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L'estructura agrària de la comarca de Bages es pot caracteritzar pels següents trets: - Predomini d'una pagesia benestant, conseqüència d'un procés històric que tindria els seus orígens a I'edat mitjana i que tindria com a unitat d'explotació el mas, entorn del qual es concretarien una bona part de les relacions socials amb la resta de la pagesia. - Aquesta accediria a la terra de dues maneres: en el marc de la comunitat rural com a propietari d'una petita parcel·la d'hort i de cereal de seca i accedint, mitjançant contracte de conreu, a les terres dels masos. El més freqüent era la rabassa morta i, en algunes ocasions, la masoveria. Els jornalers agrícoles sense terres eren pràcticament inexistents. Només els mossos tenen una certa importància, però vivien i eren alimentats al mas, a canvi del seu jornal...

Businesses, researchers, and policymakers in the agricultural and food sector regularly make use of large public, private, and administrative datasets for prediction, including forecasting, public policy targeting, and management research. Machine learning has the potential to substantially improve prediction with these datasets. In this study we demonstrate and evaluate several machine learning models for predicting demand for new credit with the 2014 Agricultural Resource Management Survey. Many, but not all, of the machine learning models used are shown to have stronger predictive power than standard econometric approaches. We provide a cost based model evaluation approach for managers to analyze returns to machine learning methods relative to standard econometric approaches. While there are potentially significant returns to machine learning methods, research objectives and firm-level costs are important considerations that in some cases may favor standard econometric approaches.

This report presents analysis of farm financial performance, debt, equity, capital, investment and physical characteristics of Australian dairy farms from 2017–18 to 2019–20. The report provides more detailed description at industry and regional level of the results from the ABARES annual Australian Agricultural and Grazing Industries Survey (AAGIS) that were released in March at Outlook 2020. Given the variation in seasonal conditions across regions, industry averages at state and national levels mask significant differences in performance at a regional level. Key issues Average farm cash income of Australian dairy farms is projected to have increased by around 37% to $165,000 per farm in 2019–20, following a sharp decline in 2018–19. The projected increase in income is a result of higher milk prices, reduced quantities of fodder purchased and lower feed prices, despite persistent drought in some regions. When adjusted for changes in area operated, average farm cash income per hectare in 2019–20 is projected to have been 21% above the average since 2010–11. The average rate of return is projected to have been around 1.7% in 2019–20. An estimated 72% of dairy farms recorded a positive rate of return (excluding capital appreciation) in 2019–20 and an estimated 32% of farms earned rates of return greater than 5%.

The aim of the research was to assess economic results and production efficiency in farms specializing in field crops classified by economic size in five EU countries: Poland, Bulgaria, Romania, Lithuania and Hungary. The analysis utilized the average two-year FADN EU data, from 2015-2016. The profitability of land, production efficiency, and farm debt were tested. On average, in the sample, the highest income without subsidies per 1 ha of UAA was obtained on Romanian farms, while on Bulgarian farms a loss was recorded. In Bulgarian farms from economic size classes 3-6 and Lithuanian from classes 1-3, the costs exceeded the value of production. A similar situation occurred in Hungarian and Polish farms from the sixth class of economic size. This means that production was economically ineffective. The debt of farms increased with the increase of economic size, but it did not exceed the limit value for which 50% is assumed. / Streszczenie. Celem badań była ocena wyników ekonomicznych i efektywności produkcji w gospodarstwach specjalizujących się w uprawach polowych sklasyfikowanych według wielkości ekonomicznej w pięciu krajach UE, tj. w Polsce, Bułgarii i Rumunii oraz na Litwie i Węgrzech. W badaniach wykorzystano średnie dwuletnie dane FADN EU, tzn. z lat 2015-2016. Zbadano dochodowość ziemi, efektywność produkcji oraz zadłużenie gospodarstw. Średnio w próbie najwyższy dochód bez dopłat na 1 ha UR uzyskano w gospodarstwach rumuńskich, natomiast w bułgarskich odnotowano stratę. W gospodarstwach bułgarskich z klas wielkości ekonomicznej 3-6 i litewskich z klas 1-3 koszty przewyższały wartość produkcji. Podobna sytuacja wystąpiła w gospodarstwach węgierskich i polskich z szóstej klasy wielkości ekonomicznej. Oznacza to, że produkcja była ekonomicznie nieefektywna. Zadłużenie gospodarstw zwiększało się wraz ze wzrostem wielkości ekonomicznej, jednak nie przekroczyło granicznej wartości, za którą przyjmuje się 50%., DOI 10.22630/PRS.2019.19.2.32

