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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.