# Agricultural Supply Chain Financing Strategies under the Impact of Risk Attitudes

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

#### 2.1. Agricultural Product Supply Chain

#### 2.2. Supply Chain Finance

#### 2.3. Risk Measurement Method

## 3. Model Description and Assumptions

#### 3.1. Model Description

#### 3.2. Assumptions

## 4. Financing with Bank or E-Commerce

#### 4.1. Bank Financing Strategy (BF)

**Theorem**

**1.**

#### 4.2. E-Commerce Financing Strategy (EF)

**Theorem**

**2.**

**Corollary**

**1.**

**Corollary**

**2.**

**Corollary**

**3.**

#### 4.3. Comparative Analysis

**Corollary**

**4.**

**Corollary**

**5.**

**Corollary**

**6.**

## 5. Numerical Analysis

#### 5.1. Impact of Farmers’ Expected Output Factors on Equilibrium Decision-Making and Financing Strategy Selection

#### 5.2. Impact of Farmers’ Risk Aversion and E-Commerce Interest Rates on Equilibrium Decision-Making and Financing Strategy Selection

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Appendix A

**Proof**

**of**

**Theorem**

**1.**

**Proof**

**of**

**Theorem**

**2.**

**Proof**

**of**

**Corollary**

**1.**

**Proof**

**of**

**Corollary**

**2.**

**Proof**

**of**

**Corollary**

**3.**

**Proof of Corollary**

**4.**

**Proof of Corollary**

**5.**

**Proof of Corollary**

**6.**

- (1) Let $CVaR\left[{\pi}_{f}^{RF*}\left(q\right)\right]-CVaR\left[{\pi}_{f}^{BF*}\left(q\right)\right]>0$, we can get$\left(1+{r}_{0}\right){\left[2b\left({\mu}_{0}^{2}+{\delta}^{2}\right){\displaystyle {\int}_{0}^{{F}^{-1}\left(\overline{\eta}\right)}\mu f\left(\mu \right)d\mu}+2c{\mu}_{0}\overline{\eta}\left(1+{r}_{e}\right)-\left(\overline{{r}_{e}}-{r}_{0}\right)c{\displaystyle {\int}_{0}^{{F}^{-1}\left(\overline{\eta}\right)}\mu f\left(\mu \right)d\mu}\right]}^{2}<$$\left(1+\overline{{r}_{e}}\right){\left[2b\left({\mu}_{0}{}^{2}+{\delta}^{2}\right){\displaystyle {\int}_{0}^{{F}^{-1}\left(\overline{\eta}\right)}\mu f\left(\mu \right)d\mu}+2c{\mu}_{0}\overline{\eta}\left(1+{r}_{0}\right)\right]}^{2}$
- (2) Let $E{\pi}_{e}^{RF*}-E{\pi}_{e}^{BF*}>0$, and we can trigger $\left({r}_{e}-{r}_{0}\right)c\left(2{\mu}_{0}\eta -{\displaystyle {\int}_{0}^{{F}^{-1}\left(\eta \right)}\mu f\left(\mu \right)d\mu}\right)<0$. Therefore, $\left(2\tilde{{\mu}_{0}}\tilde{\eta}-{\displaystyle {\int}_{0}^{{F}^{-1}\left(\tilde{\eta}\right)}\mu f\left(\mu \right)d\mu}\right)>0$ and $\tilde{{r}_{e}}<{r}_{0}$ or $2\tilde{{\mu}_{0}}\tilde{\eta}-{\displaystyle {\int}_{0}^{{F}^{-1}\left(\tilde{\eta}\right)}\mu f\left(\mu \right)d\mu}<0$ and $\tilde{{r}_{e}}<{r}_{0}$, and $E{\pi}_{e}^{RF*}-E{\pi}_{e}^{BF*}>0$. □

