Weedy Finance: Weather Insurance and Parametric Life on Unstable Grounds

Based in the agrarian worlds of commercial sesame farming in northern Paraguay, where insurance companies are now selling weather derivatives to poor farmers, this article tracks financial practices that depend less on the healthy crops and more on the weeds that thrive among the profitable plants. Parametric insurance operates like a derivative and is triggered by certain weather conditions, which raises questions about the limits of survivability for human-crop relations. I sketch out a series of concerns about weeds as an entry point and helpful heuristic for multiple overlapping kinds of speculation in a multispecies, capitalist, and troubled landscape. By gridding the world to a limited set of expedient parameters, what generative social and human grounds do we lose in the process? A speculative anthropological imaginary might posit “weedy finance” as a critical standpoint and set of political claims for casting climate-based finance as one of the lively systems that can and should be intentionally and selectively weeded out.

Formulation of weekly and monthly thumb rule models for prediction of potential productivity of sunflower in Punjab

A study was conducted to evaluate the effect of meteorological parameters on Sunflower crop by analyzing meteorological and crop data (2003-2017) for three locations (Ludhiana, Ballowal Saunkhari and Amritsar) and to develop weather based “Weekly and Monthly Thumb Rule Models” for predicting the potential yield of sunflower crop in Punjab. These climatic normals were used for comparing the actual data to evaluate the effect of meteorological parameters on the yield of sunflower. In Punjab, ideally humid (maximum relative humidity between 77% - 94%) weather from mid-February to mid-March is favourable for optimum growth and development of vegetative stage in crop. The warm temperature (>35 ºC) during the seed development period after the flowering stage of sunflower is favourable for seed yield. However, heavy rainfall in the months of April and May with cloudy weather (sunshine hour < 9.2 hour) are not favourable for its productivity. The actual meteorological data of high yield crop years over the past 15 years were analyzed for different growth stages of sunflower to work out the critical ranges of meteorological parameters. Weather based “Thumb Rule Models” using the weekly and monthly meteorological data for different growth stages were formulated for use in developing the crop weather insurance term sheets and also predicting the potential yield of sunflower crop.

Farm Weather Insurance Assessment

The pilot aimed to develop services supporting both the risk and the damage assessment in the agro-insurance domain. It is based on the use of remotely sensed data, integrated with meteorological data, and adopts machine learning and artificial intelligence tools. Netherlands and Greece have been selected as pilot areas . In the Netherlands, the pilot was focused on potato crops for the identification of areas with higher risk, based on the historical analysis of heavy rains. In addition, it covered automated detection of potato parcels with anomalous behaviours (damage assessment) from satellite data, meteorological parameters and soil characteristics. In Greece, the pilot worked with 7 annual crops of high economic interest to the national agricultural sector. The crops have been modelled exploiting the last 3-year NDVI measurements to identify their deviations from the normal crop health behaviour for an early identification of affected parcels in case of adverse events. The models were successfully tested on a flooding event that occurred in 2019 in the Komotini region. Even though the proposed methodologies should be tested over larger areas and compared against a larger validation dataset, the results already now demonstrate how to reduce the operating costs of damage assessors through a more precise and automatic risk assessment. Additionally, the identification of parameters that most affect the crop yield could transform the insurance industry through index-based solutions allowing to dramatically cut costs.

Subsidy Policies and Insurance Demand

Using data from a two-year pricing experiment, we study the impact of subsidy policies on weather insurance take-up. Results show that subsidies increase future insurance take-up through their influence on payout experiences. Exploring mechanisms of the payout effect, we find that for households that randomly benefited from financial education, receiving a payout provides a one-time learning experience that improves take-up permanently. In contrast, households with poor insurance knowledge continuously update take-up decisions based on recent experiences with disasters and payouts. Combining subsidy policies with financial education can thus be effective in promoting long-run insurance adoption. (JEL G22, G52, G53, Q54)