Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.

Hedging the Price Risk Inherent in Revenue Protection Insurance

Crop revenue insurance is unique, because it involves a guarantee subsuming yield risk and highly systematic price risk. This study examines whether crop insurers could use options instead of, or in addition to, assigning policies to the Commercial Funds of the USDA Federal Crop Insurance Corporation (FCIC) as per the Standard Reinsurance Agreement (SRA) to hedge the price risk of revenue insurance policies. The behavioral model examines the optimal hedge ratio for a crop insurer with a book of business consisting of corn Revenue Protection (RP) policies. Results show that a mix of put and call options can hedge the price risk of the RP policies. The higher optimal hedge ratios of call options as compared to put options imply that the risk of increased liability due to upside price risk can be hedged using options better than downside price risk. This study also analyzed the combination of options with the SRA at 35, 50, and 75% retention levels. The zero optimal hedge ratios at each retention level and the negative correlation between RP indemnities and the option returns when the crop insurer mixed options and SRA suggest that the purchasing of options provides no additional risk protection to crop insurers beyond what is provided by the SRA despite retention limits.

The effects of overconfident yield forecasting on cross-coverage level crop insurance demand

PurposeThis paper examines the effect of overconfident yield forecasting (optimism bias) on crop insurance coverage level choices across both yield and revenue insurance.Design/methodology/approachThis study simulates a representative producer’s preferred coverage level for both yield and revenue insurance under three potential models of decision-making and four potential manifestations of overconfident yield forecasting. The study then uses this framework to examine how coverage level choices change as overconfidence increases (decreases).FindingsAs overconfidence increases, producers prefer lower levels of crop insurance coverage than they would otherwise prefer, with extreme overconfidence inducing farmers to buy no insurance at all. While overconfidence affects cross-coverage demand for revenue and yield insurance similarly, this effect is more pronounced for yield insurance. Cross-coverage level demand for revenue insurance is relatively stable across changes in the correlation between prices and yields.Practical implicationsThis research has important implications for crop insurance subsidy design and crop insurance demand modeling.Originality/valueThere is a growing body of literature suggesting that producers are overconfident with regard to their future yield risk and that this bias reduces their willingness to pay for risk management tools such as crop insurance. This is the first study to look at how such overconfidence affects cross-coverage level demand for crop insurance.

Manajemen Risiko Pada Bank Pembangunan Daerah Jawa Barat dan Banten Tbk

This study aims to identify and analyze risk management in banking, namely risk, operational risk, market risk, liquidity risk, legal risk, compliance risk, yield risk and investment risk. And to analyze the financial ratio reports for the first quarter and second quarter of 2020 at the West Java and Banten (BJB) Tbk development bank. This research uses descriptive quantitative method at PT. West Java and Banten Regional Development Bank Tbk (BJB) Tbk which are listed on the Indonesia Stock Exchange during the period 2018 to 2020. The sample in this study is PT. West Java Regional Development Bank and Banten Tbk (BJB) which are registered on the IDX and are still operating in the 2018-2020 period. Data collection tools in this study are to use the method of observation on financial data at the bank and internet research. The results of this study indicate the existence of smooth and bad conditions in management analysis and financial ratio analysis at BJB banks which consist of analysis of CAR, non-performing assets, CKPN, gross NPF, NPF Net, ROA, ROE, NI, NOM, BOPO, and FDR. The results of the overall calculation at PT. West Java Regional Development Bank and Banten Tbk, in the 2018-2020 period experienced an increase and decrease every year.

The Total Return and Risk to Residential Real Estate

We estimate total returns to rental housing by studying over 170,000 hand-collected archival observations of prices and rents for individual houses in Paris (1809–1943) and Amsterdam (1900–1979). The annualized real total return, net of costs and taxes, is 4.0% for Paris and 4.8% for Amsterdam and entirely comes from rental yields. Our returns weakly correlate with the implied returns in Jorda et al. (2019) and are substantially lower. We decompose total return risk at the individual asset level and find that yield risk becomes an increasingly important component of property-level risk for longer investment horizons.

How will future climate depending agronomic management impact the yield risk of wheat cropping systems? A regional case study of Eastern Denmark

One of the major challenges in agriculture is how climate change influences crop production, for different environmental (soil type, topography, groundwater depth, etc.) and agronomic management conditions. Through systems modelling, this study aims to quantify the impact of future climate on yield risk of winter wheat for two common soil types of Eastern Denmark. The agro-ecosystem model DAISY was used to simulate arable, conventional cropping systems (CSs) and the study focused on the three main management factors: cropping sequence, usage of catch crops and cereal straw management. For the case region of Eastern Denmark, the future yield risk of wheat does not necessarily increase under climate change mainly due to lower water stress in the projections; rather, it depends on appropriate management and each CS design. Major management factors affecting the yield risk of wheat were N supply and the amount of organic material added during rotations. If a CS is characterized by straw removal and no catch crop within the rotation, an increased wheat yield risk must be expected in the future. In contrast, more favourable CSs, including catch crops and straw incorporation, maintain their capacity and result in a decreasing yield risk over time. Higher soil organic matter content, higher net nitrogen mineralization rate and higher soil organic nitrogen content were the main underlying causes for these positive effects. Furthermore, the simulation results showed better N recycling and reduced nitrate leaching for the more favourable CSs, which provide benefits for environment-friendly and sustainable crop production.

Coordination mechanisms of a three-layer supply chain under demand and supply risk uncertainties

This paper considers a newsvendor model for a single product to focus on the importance of coordination under demand and supply uncertainties where the raw materials are procured from two unreliable suppliers without any emergency resource; the main supplier (which is cheaper but more unreliable) is prone to random supply disruption and, therefore, it can satisfy all or nothing of the buyer's order, while the backup supplier (which is expensive but less unreliable) is prone to random yield and, therefore, can satisfy only a random fraction of the buyer's order. From the numerical results, we observe that it would be optimal to over-utilize the backup supplier and under-utilize the main supplier if the maximum growth in supply risk results from supply disruption. On the other hand, when the growth in supply risk occurs mainly due to increase in yield risk, the optimal risk mitigation strategy would be to increase the use of the backup supplier and decrease the use of the main supplier. We propose the price only contract and a new revenue sharing contract to mitigate demand and supply uncertainties in the decentralized model, and observe that the revenue sharing contract can fully coordinate the supply chain with win-win outcome for all entities involved in the supply chain.

Using insurance data to quantify the multidimensional impacts of warming temperatures on yield risk

Previous research predicts significant negative yield impacts from warming temperatures, but estimating the effects on yield risk and disentangling the relative causes of these losses remains challenging. Here we present new evidence on these issues by leveraging a unique publicly available dataset consisting of roughly 30,000 county-by-year observations on insurance-based measures of yield risk from 1989–2014 for U.S. corn and soybeans. Our results suggest that yield risk will increase in response to warmer temperatures, with a 1 °C increase associated with yield risk increases of approximately 32% and 11% for corn and soybeans, respectively. Using cause of loss information, we also find that additional losses under warming temperatures primarily result from additional reported occurrences of drought, with reported losses due to heat stress playing a smaller role. An implication of our findings is that the cost of purchasing crop insurance will increase for producers as a result of warming temperatures.