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The report examines the gap between soybean production distribution and rural insurance coverage in Brazil, highlighting how climate change—manifesting as severe droughts, hail, and frost—has led to unprecedented insurance claim payments. Using LASSO machine learning models, researchers found that while insurance data is insufficient to predict losses in the North and Northeast regions, agricultural yield data from PAM/IBGE can effectively identify climate variables (precipitation, fire risk, and wind) that correlate with losses. The authors recommend expanding insurance supply to vulnerable regions like MATOPIBA, utilizing production history for pricing in underserved areas, and strengthening the reinsurance market and public-private funds to ensure insurer liquidity.

The Ceres2030 project identifies a funding gap of USD 33 billion per year to achieve SDG 2 targets, with USD 14 billion coming from donors and USD 19 billion from recipient countries. The report emphasizes that most existing agricultural research ignores the needs of smallholders, who represent over 80% of the world's farms. Key recommendations include investing in extension services, climate-resilient crops, social protection, and supporting small and medium enterprises (SMEs) in the value chain. The project utilizes a complex general equilibrium model and machine-learning-assisted evidence synthesis to identify a portfolio of 14 policy instruments across three categories: empowering the excluded, on-farm interventions, and food on the move.

The report maps the 'online ecosystem' of the German far-right, identifying 379 channels across ten alternative platforms. It finds that while these platforms provide safe havens for extremist discourse, they generally have a much smaller reach than mainstream platforms. The study suggests that banning extremists from mainstream sites reduces their overall reach and does not lead to a direct mass migration to alternative platforms. It also highlights the role of 'alt-media' in amplifying far-right narratives, such as the 'great replacement' myth, which are rarely prominent in mainstream media.

The research examines how the effect of anomalous rainfall (excess and scarce) on violent, state-based conflict varies based on societal vulnerability factors. Using a causal forest model on global grid-cell data, the author finds that while rainfall variability generally has a small effect on conflict, this effect is context-specific. Scarce rainfall is more likely to increase conflict risk in communities with high agricultural employment and poor governance (low SHDI and GDP). Excess rainfall's effect is more complex, showing higher risk in areas with high population density, low globalization, low democracy, and low GDP, but contrary to expectations, it showed smaller effects in areas with high political exclusion or greater distance to the capital.