Many studies have used the U.S. Department of Agriculture’s (USDA) Agricultural Resource Management Survey (ARMS) to research various aspects involving the agricultural sector in the United States, including studies focused at the farm level. Since nonresponse and inaccurate reporting may cause significant bias in statistical analysis, research is conducted to estimate the magnitude of response error in the farm debt section of Phase III in ARMS. USDA Farm Service Agency (FSA) data on Farm Loan Program (FLP) borrowers with end of year debt are matched with ARMS respondents for years 2001, 2004, 2006, and 2007. A multinomial logit model is estimated to identify demographic, structural, and financial characteristics of FSA borrowers who refused to indicate if they had end of year farm debt, or who accurately or inaccurately classified their farm operations as having end of year farm debt on the ARMS. Additionally, estimates of the magnitude of response errors in ARMS for both FSA direct and guaranteed FLPs are estimated. The current study estimates that 12.9 percent of direct FLP respondents and 9.9% of guaranteed FLP respondents indicated “no” to the question on having end of year farm debt when they should have indicated “yes”. Also, those responding “no” are found to have their ARMS total debt outstanding less than their FSA total debt outstanding. Direct FLP operators are more likely to report “no” and, therefore, under-report end of year debt in the ARMS if they had a lower total FSA debt outstanding balance, had a greater value of crop production relative to total production, or had a lower gross cash farm income. Guaranteed FLP operators are more likely to under-report their debt in the ARMS if they had an operating line of credit loan, had a greater share of production from crops, or had a lower gross cash farm income. They are less likely to under-report their debt if they either had some college education, were eligible for socially disadvantaged loans, or were beginning farmer eligible. These results are of keen interest to those using ARMS data and those responsible for collecting ARMS data. The results allow researchers using ARMS data to appraise operator debt status to be better informed about potential data limitations. For example, estimates of the share of farm operations with little or no debt may be overestimated. Also, the results could assist the USDA National Agricultural Statistics Service in developing improved imputation techniques to estimate farm debt from the ARMS, especially for those respondents indicating “no” as to having end of year debt in the Farm Debt section of the ARMS.

Inter-annual climate variability in the Southeastern US that affects farm productivity and cash flows is largely dependent on the predictable El Nino Southern Oscillation (ENSO) phenomenon. In this paper, we estimate the association between the ENSO anomalies and the performance of agricultural loan portfolios of the Farm Credit System (FCS) institutions - the largest agricultural lender in this region. We find that, compared to neutral years, the share of delinquent loans in the FCS portfolio decreases by 1.5 to 2 percentage points following La Nina years and increases by 1.5 to 2 percentage points following El Nino years. These delinquencies are generally resolved because the impact on loan write-offs is much smaller, although statistically significant which suggests that the FCS institutions have well-diversified portfolios. The results also suggest that agricultural insurance markets are complementary to credit markets, that land values at loan origination have a positive impact on delinquencies, and that loan write-offs decrease with the lender’s size., IN: International Journal of Economics and Finance, 8(12), 238-249. doi:10.5539/ijef.v8n12p238.

Purpose – The paper considers how the Federal crop insurance program influences farm debt use, one of the key financial decisions made by farm operators. Design/methodology/approach – Using data from the nationally-representative Agricultural Resource Management Survey, the paper implements a propensity score matching model of the impact of Federal crop insurance participation on various measures of farm business debt use. To account for the simultaneity of financial decisions, the paper further tests this relationship using a seemingly unrelated regression model. Findings – Federal crop insurance participation is associated with an increase in use of short term farm debt, but not long term debt, consistent with risk balancing behavior and current trends in the farm sector. Research limitations/implications –In addition to risk balancing, the results are also consistent with credit constraints or lender preferences. The paper cannot fully establish causality between crop insurance participation and short term debt levels. Future research should address these limitations. Practical implications – Agricultural lending standards are generally conservative and the farm sector as a whole currently has historically low leverage, which implies that an increase in debt use may not be a threat to the financial health of the farm sector. Social implications – The results indicate that the reduction in total risk facing the farm sector is significantly less than the decline in risk provided by Federal crop insurance, which is an important consideration for policymakers. Originality/value – This is the first paper to use an econometric model to analyse the relationship between federal crop insurance and farm debt use decisions. This paper can inform future research on the Federal crop insurance program and farm financial decisions.

