This dataset provides seasonally estimated monthly costs of healthy and sustainable diets for five demographic profiles: infants, adolescents, adult women, adult men, and the elderly. The estimates cover all Italian provinces across 12 seasonal periods from 2021 to 2024. Food baskets are based on nationally recommended nutritional guidelines that are specific to age, gender, and season. They include 167 food items, ranging from fresh produce to processed foods. Costs are calculated by matching these dietary requirements with official provincial-level food price data from the Osservatorio Prezzi e Tariffe. Missing prices in provinces not covered by the survey are imputed using a spatial model that accounts for neighboring prices, local income levels, and seasonal variation. For each basket, the dataset reports minimum, average, and maximum monthly costs, depending on the variation in item-level prices. This dataset allows for spatial and temporal analysis of the affordability of healthy diets and supports applications in public health, food policy, and targeted support for vulnerable populations across Italian provinces.

This study examines the economic dimension of food poverty by developing a replicable methodology for estimating the cost of following Healthy and Sustainable Diet (HSD) patterns in Italy. Using a novel dataset constructed via web scraping from the Osservatorio Prezzi e Tariffe, we calculate diet costs for different population groups in line with national nutritional guidelines. Detailed provincial price information enables us to assess local disparities in food affordability, and missing data are handled through an imputation strategy that exploits spatial and temporal correlations. The results highlight substantial variation in HSD costs across provinces, revealing key implications for food poverty and social inequality. The study contributes a methodological framework for estimating food costs with publicly available data and offers actionable insights for policymakers committed to expanding access to nutritious and environmentally sustainable diets.

Food poverty remains a critical lens for assessing economic and social inequalities, particularly regarding access to nutritionally adequate and sustainable diets. This chapter introduces a methodology for defining and quantifying the cost of a healthy and sustainable diet by combining scientific dietary guidelines with price data analysis. We design dietary plans based on systematic reviews and meta-analyses that link food consumption to chronic disease risk, and we estimate their costs using web-scraped price data from the Italian Osservatorio Prezzi e Tariffe. The approach provides clear insights into the affordability of recommended diets across population groups and informs policy debates on food security and economic accessibility.

Encouraging farmers to adopt environmentally friendly technologies through social learning and agricultural extension helps overcome the bottleneck created by slow diffusion. Expanding on previous adoption studies, this paper takes a dynamic perspective on how social learning and both traditional and new extension channels influence the uptake of fertigation technology. We build a general analytical framework and show that the primary information channels—social learning and extension services—shorten the duration from awareness to adoption. Using survey data and a discrete-time cloglog model, we confirm the complementary effects between social learning and extension approaches. Heterogeneity analysis reveals larger marginal impacts for younger and middle-aged farmers, those with middle to high education, and farms operating above the median land scale. The findings provide new empirical evidence on technology adoption in the internet era and offer guidance for designing complementary, collaborative, and efficient agricultural service systems.

Using field survey data from 443 family farms in Shandong and Henan Provinces, we measure adoption of green control techniques (GCTs) as a binary choice and assess spatial dependence in these decisions. After conducting a global Moran's I test, we estimate a Bayesian spatial Durbin probit model with the appropriate spatial weight matrix and decompose direct and spillover effects via partial derivatives. GCT adoption is spatially correlated among neighboring farms, with the strongest link appearing within 2.0 km. Farm leaders’ education, risk preferences, financial status, labor availability, understanding of GCTs and pesticide risks, awareness of other adopters, frequency of neighbor communication, participation in technical training, and media exposure all increase the likelihood of adoption, largely through direct effects. Nevertheless, spillovers from neighboring farms' training participation, labor force, and financial health are also meaningful. These findings provide theoretical support for demonstrating and scaling GCTs and help identify the most suitable model households.

Green control techniques support cleaner farm production and protect ecological systems, yet adoption remains limited because farmers are risk-averse. Using an experimental economics approach applied to survey data from 385 vegetable growers in Shandong Province, we measure individual risk preferences and estimate an endogenous switching probit model to study how risk aversion and cooperative membership influence GCT adoption. We also test whether cooperatives mitigate the dampening effect of risk aversion. Risk aversion increases the likelihood of cooperative membership but reduces the probability of adopting GCTs, whereas cooperative participation both promotes adoption and attenuates the negative impact of risk aversion. To expand cleaner production, policymakers should lower the perceived risk of GCT adoption, strengthen cooperatives, and improve the institutional environment that supports farmers' adoption decisions.

The spread of internet technologies and mobile devices has enabled an emerging agricultural extension model that relies on new media channels such as WeChat accounts and dedicated apps. Empirical evidence on the effectiveness of this model remains scarce, so we analyze survey data from 759 peasant households in Shandong and Henan Provinces to measure adoption of soil fertilization, water-saving irrigation, and green pest-control technologies. Propensity score matching and instrumental-variable approaches allow us to examine the direct, spillover, and distributional effects of the new extension mode on farmers’ technology adoption decisions. We find that the model raises adoption levels and generates partial spillover effects, with benefits varying by age group and farm size. Effective promotion should therefore emphasize information diffusion among early adopters and ensure that elderly and small-scale farmers receive tailored support.

Under China's Three Rights Separation Policy, land tenure security hinges on the stability of land management rights. Using endogenous switching probit models and data from 443 family farms in Shandong and Henan Provinces, we study how social capital and tenure security shape the adoption of green control techniques (GCTs). We distinguish between embedded and disembedded social capital to test whether social capital amplifies the effect of secure land tenure on GCT adoption. Both social capital and tenure security have significant positive impacts, and social capital strengthens the influence of tenure security, although embedded and disembedded forms operate differently. The findings suggest policies should stabilize land management rights while cultivating farm-level social capital, particularly disembedded networks.

Using survey data from 375 family farms across five provinces of the Huang-Huai-Hai Plain, we evaluate welfare within a capability-approach framework and study how adopting green control techniques (GCTs) affects outcomes. Endogenous switching regression and multinomial treatment-effects models allow us to compare adopters and non-adopters while capturing the intensity and timing of adoption. Average treatment effects on welfare are significant for both adopters (0.084) and non-adopters (0.046), indicating that GCTs raise household welfare. Relative to non-adopters, farms with high and low adoption intensity experience welfare gains of 22.63% and 16.42%, while early and late adopters gain 5.87% and 7.57%, respectively. Intensive adoption thus delivers the largest welfare improvements, and late adopters eventually reach higher welfare levels.

Drawing on field survey data from 366 traditional households (THs) and 364 family farms (FFs) in the Huang-Huai-Hai Plain, we estimate discrete-time complementary log-log models to identify factors that shape the duration between awareness and adoption of green control techniques (GCTs). The duration is significantly shorter for FFs than for THs. Higher education, stronger risk tolerance, better household finances, positive perceptions of usefulness and ease of use, and proactive media and government extension support all reduce the time to adoption for both groups, while male household heads lengthen it. Age, farm size, and labor availability affect THs and FFs differently, underscoring the need for tailored extension strategies.