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Modelling the heterogeneous effects of stocking rate on dairy production: an application of unconditional quantile regression with fixed effects

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  • Wanglin Ma
  • Alan Renwick
  • Bruce Greig

Abstract

This article analyses the effects of stocking rate on dairy production, using New Zealand dairy farm business data for the period 2005–2014. Unlike previous studies that assume a homogenous relationship between stocking rate and dairy production, we contribute to the literature by investigating the heterogeneous effects of stocking rate on milksolids production and applying an unconditional quantile regression model with fixed effects to control for unobserved farm-specific traits that are time invariant. The empirical results show that stocking rate exerts significant heterogeneous effects on milksolids production at different quantiles. In particular, we find that an additional increase in stocking rate (i.e. one cow/ha increase) increases milksolids production per hectare by between 17% and 25% but decreases milksolids production per cow by between 5% and 12%. In addition, we find that milking interval, dairy breed, farm labour, access to irrigation and farm location are important factors that increase milksolids production.

Suggested Citation

  • Wanglin Ma & Alan Renwick & Bruce Greig, 2019. "Modelling the heterogeneous effects of stocking rate on dairy production: an application of unconditional quantile regression with fixed effects," Applied Economics, Taylor & Francis Journals, vol. 51(43), pages 4769-4780, September.
    • Handle: RePEc:taf:applec:v:51:y:2019:i:43:p:4769-4780
    DOI: 10.1080/00036846.2019.1602710
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    Cited by:

    1. Apergis, Nicholas & Chasiotis, Ioannis & Georgantopoulos, Andreas G. & Konstantios, Dimitrios, 2021. "The integration of share repurchases into investment decision-making: Evidence from Japan," International Review of Financial Analysis, Elsevier, vol. 78(C).
    2. Hongyun Zheng & Wanglin Ma, 2021. "The role of resource reallocation in promoting total factor productivity growth: Insights from China’s agricultural sector," Review of Development Economics, Wiley Blackwell, vol. 25(4), pages 2350-2371, November.
    3. Zhineng Hu & Jing Ma & Qiong Feng & C. Patrick Scott & Hani I. Mesak, 2022. "The detection dilemma of marginally non‐poor households in poverty alleviation evaluation: Evidence from a linear quantile mixed model," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1491-1517, August.
    4. Wanglin Ma & Peng Nie & Pei Zhang & Alan Renwick, 2020. "Impact of Internet use on economic well‐being of rural households: Evidence from China," Review of Development Economics, Wiley Blackwell, vol. 24(2), pages 503-523, May.
    5. Xiaoshi Zhou & Wanglin Ma & Gucheng Li & Huanguang Qiu, 2020. "Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1282-1307, October.
    6. Weir, Rebecca, 2022. "Financial performance of beginning dairy farmers in Minnesota," Master's Theses and Plan B Papers 324022, University of Minnesota, Department of Applied Economics.
    7. Ioannis Chasiotis & Andreas G. Georgantopoulos, 2021. "The flexibility of corporate payouts vis-à-vis capital investment: some UK evidence," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 18(1), pages 181-201, February.

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