## References

- Li, X.; Sun, Y. Network Evolutionary Game-Based Diffusion Mechanism regarding the Nonperformance of Farmers in Agricultural Supply Chain Finance. Discrete Dyn. Nat. Soc.
**2022**, 2022, 8550974. [Google Scholar] [CrossRef] - Yu, Z.; Rehman Khan, S.A. Evolutionary game analysis of green agricultural product supply chain financing system: COVID-19 pandemic. Int. J. Logist.
**2021**, 25, 1115–1135. [Google Scholar] [CrossRef] - Luo, Y.; Deng, T.; Wei, Q.; Xiao, G.; Ling, Q. Optimal Financing Decision in a Contract Food Supply Chain with Capital Constraint. Complexity
**2021**, 2021, 8925102. [Google Scholar] [CrossRef] - Deng, L.; Wang, S.; Wen, Y.; Li, Y. Incorporating ‘Mortgage-Loan’ Contracts into an Agricultural Supply Chain Model under Stochastic Output. Mathematics
**2022**, 10, 85. [Google Scholar] [CrossRef] - Su, Y.; Gabrielle, B.; Makowski, D. The impact of climate change on the productivity of conservation agriculture. Nat. Clim. Change.
**2021**, 11, 628–633. [Google Scholar] [CrossRef] - Aslam, M. Aggregative effect on rice production due to climate change using index number under indeterminate environment: A case study from Punjab, Pakistan. Theor. Appl. Climatol.
**2022**, 147, 283–290. [Google Scholar] [CrossRef] - Jin, J.; Xuhong, T.; Wan, X.; He, R.; Kuang, F.; Ning, J. Farmers’ risk aversion, loss aversion and climate change adaptation strategies in Wushen Banner, China. J. Environ. Plann. Man.
**2020**, 63, 2593–2606. [Google Scholar] [CrossRef] - Si, R.; Aziz, N.; Liu, M.; Lu, Q. Natural disaster shock, risk aversion and corn farmers’ adoption of degradable mulch film: Evidence from Zhangye, China. Int. J. Clim. Chang. Str.
**2021**, 13, 60–77. [Google Scholar] [CrossRef] - Freudenreich, H.; Musshoff, O. Experience of losses and aversion to uncertainty-experimental evidence from farmers in Mexico. Ecol. Econ.
**2022**, 195, 107379. [Google Scholar] [CrossRef] - Yin, M.; Li, G. Supply Chain Financial Default Risk Early Warning System Based on Particle Swarm Optimization Algorithm. Math. Probl. Eng.
**2022**, 2022, 7255967. [Google Scholar] [CrossRef] - Luo, Q.; Liao, R.; Li, J.; Ye, X.; Chen, S. Blockchain Enabled Credibility Applications: Extant Issues, Frameworks and Cases. IEEE Access
**2022**, 10, 45759–45771. [Google Scholar] [CrossRef] - Corcioli, G.; Medina, G.D.S.; Arrais, C.A. Missing the Target: Brazil’s Agricultural Policy Indirectly Subsidizes Foreign Investments to the Detriment of Smallholder Farmers and Local Agribusiness. Front. Sustain. Food Syst.
**2022**, 5, 796845. [Google Scholar] [CrossRef] - Ochou, F.; Quirion, P. Impact of climate change on agriculture: Quantifying the price bias in econometric approaches. Rev. Econ.
**2022**, 73, 43–67. [Google Scholar] [CrossRef] - Zhao, X.; Calvin, K.V.; Wise, M.A.; Patel, P.L.; Snyder, A.C.; Waldhoff, S.T.; Hejazi, M.I.; Edmonds, J.A. Global agricultural responses to interannual climate and biophysical variability. Environ. Res. Lett.
**2021**, 16, 104037. [Google Scholar] [CrossRef] - Yu, Y.; Clark, J.S.; Tian, Q.; Yan, F. Rice yield response to climate and price policy in high-latitude regions of China. Food Secur.
**2022**. [Google Scholar] [CrossRef] - Assouto, A.B.; Houensou, D.A.; Semedo, G. Price risk and farmers’ decisions: A case study from Benin. Sci. Afr.
**2020**, 8, e311. [Google Scholar] [CrossRef] - Liu, X.; Shen, X.; You, M. Study on Coordination and Optimization of Contract Farming Supply Chain Based on Uncertain Conditions. Sci. Program. Neth.
