# Estimating the Contribution of New Seed Cultivars to Increases in Crop Yields: A Case Study for Corn

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Methodology

_{0}to t

_{1}, then we can set period t

_{0}–t

_{1}as period one (the base period). If new seed cultivars are extensively planted in period t

_{1}–t

_{2}, this period can be identified as period two. The remaining periods can be deduced by analogy until period n.

^{e}is the expected producer price for crop; μ is a random residual term, t is the year; and α

_{0}and α

_{1}are coefficients to be estimated. Unfortunately, the expected price cannot be observed, and thus these coefficients cannot be estimated. To solve this problem, here we include Equation (3), the basic yield equation in the previous year.

_{0}λ, β

_{0}equals 1 − λ, β

_{1}equals α

_{1}λ, and v is a random residual that differs from μ. Most importantly, all variables are observed, and therefore parameters can be estimated using observed data. This model is so called Nerlove model, which has been widely applied to estimate this dynamic process in crop production [25,30,31,32,33].

#### 2.2. Model Specification for Corn in China

#### 2.3. Data Sources

## 3. Results

#### 3.1. Estimated Results

#### 3.2. Contribution Calculation

## 4. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

## References

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**Figure 1.**Seed diffusion periods in China, bordered on the time of change in adoption of new cultivars.

**Table 1.**Estimated results for corn yield equation using Arellano-Bond generalized method of moments (GMM).

Coefficients | Estimate | Std. Error | t-Value | Prob. |
---|---|---|---|---|

C | 1796.95 | 309.75 | 3.92 | 0.000 |

Y_{t-1} | 0.36 | 0.09 | 2.39 | 0.000 |

P_{t-1} | 18.27 | 7.66 | 5.90 | 0.017 |

D2 | 487.27 | 82.57 | 5.01 | 0.000 |

D3 | 782.36 | 156.08 | 6.94 | 0.000 |

D4 | 819.25 | 118.11 | 5.62 | 0.000 |

D5 | 1192.09 | 212.07 | 5.80 | 0.000 |

Method = A-Bond GMM | Obs. = 988 |

Diffusion Period | Average Yield | Over the Base Period | Over the Previous Period | ||||
---|---|---|---|---|---|---|---|

Total Increase | Increase Caused by New Cultivars | Cont. of New Cultivars | Total Increase | Increase Caused by New Cultivars | Cont. of New Cultivars | ||

Base (80–86) | 2852.4 | -- | -- | -- | -- | -- | -- |

2 (87–94) | 3642.5 | 790.1 | 487.3 | 61.7 | 790.1 | 487.3 | 61.7 |

3 (95–99) | 4494.9 | 1642.5 | 782.4 | 47.6 | 852.4 | 295.1 | 34.6 |

4 (00–03) | 4633.2 | 1780.8 | 819.3 | 46.0 | 138.3 | 36.9 | 26.7 |

5 (04–13) | 5290.9 | 2438.5 | 1192.1 | 48.9 | 657.6 | 372.8 | 56.7 |

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**MDPI and ACS Style**

Qian, J.; Zhao, Z.
Estimating the Contribution of New Seed Cultivars to Increases in Crop Yields: A Case Study for Corn. *Sustainability* **2017**, *9*, 1282.
https://doi.org/10.3390/su9071282

**AMA Style**

Qian J, Zhao Z.
Estimating the Contribution of New Seed Cultivars to Increases in Crop Yields: A Case Study for Corn. *Sustainability*. 2017; 9(7):1282.
https://doi.org/10.3390/su9071282

**Chicago/Turabian Style**

Qian, Jiarong, and Zhijun Zhao.
2017. "Estimating the Contribution of New Seed Cultivars to Increases in Crop Yields: A Case Study for Corn" *Sustainability* 9, no. 7: 1282.
https://doi.org/10.3390/su9071282