Diversification has been increasingly recognized as a rewarding farm strategy through which farmers produce on-farm non-agricultural goods and services. In doing so, farmers employ farm inputs (capital, labor, and land) in products other than agricultural goods, with the aim to sell them in the market and increase their income. While a significant body of literature has explored the drivers affecting the adoption of diversification activities, so far little attention has been given to the impact of such adoption on the technical and financial performance of farms. This article intends to provide empirical evidence on the impact of on-farm non-agricultural diversification on the financial performance of family farms in Italy, by using a nation-wide sample of agricultural holdings based on the Farm Accountancy Data Network (FADN) data. We estimated a fixed effects-instrumental variable panel model to deal with two potential sources of bias: self-selection in the diversification strategy and simultaneity, due to the fact that farmers often decide to diversify with outcome expectations in mind. Our findings show that in Italy the diversification strategy has a positive impact on the financial performance of family farms, which is second in magnitude only to that of land growth strategy. Our results also confirm the positive impact of efficiency and clarify that education has a positive return to investment when it is specialized in agriculture.

We exploit the information derived from geographical coordinates to endogenously identify spatial regimes in technologies that are the result of a variety of complex, dynamic interactions among site-specific environmental variables and farmer decision making about technology, which are often not observed at the farm level. Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can produce model misspecification and preclude causal inference. In this article, we adopt a two-step procedure to deal with unobserved spatial heterogeneity, while accounting for spatial dependence in a cross-sectional setting. The first step of the procedure takes explicitly unobserved spatial heterogeneity into account to endogenously identify subsets of farms that follow a similar local production econometric model, i.e. spatial production regimes. The second step consists in the specification of a spatial autoregressive model with autoregressive disturbances and spatial regimes. The method is applied to two regional samples of olive growing farms in Italy. The main finding is that the identification of spatial regimes can help drawing a more detailed picture of the production environment and provide more accurate information to guide extension services and policy makers. Keywords Unobserved heterogeneity • Spatial dependence • Cobb-Douglas production function • Olive production JEL codes D24 • C14 • Q12

The objective of this paper is to provide an empirical investigation into the decision to participate in rural landscape conservation schemes in Italy. Although the high emphasis given to this issue and the increasing resources devoted to the landscape conservation schemes in the Rural Development Programmes (RDP) implemented by the Italian regions, farmers' participation is still very low. A better understanding of what motivates farmers to participate may help to increase adoption of the scheme and the effectiveness of the scheme design itself. In this paper we use data from 2149 household farms located in three Italian northern regions-Alto Adige, Lombardy and Piedmont-extracted from the Farm Accountancy Data Network sample, to estimate a discrete choice model aimed at identifying the variables that affect the probability of participating in a landscape conservation scheme. The model results indicate that participation correlates to farmer income. In addition to this, the probability is influenced by farm characteristics; mainly the use of organic farming practices, specialisation in livestock production and location of the farm in mountain areas.

Summary Until the end of the 20th century, farming in Greece, Italy and Portugal was characterised by inflexible land and labour markets and thus had persistently high proportions of small farms and aged holders. Over time, several policy interventions in these countries have aimed at increasing average farm size. Nevertheless, small farms still account for a very large percentage of all holdings, due to institutional, social and market factors. Small farms are mainly concentrated in two kinds of area. First, mountainous and economically depressed inland regions where outmigration has often resulted more in farmland abandonment rather than in land concentration. Second, peri‐urban areas and other economically diversified rural contexts where many small farms survive thanks to the adoption of household strategies based on pluriactivity and outsourcing. Although it is still difficult to predict the impact of the current recession on small farms, it is possible to distinguish two emerging trajectories. In one, reduced chances of off‐farm work may challenge the resilience of pluriactive small farms. Former pluriactive farmers may focus on scale economies in order to increase their farming incomes. On the other, small‐scale farming is increasingly relied on for household budgets and food security, and could cause a ‘return to the land’.

Most farms are family business, both in developed and developing countries. Labour allocation choices of farm household members are therefore relevant both for production choices in the farm and for rural labour markets. In particular, off-farm work and combination of on-and off-farm work (pluriactivity) are viewed as an efficient allocation of household labour resources. Moreover, labour choice of the children of the farm household are relevant for farm succession. In this paper we extend previous literature by estimating in an unified framework labour participation choices both for on-and off-farm work for operators, spouses, and their eldest children in working age, using a five-equation multivariate probit.