The US Department of Agriculture (USDA), through its ARMS, collects detailed information from farm operators on debt used in the farm business. Specific loan characteristics such as interest rate, loan term, origination date, type of loan, loan purpose, and type of financing are collected for up to the five largest loans. This information is used to construct portions of the farm sector balance sheet in addition to supporting research on credit use, farm solvency, and debt repayment capacity. Valid estimation and inferences are critical to the generation of this data, however, because of sensitivity, is subject to nonresponse or "do not know." Ignoring item nonresponse completely, by setting all missing values to zero or by taking into account only the existing answers will result in a bias. Imputation, the practice of filling in missing data with plausible values, can mitigate this bias. This analysis examines the use of multivariate techniques for debt component imputation. This would be an improvement over the generalized mean imputation approach used in ARMS and for many of the debt components the first attempt at imputation.

The objectives of this paper are to examine the debt maturity structure of farm businesses and to investigate the determinants of the farm businesses’ choice of the maturity structure. Based on 2008 ARMS data, our findings indicate that size, asset maturity, financial risk, region, and type of farm operation influenced the debt maturity structure in 2008.

This paper uses a double hurdle model to help explain one aspect of the changing capital structure of U.S. production agriculture--the increase in the number of debt free farms. Our findings suggest that nonfinancial factors, such as operator age, region, risk aversion, and financial factors such as debt service ability and the cost of capital play significant roles in distinguishing borrowers from non borrowers.

This research provides the first analysis of the relationship between farm financial exposure and technical efficiency in the Pangasius farming in An Giang province, in the Mekong Delta of Vietnam. A nonparametric DEA approach has been applied to estimate technical and scale efficiency scores of 61 Pangasius farms in An Giang province in the year 2008. The mean technical efficiencies under assumption of constant returns to scale and variable returns to scale and scale efficiency were measured to be 0.595, 1.058 and 0.58 respectively. The decomposition of the technical efficiency measure shows that scale inefficiency is the primary cause of technical inefficiency in the the case of Pangasius farming as about 92% of the sample Pangasius farms exhibits increasing returns to scale (IRS). Then, estimated technical efficiency (TE) scores under assumption of variable returns to scale are used in a regression analysis to investigate the relationship between the efficiency measures and different farm characteristics, including financial considerations. Research results suggest that technical efficiency is influenced by investment level of farms as well as by farm operator's experience. The farms are invested more will be more efficient. The experience measured as the years of operator in farming Pangasius also suggests that the farmers having more experience may have better decisions in farm operating and more efficient in using inputs, thus, their farms are more efficient. Technical efficiency is positively influenced by the debt-to-asset ratio and also by the debt-to-equity ratio, while no statistically significant relationship is found between technical efficiency and the bank debt-to asset ratio. The other factors (age and education levels of the houshlod head) are found to have no effects on the technical efficiency in the sample farms.

Farms in Tajikistan currently face a severe debt crisis that has been caused by a combination of two factors typical of such situations in many countries: (a) the inability of the farms to make a profit under current conditions and (b) continued lending by the banks to cotton producers regardless of reduced payment capacity and lack of credit-worthiness. The paper traces the accumulation of farm debt in Tajikistan to pervasive government intervention in both financing and production decisions, which has led to soft budget constraints and moral hazard behavior. The purpose of the paper is to inform the debate around the issue of cotton farm debt in Tajikistan by studying the experience of other countries that had to contend with farm debt overhangs in the 1980s and the 1990s. Five CIS transition countries (Belarus, Kazakhstan, Moldova, Russia, and Ukraine) and one market economy (Israel) are studied using time series of aggregate financial reports of the farm sectors. The comparative analysis shows that the farm debt issue is not strictly a transition economy phenomenon. The problem can occur in market economies (e.g., Israel) if the state pursues policies directed toward the expansion of farm production without heed to creditworthiness of the farms and if the farm structure is incompatible with profitability and efficiency criteria. The basic reasons that led to debt accumulation in CIS and in Israel remain valid to this day, and the policy solutions implemented in these countries are relevant for Tajikistan.