**2020**, 2020, 8858812. [Google Scholar] [CrossRef] - Shi, Y.; Wang, F. Agricultural Supply Chain Coordination under Weather-Related Uncertain Yield. Sustainability
**2022**, 14, 5271. [Google Scholar] [CrossRef] - Wang, X.; Sun, S. Optimal Decisions for Contract Farming under Weather Risk. Discret. Dyn. Nat. Soc.
**2022**, 2022, 9668872. [Google Scholar] [CrossRef] - Chen, Y.; Chai, Y.; Liu, Y.; Sun, H. Novel transaction financing model towards electronic commerce. In Proceedings of the Electronic System-Integration Technology Conference, Amsterdam, The Netherlands, 17–20 September 2012. [Google Scholar]
- Cai, S.; Yan, Q. Online sellers’ financing strategies in an e-commerce supply chain: Bank credit vs. e-commerce platform financing. Electron. Commer. Res.
**2022**, 2022, 1–32. [Google Scholar] [CrossRef] - Yang, H.; Zhen, Z.; Yan, Q.; Wang, H. Mixed financing scheme in a capital-constrained supply chain: Bank credit and e-commerce platform financing. Int. Trans. Oper. Res.
**2022**, 29, 2423–2447. [Google Scholar] [CrossRef] - Yan, N.; Zhang, Y.; Xu, X.; Gao, Y. Online finance with dual channels and bidirectional free-riding effect. Int. J. Prod. Econ.
**2021**, 231, 107834. [Google Scholar] [CrossRef] - Tang, R.; Yang, L. Financing strategy in fresh product supply chains under e-commerce environment. Electron. Commer. Res. Appl.
**2020**, 39, 100911. [Google Scholar] [CrossRef] - Tao, Y.; Yang, R.; Zhuo, X.; Wang, F.; Yang, X. Financing the capital-constrained online retailer with risk aversion: Coordinating strategy analysis. Ann. Oper. Res.
**2022**, 2022, 493. [Google Scholar] [CrossRef] [PubMed] - Yang, H.; Zhuo, W.; Shao, L.; Talluri, S. Mean-variance analysis of wholesale price contracts with a capital-constrained retailer: Trade credit financing vs. bank credit financing. Eur. J. Oper. Res.
**2021**, 294, 525–542. [Google Scholar] [CrossRef] - Qazi, A.; Simsekler, M.C.E. Worst Expected Best method for assessment of probabilistic network expected value at risk: Application in supply chain risk management. Int. J. Qual. Reliab. Manag.
**2021**, 39, 155–175. [Google Scholar] [CrossRef] - Fan, Y.; Feng, Y.; Shou, Y. A risk-averse and buyer-led supply chain under option contract: CVaR minimization and channel coordination. Int. J. Prod. Econ.
**2020**, 219, 66–81. [Google Scholar] [CrossRef] - Markowitz, H.M. Portfolio selection. J. Financ.
**1952**, 7, 77. [Google Scholar] [CrossRef] - Artzner, P.; Delbaen, F.; Eber, J.; Heath, D. Coherent Measures of Risk. Math Financ.
**1999**, 9, 203–228. [Google Scholar] [CrossRef] - Ye, F.; Lin, Q.; Li, Y. Coordination for contract farming supply chain with stochastic yield and demand under CVaR criterion. Oper. Res. Ger.
**2020**, 20, 369–397. [Google Scholar] [CrossRef] - Huang, F.; He, J.; Lei, Q. Coordination in a retailer-dominated supply chain with a risk-averse manufacturer under marketing dependency. Int. T Oper. Res.
**2020**, 27, 3056–3078. [Google Scholar] [CrossRef] - Jing, B.; Seidmann, A. Finance sourcing in a supply chain. Decis. Support Syst.
**2014**, 58, 15–20. [Google Scholar] [CrossRef] - Nasiri, F.; Zaccour, G. An exploratory game-theoretic analysis of biomass electricity generation supply chain. Energ. Policy
**2009**, 37, 4514–4522. [Google Scholar] [CrossRef] - Boyabatlı, O.; Kleindorfer, P.R.; Koontz, S.R. Integrating Long-Term and Short-Term Contracting in Beef Supply Chains. Manage. Sci.
**2011**, 57, 1771–1787. [Google Scholar] [CrossRef] [Green Version] - Kouvelis, P.; Zhao, W. Financing the Newsvendor: Supplier vs. Bank, and the Structure of Optimal Trade Credit Contracts. Oper. Res.
**2012**, 60, 566–580. [Google Scholar] [CrossRef]

