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This investigation considers factors affecting off‐farm investment of farm households. A national farm‐level survey was used to evaluate the effects of various farm and operator characteristics on the likelihood of off‐farm investment. Results suggest differences in level of education, age of the operator, off‐farm income, household net worth, leverage, farm size, farm diversification, management skills, and location influence off‐farm investment decisions.

The purpose of this paper is to estimate the impact of Internal Revenue Code cost recovery provisions – Section 179 and “bonus depreciation” – on farm capital investment.

The authors construct a synthetic panel of data consisting of cohorts of similar farms based on state and production specialization using the USDA’s Agricultural Resource Management Survey for years 1996-2012. Employing panel data methods, the authors are able to control for time-invariant fixed effects, as well as the effects of past investment on current investment.

The authors estimate statistically significant investment demand elasticities with respect to the Section 179 expensing deduction of between 0.28 and 0.50. A change in bonus depreciation, on average, had little impact on capital investment.

The estimates suggest there is a modest effect of the cost recovery provisions on investment overall, but a stronger effect on farms that have more than $10,000 in gross cash farm income. There are other implications for the agricultural sector: the provisions may encourage technology adoption with its associated benefits, such as reduced cost of production and improved conservation practices. On the other hand, the policy could contribute to the growing concentration in production as large commercial farms expand their operated acreage to take advantage of increasingly efficient physical capital.

To the authors’ knowledge, this is the first research to use a nationally representative dataset to estimate to impact of Section 179 and “bonus depreciation” on farm investment. The findings provide evidence of the provisions’ impact on farm capital purchases.

Off‐farm investment decisions of farm households are analyzed. Farm‐level data for a sample of Kansas farms observed from 1994 through 2000 are utilized. A system of censored dependent variable models is estimated to investigate the factors that influence the composition of farm households’ portfolios. The central question underlying the analysis is whether farm income variability influences off‐farm investment decisions. Previous analyses on the determinants of non‐farm investments have failed to consider the role of income variability. Results of this study indicate that higher farm income fluctuations increase the relevance of non‐farm assets in the farm household portfolio, thus suggesting these assets are used as farm household income risk management tools.

The purpose of this study is to explore the determinants of investment decisions of Estonian farms after the transition to market economy and accession to the European Union (EU), in the period 2006–2019.

The paper employs Estonian Farm Accountancy Data Network (FADN) individual farm-level data from the period 2006–2019, and standard and augmented accelerator investment models. Generalised methods of moments (GMM) and bias-corrected least-squares dummy variables (LSDVC) regressions were used to estimate parameters of these models.

In the considered period, farm investments were positively affected by sales growth, investment subsidies and the cash flow. Decomposition of cash flow into volatile, market income related part, and more stable, farm subsidies related part indicated that investments do not depend on market income part of cash flow. Instead, the stable part of the cash flow (farm subsidies) had a significant and positive effect on investments. This suggests that credit rationing could be present in the EU agriculture, and it depends on the farm subsidies not market income of farms.

Despite the wealth of literature on the investment behaviour of farmers, this article is the first attempt to decompose farm cash flow into stable (farm subsidies) and volatile (market income) parts to explain the role of subsidies as a part of cash flow in credit rationing.

The purpose of this paper is to determine if there are positive microeconomic effects from a state‐funded loan participation program on farm productivity and investment behavior.

The authors take the approach that access to credit solves a liquidity problem. If a credit constraint exists it results in a suboptimal allocation of resources and a reduction in farm output and profitability. A two‐stage regression model approach is used to analyze farmer survey and loan application data. In the first stage, a probit regression model is used to identify the farmers who are likely to be credit rationed. In the second stage, switching regression models are used to observe the effect of credit rationing on farm productivity and on farm investment behavior.

It is found that there are liquidity effects of credit constraints for a significant share of the beginning and low‐resource farmers who participated in the state‐funded farm loan program. After controlling for various farm and farmer characteristics, the estimated productivity and investment demand equations imply that a 1 percent increase in credit received by credit constrained farmers under the state program increased their gross income by about 0.49 percent, and their investments in depreciable assets by about 0.33 percent.

This paper is the first to apply the switching regression model to a state‐funded farm loan program for the purpose of evaluating the financial impacts on farmer participants.

The purpose of this paper is to examine the impact of changes in farm economic conditions and macroeconomic trends on US farm capital expenditures between 1996 and 2013.

A synthetic panel is constructed from Agricultural Resource Management Survey (ARMS) data. A dynamic system GMM regression model is estimated for farms as a whole and separately within farm typology categories. The use of farm typologies allows for comparison of the relative magnitudes of these estimates across farms by farm sales level and the operator’s primary occupation.

Changes in gross farm income levels, tax depreciation rates, and interest rates have a significant impact on crop farm investment, while changes in output prices, net cash farm income levels, tax depreciation rates, and farm specialization levels have significant impacts on livestock farm capital investment. The relative significance and magnitudes of these impacts differ within farm typologies. Significant differences include a greater responsiveness to change in tax policy variables for residential crop farms, greater responsiveness to changes in output prices and debt to asset ratios for intermediate livestock farms, and larger changes in commercial crop and livestock farm investment given equivalent changes in farm sales or the returns to investment.

These findings are of interest to agricultural economists when constructing farm investment models and employing pseudo panel methods, to those in the agricultural equipment and manufacturing sector when constructing models to manage inventories and plan for production needs across regions and over time, to those involved in drafting tax policy and evaluating the potential impacts of tax changes on agricultural investment, and for those in the agricultural lending sector when designing and executing agricultural capital lending programs.

