Optimizing contract allocation for risky conservation tenders

In the face of a shrinking budget for environmental activities, conservation agencies must design and implement agri-environmental policies that cost-effectively meet the environmental objectives. However, designing such programs is often challenging due to different uncertainties. For example, landholders may be exposed to risks when carrying out conservation projects. To minimise the negative impact of unexpected losses, landholders may require additional financial incentives as compensation for undertaking “risky” conservation projects. In such situations, the conservation agency risks over-spending public funds because of prohibitively high opportunity costs from landholders or failing to meet the environmental target. We used analytical and simulation approaches to explore optimal budget allocation in a target-constrained conservation tender. We also compared the performance of the tender with and without own-cost uncertainty. Results showed that as landholders’ own-cost uncertainty rises, the conservation agency is forced to allocate more funding to secure the same level of the environmental target. We found that the optimal funding level is sensitive to landholders’ competition uncertainty and the magnitude of expected losses

The influence of a conservation insurance mechanism on optimal bidding in risky conservation auctions

Conservation auctions or tenders (CTs) are gaining popularity globally due to their ability to generate efficiency gains with limited public funding. However, the existence of various types of uncertainties, in particular, the cost of delivering environmental goods (own-cost uncertainties), can undermine the attractiveness of CTs as a conservation policy instrument. This paper uses the optimal bidding model to examine the bidding behaviour of risk-neutral and risk-averse bidders. We incorporate a security loading factor, which reflects a bidder's confidence in the data used to estimate the expected profit. The factor relates to the risk of facing the winner’s curse, that is, win the auction but experience higher than predicted cost. We then explore the impact of introducing an embedded insurance (a conservation insurance mechanism that is included in a successful conservation contract) on the expected profit as well as bidders' optimal bidding behaviour. We find that in the presence of own-cost uncertainty, the optimal bid rises due to the increased cost of participating in the auction. This is true for both risk-neutral and risk-averse bidders. However, in the presence of a conservation insurance mechanism the optimal bid reduces

The socio-economic impact of fungicide resistance in West Australia's Wheatbelt

Farming is a risky business, demanding daily decisions on farm input expenditure and best practices while operating in an uncertain climate. One of these decisions regards agro-chemical inputs for disease control, a decision increasingly challenged by fungicide resistance for many pathogens of agricultural significance. To understand disease management decision-making and the importance of fungicide resistance, we surveyed 137 barley growers from West Australia's Wheatbelt. On average, this group spent AU$42/ha on fungicide application. Our survey found that growers were willing to invest an additional AU$18/ha to delay resistance of the pathogen to fungicides. Qualitative data show that barley growers perceive fungicide resistance as a growing issue in the region with a significant economic and emotional impact. Growers also expressed concern that fungicide resistance could become a long-term threat to the sustainability of their agribusiness. This study demonstrates that understanding growers' financial motivations and the economics of plant diseases is vital

Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed