The unpredictable shifts in climatic patterns and limited adaptive capacity are adversely affecting agriculture and, in turn, the livelihoods of the Ugandan population (UBOS, 2024). To this end, the Government of Uganda, through the Ministry of Agriculture, working with other ministries, including the Ministries of Water and Environment, Energy, and Finance, has promoted the uptake of Climate-Smart Agriculture (CSA) (Zizinga et al., 2022).

We estimate the implicit market price of soil erosion using quarterly data of 2824 agricultural farms traded in Uruguay between 2000 and 2014. A unique feature of our estimation is that we allow for possible spatial spillovers. We find evidence of a negative and statistically significant association between erosion and land values. On average, an additional loss of 1% of the original topsoil due to erosion is associated with a direct (own) decrease of .24% in the per‐hectare price of agricultural land (P‐value: .012, 95% CI: −.0042, −.0005).

Climate change is one of the most relevant sources of uncertainty in present times, imposing high costs on different societal actors, including firms and organizations. Here, we analyze how manufacturing firms could adapt to climate-related stressors, specifically the risk associated with precipitation anomalies faced by firms. We are interested in the strategic adaptation responses related to the water source selection and the performance impacts of these responses. Additionally, we seek to investigate the extent to which the firms' size moderates these relationships.

Establishing a clear link between prices of conventional and niche product versions can help address challenges associated with anticipating these prices and attenuate concerns related to thin market conditions. In that regard, we analyze the price dynamics, differentiating between agri-product quality and marketing channels. Special attention is devoted to exploring how this price relationship varies when affected by energy or climate-related shocks. Our methodology follows a panel vector autoregression model (PVAR), which we apply to 23 local market prices in Chile for 2014.M10–2019.M12.