This study uniquely identifies differences in the level of investment and the magnitude of investment responsiveness to changes in farm economic conditions and macroeconomic trends given differences in income levels and primary operator occupation. In addition, this study is one of the few which utilizes ARMS data to study farm capital investment. Utilizing ARMS data provides a rich panel data set, covering producers across many different crop production types and regions. Finally, employing pseudo panel construction methods contributes to efforts to effectively employ cross-sectional data and dynamic models to study farm behavior across time.

This chapter examines the involvement of finance companies in the purchasing and leasing of Australian farmlands. This is a new global phenomenon as, in past decades, finance companies have lent money to farmers, but have rarely sought to purchase land themselves. We investigate and discuss the activities of the Hancock company – an asset management firm that invested in farmland in northern NSW. Material on the activities of Hancock and other investment firms were obtained from documents on the public record, including newspaper reports. Semi-structured interviews with community members were conducted in the region of NSW where Hancock operated. Australian agriculture is being targeted for investment by companies in the finance industry – as part of a growing ‘financialization’ of farming. While it is financially beneficial for companies to invest, they do not do so in ‘empty spaces’ but in locations where people desire to live in a healthy environment. The Hancock company was criticized by community residents for failing to recognize the concerns of local people in pursuing its farming activities. To date, there have been few studies on the financialization of farming in Australia. By investigating the operations of the Hancock company we identify a number of concerns emerging, at the community level, about an overseas company running Australian-based farms.

The purpose of this paper is to examine the use of accelerated depreciation deductions, which includes Section 179 and bonus depreciation, taken in the first year of asset life by Michigan farms. The frequency, value and influence of accelerated depreciation on farm investment are also analyzed.

Accrual adjusted income statements, balance sheets, depreciation schedules, and income tax information for 66 Michigan farms from 2004 to 2014 provide data for the analysis. The present value of the accelerated deduction and change in the cost of capital were calculated. Finally, investment elasticities were used to arrive at the change in investment due to accelerated depreciation.

Accelerated depreciation was utilized across all applicable asset classes. Section 179 was used more often than bonus depreciation in part because it was available in all the examined years. Based on actual farm business use, accelerated depreciation lowered the cost of capital for the operations resulting in an estimated increase in investment of 0.27 to 11.6 percent depending on asset class.

The data utilized are of a detail not available in previous investigations which used either aggregate data or estimated rather than the observed use of accelerated depreciation. This analysis reveals that accelerated depreciation as used by commercial farms lowers the cost of capital and thus encourages investment particularly in machinery and equipment.

The problem of inaccessibility of finance for farm investment is a common phenomenon among farmers, especially the rural dwellers. Thus, there is a need to know how the accessibility of informal finance can be increased to increase farm investment. Therefore, this study evaluates farmers’ access to informal finance and its contribution to farm investment among rural farmers in Northcentral Nigeria.

A three-stage random sampling technique was employed to select 160 farmers. Primary data collected were analysed with descriptive statistics and the Heckman selection model.

The study revealed that cooperative society is the major informal means of loan acquisition used by the farmers followed by Rotational Savings and Credit Associations (RoSCAs). Informal loans contributed to agricultural investment through the various operational activities involved in production. Factors influencing farmers’ access to informal loans were the age, farm size and income of the farmers. Interest charged, farmers' age, farming experience, household size, education and loan duration were the drivers of the amount borrowed from the informal financing sector.

The findings of the study call for policies that will sustain informal financial institutions in developing economies, like Nigeria. Thus, the government through its regulatory agencies should assist informal finance providers with the necessary resources to achieve more goals. This is because the informal credit lenders help in bridging financial gaps created by formal financial institutions, such as commercial banks.

Unlike the previous research studies, this study investigated the driving factors of the amount borrowed from informal finance and its use in farm investment.

The paper examines the factors that influence the production of cash flows in a sample of Italian farm accountancy data network (FADN) farms to generate information useful for calibrating policies to support farmers' investments.

An econometric analysis on the sample estimates the influence of structural, economic, commercial and financial variables on CAFFE, i.e. the cash flow that includes the payments to the farmer's resources and the free cash flow on equity (FCFE). The econometric problem of endogeneity is treated by adopting the Hausman test to choose between fixed and random effects models. The results for Italian agriculture and its types of farming (TFs) are examined based on the FCFE/capital depreciation ratio, where FCFE subtracts from CAFFE the opportunity cost payments to the farmer's resources. This ratio identifies TFs with problems of sustainability of the production system.

The results show that increasing the productive dimension, in particular the endowment of farmland and working capital, is still essential to stimulate the production of cash flows of Italian agriculture. Without this growth, increasing the depreciable capital base is ineffective. FCFE does not compensate for depreciation in several TFs, which in various cases could also improve by improving economic efficiency and commercial position.

Assessing the factors that most influence cash flows can help to better calibrate rural development measures to the territories and farming types that most need public support. Our analysis procedure can be applied to all production systems equipped with farm accounting networks; however, the criteria for rewarding farmer resources and calculating the replacement value of agricultural capital need to be better discussed.

The specification of rural development policies rarely takes into account the financial sustainability conditions of farms, as well as the factors that determine them, in defining the support parameters and the selection criteria for funding. Our approach, based on the analysis of FADN data, considers these aspects and provides ideas for better calibrating public support for investments among agricultural territories, sectors and types of farms.