Debt-for-nature swaps have been successfully applied in an international context to achieve nature conservation objectives in developing countries. The swap involves alleviating a country’s external debt burden in exchange for that country investing the equivalent amount of resources into specified nature conservation programs or activities. This paper explores to what extent the concept might be applicable and relevant in a domestic setting – by alleviating farm debt in return for on-farm conservation activities. The case for relevance of the instrument is argued on the basis of empirical data from a grazing region in northern Australia. Stakeholders and participants are identified for debt-for-conservation swaps and details for instrument design discussed. Preliminary results from a grazier survey and lessons from a similar incentive in the USA support a critique of the incentive instrument against a range of policy criteria.

Changes in commercial bank market shares of farm debt are decomposed into portfolio decisions loanable funds availability and loan market size for 64 counties in Arkansas from 1986 through 1990. A seemingly unrelated regression model is hypothesized to identify county characteristics that are related to changes in commercial bank market shares. Regression results indicate that county differences in economic activity, the relative risk associated with agriculture, farm structure and regional location contributed to changes in commercial bank market shares. The results imply a market niche for rural commercial banks emphasizing agricultural loans in the presence of unlimited branch banking.

Report Summary: Estimates of farm debt were lowered substantially by a revision of the I&O component of the nonreal estate debt series. The estimate for January 1, 1975, of I&O debt was revised downward by $10.6 billion, from $16.7 billion to $6.1 billion. Total nonreal estate debt on January 1, 1975, was estimated at $35.2 billion, and with real estate debt unchanged at $46.3 billion, total farm debt was estimated at $81.5 billion; previously it had been $92.2 billion. The I&O sources include mainly retail sellers of farm machinery and other products, individuals, farmer cooperatives, and institutional lenders not elsewhere classified. On the revised basis, I&O debt on January 1, 1976, is estimated at $6.4 billion and total nonreal estate debt at $39.4 billion; with real estate debt of $50.9 billion, total farm debt, excluding Commodity Credit loans, is estimated at $90.3 billion. The I&O revision followed appraisal of information provided by two Bureau of the Census surveys of farm debt—as of January 1, 1971, and June 30, 1966. The I&O figure had not been revised since the establishment of a benchmark estimate for January 1, 1961, based on the Bureau of the Census debt survey of that date. Review of the 1966 survey data indicated the Census I&O figures were not greatly different from existing USDA estimates and no benchmark revision was made at that time. However, the 1971 Census survey showed that much larger differences had developed. Thus, benchmark revisions were made for January 1, 1971, and January 1, 1967, and changes were prorated back to 1961. Estimates are quite tentative for years after 1971 and will remain so until results of the 1974 Census of Agriculture become available and are analyzed. Figures for 1971 and 1975 are shown in table 1.

A single equation model for explaining annual changes in non-real estate farm debt held by major institutional lenders is presented. Gross cash outlays, informational funds, the labor-capital price ratio and the interstate rate are used to explain changes in outstanding non real estate debt held by commercial banks PCA's and the FHA. Debt held by commercial banks was found to be responsive to changes in the relative cost of loan funds.

Farm assets totaled $311.4 billion on January 1, 1970, while farm debt outstanding reached $58.1 billion leaving farm proprietors' equities of $253.3 billion. The ratio of farm debt to value of assets was 18.7 percent, up 0.4 point from a year earlier. Advances in the value of farm real estate made up half of 1969's $12.3 billion gain in farm asset value. Higher values for livestock and poultry mainly accounted for the rest of asset growth. Farm non-real estate debt grew $2.2 billion during 1969 compared with $1.3 billion for farm real estate debt, the latter figure indicating farmers’ reluctance to enter long-term loan contracts at the high interest rates prevailing last year. Regionally, the Corn Belt had the highest value of farm assets but farm debt was also highest there. As a group, farmers who had gross sales of $20,000 to $39,999 during 1969 owned the largest share of farm assets and owed the largest proportion of farm debt.