**Figure 2.**Operation process of agricultural product supply chain under the e-commerce financing strategy.

**Figure 3.**Impact of expected output factor of agricultural products on decision-making of production input.

**Figure 5.**Impact of farmers’ risk aversion and e-commerce’s interest rates on the optimal agricultural production input.

**Figure 6.**Impact of farmers’ risk aversion and e-commerce’s interest rates on the optimal purchase price of agricultural products.

**Table 1.**Impact of the expected output factor of agricultural products on the expected profits of e-commerce and farmers.

${\mathbf{\mu}}_{\mathbf{0}}$ | $\mathit{C}\mathit{V}\mathit{a}\mathit{R}{\mathbf{\pi}}_{\mathit{f}}^{\mathit{B}\mathit{F}\mathbf{*}}$ | $\mathit{C}\mathit{V}\mathit{a}\mathit{R}{\mathbf{\pi}}_{\mathit{f}}^{\mathit{R}\mathit{F}\mathbf{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.04}\mathbf{)}$ | ${\mathbf{\pi}}_{\mathit{e}}^{\mathit{B}\mathit{F}\mathbf{*}}$ | ${\mathbf{\pi}}_{\mathit{e}}^{\mathit{R}\mathit{F}\mathbf{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.04}\mathbf{)}$ |
---|---|---|---|---|

0 | 0 | 0 | 0 | 0 |

0.5 | 5476 | 5477 | 18,941 | 19,123 |

1.0 | 11,559 | 11,475 | 55,037 | 55,361 |

1.5 | 11,029 | 10,900 | 80,642 | 80,934 |

2.0 | 9006 | 8874 | 97,163 | 97,370 |

2.5 | 6845 | 6743 | 105,948 | 106,099 |

3.0 | 5250 | 5162 | 111,280 | 111,393 |

3.5 | 4098 | 4027 | 114,703 | 114,789 |

4.0 | 3265 | 3208 | 117,011 | 117,079 |

4.5 | 2652 | 2604 | 118,633 | 118,688 |

5.0 | 2191 | 2151 | 119,813 | 119,858 |

$\mathbf{\eta}$ | $\mathit{C}\mathit{V}\mathit{a}\mathit{R}{\mathbf{\pi}}_{\mathit{f}}^{\mathit{B}\mathit{F}\mathbf{*}}$ | $\mathit{C}\mathit{V}\mathit{a}\mathit{R}{\mathbf{\pi}}_{\mathit{f}}^{\mathit{B}\mathit{F}\mathit{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.08}\mathbf{)}$ | $\mathit{C}\mathit{V}\mathit{a}\mathit{R}{\mathbf{\pi}}_{\mathit{f}}^{\mathit{R}\mathit{F}\mathbf{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.04}\mathbf{)}$ | ${\mathbf{\pi}}_{\mathit{e}}^{\mathit{B}\mathit{F}\mathbf{*}}$ | ${\mathbf{\pi}}_{\mathit{e}}^{\mathit{R}\mathit{F}\mathbf{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.08}\mathbf{)}$ | ${\mathbf{\pi}}_{\mathit{e}}^{\mathit{R}\mathit{F}\mathbf{*}}$$\text{}\mathbf{(}{\mathit{r}}_{\mathit{e}}\mathbf{=}\mathbf{0.04}\mathbf{)}$ |
---|---|---|---|---|---|---|

0.1 | 636 | 628 | 644 | 6457 | 6357 | 6559 |

0.2 | 1586 | 1572 | 1600 | 10,193 | 10,054 | 10,336 |

0.3 | 2558 | 2543 | 2574 | 12,946 | 12,786 | 13,109 |

0.4 | 3527 | 3513 | 3540 | 15,201 | 15,030 | 15,376 |

0.5 | 4493 | 4484 | 4502 | 17,157 | 16,981 | 17,338 |

0.6 | 5476 | 5475 | 5477 | 18,941 | 18,763 | 19,123 |

0.7 | 6492 | 6507 | 6482 | 20,623 | 20,445 | 20,804 |

0.8 | 7580 | 7602 | 7555 | 22,284 | 22,110 | 22,461 |

0.9 | 8838 | 8888 | 8794 | 24,063 | 23,896 | 24,232 |

1.0 | 9057 | 9103 | 9009 | 24,359 | 24,194 | 24,527 |

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Bai, S.; Jia, X.
Agricultural Supply Chain Financing Strategies under the Impact of Risk Attitudes. *Sustainability* **2022**, *14*, 8787.
https://doi.org/10.3390/su14148787

**AMA Style**

Bai S, Jia X.
Agricultural Supply Chain Financing Strategies under the Impact of Risk Attitudes. *Sustainability*. 2022; 14(14):8787.
https://doi.org/10.3390/su14148787

**Chicago/Turabian Style**

Bai, Shizhen, and Xuelian Jia.
2022. "Agricultural Supply Chain Financing Strategies under the Impact of Risk Attitudes" *Sustainability* 14, no. 14: 8787.
https://doi.org/10.3390/su14148787