously and is used as an effective and flexible tool in order to measure the efficiency of the hospital. DEA can be easily used for calculating efficiency scores based on the proper selection of input and output indicators. The data envelopment analysis method and different input and output variables have been used in most studies conducted in Iran, and Stochastic Frontier Analysis has been less considered. In the present study, the DEA method in governmental educational hospitals showed a higher efficiency than SFA method in the hospitals under study. But in general, due to lack of optimal efficiency level in the hospital, it is suggested that policymakers determine the hospital efficiency indices in order to evaluate their efficiency from different dimensions.CONCLUSION:The average technical efficiency score of hospitals using DEA and SFA method was obtained equal to 0.885 and 0.809, respectively. Also, the mean technical efficiency score in terms of input-oriented and output-oriented, general and specialized care hospitals and constant returns to scale using the DEA method was obtained equal to 0.885, 0.891.0.952 and 0.913 and using the SFA method, respectively, it was equal to 0.733, 0.664, 0.641, 0.802, and the inputs and outputs affecting measuring the efficiency. There is no significant difference between the mean efficiency score between the two methods, but the data envelopment analysis method is used more. It is suggested that the hospitals efficiency indicators to be determined in order to more accurately evaluate the hospitals efficiency.

This study focuses on comparing the technical efficiency of rice farms in two locations with different level of urbanization. A production function using maximum likelihood method is estimated and efficiency score of individual household is calculated using stochastic frontier analysis. The efficiency scores are regressed on variables including farm-household characteristics and degree of output market commercialization. The empirical evidence suggests that the elasticity of production to land size and biological inputs like chemical fertilizer, pesticide, fungicide and seed is positive and statistically significant. The average efficiency scores in two sample districts indicate that the production can be increased by 26-33% through improving efficiency in a given technological condition. The result suggests that the degree of commercialization has positive effect on technical efficiency. Furthermore, household characteristics like education, age, share of agriculture in total household income, sharecropping also have a significant effect on technical efficiency.