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Climatic impacts on crop yield and its variability in Nepal: do they vary across seasons and altitudes?

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Abstract

A rapid change in climate patterns potentially driven by global warming is considered to be greatest threats to agriculture. However, little is known about how the change in climate concretely affects agricultural production especially in Nepal with respect to seasons and regions of different altitudes. To examine this issue, we seek to empirically identify the impact of climatic variation on agricultural yield and its variability by utilizing the data of rice, wheat and climate variables in the central region of Nepal. The main focus is on whether the impacts vary across seasons, altitudes and the types of crops. For this purpose, we employ a stochastic production function approach by controlling a novel set of season-wise climatic and geographical variables. The result shows that an increase in the variance of both temperature and rainfall has adverse effects on crop productions in general. On the other hand, a change in the mean levels of the temperature and rainfall induces heterogeneous impacts, which can be considered beneficial, harmful or negligible, depending on the altitudes and the kinds of crops. These results imply that adaptation strategies must be tailor-made in Nepalese agriculture, considering growing seasons, altitudes and the types of crops.

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Notes

  1. Climate change literally means statistically significant variation either in the average state of the climate elements or in their variability (Benson 2008). It includes intra and inter-seasonal, inter-annual as well as spatial variation and changes in temperature, precipitation and solar energy.

  2. We could have done a Hausman test for a specification of whether to use a random or fixed effects model in theory. In fact, we have run the test: For rice, χ 2 ≈ 125 and the corresponding p-value is .000. Therefore, the null hypothesis is rejected. For wheat, χ 2 ≈ 8.27 and the corresponding p-value is .507. Therefore, the null hypothesis cannot be rejected. However, our decision on whether a fixed or random effects model should be used is made based on the following arguments. In our Nepalese case of agricultural production, one of the underlying assumption for use of a random effects model appears to be violated. That is, a random effects model requires that a district specific effect be independent of the covariates included in the regression. Unfortunately, in our regression, this assumption is likely not to be satisfied since most farmers understand, for instance, that altitudes or climate variables as one covariate must be correlated with a district specific effect of rice and wheat production including indigenous knowledge, local ethnic customs and traditional agricultural practices that are unique in a district and very slow to change over time. The same type of arguments is made in McCarl et al. (2008) to use a fixed effects model.

  3. In Nepalese agriculture, a district specific effect cannot be considered independent of covariates in the model as explained in footnote 2.

  4. Crop area may potentially be considered endogenous especially when the variable shows an upward trend over time together with crop yield. This case corresponds to the case of wheat in our analysis. Therefore, we have run an endogeneity test (Durbin–Wu–Hausman (DWH)) for the wheat equation, and estimated an alternative specification of the model which does not include crop area of wheat. The results suggest that the null hypothesis of no endogeneity cannot be rejected, and the regression of the wheat equation in the absence of crop area exhibits a qualitatively similar result to the base model. These results are presented in the Appendix.

  5. To check heteroskedasticity in this step, we run a Breusch–Pagan test, and confirmed the presence of heteroskedasticity in both rice (at 2% level) and wheat regressions (at 1% level).

  6. The altitude dummy variable categorizes 16 districts into three groups: Low altitude (Terai)—Chitwan, Bara, Sarlahi, Dhanusha, Mahottari, Rautahat. Mid altitude (Hill)—Makwanpur, Nuwakot, Sindhuli, Kavre, Lalitpur, Bhaktapur, Kathmandu, Dhading, High altitude (Mountain)—Rasuwa and Dhading.

  7. Farmers in most of the Nepal is subsistent one, and several studies suggest that they have little capacity to adapt to climate variations (Practical Action 2008; Regional Climate Change Adaptation Knowledge Platform for Asia 2010). Of course, there is some case that a group of farmers seeks to adapt their agricultural practices to a daily change in climate, but their adaptation is still based on their “indigenous knowledge and traditional customs,” which are different from the scientific-based adaptation strategy. Thus, the effectiveness of such indigenous type agricultural adaptation to a change in climate is reported to be limited or at most only in the short run in a situation where most farmers are totally unaware of a change in climate or other groups are aware, but they would not have enough capacity to adapt (Bhusal 2009; Regional Climate Change Adaptation Knowledge Platform for Asia 2010; Manandhar et al. 2011; Maharjan et al. 2011). Typically, the only way to comp with is a change in the type of crops (Maharjan et al. 2011). This limited capacity to adapt in Nepalese agriculture mainly comes from insufficiency of local farmers’ scientific knowledge, equipments, irrigation facilities and other infrastructure (Regional Climate Change Adaptation Knowledge Platform for Asia 2010). For example, it is noted that only one-fifth of the total irrigable land has irrigation facilities, only 25% farmers have access to improved seeds, and common agricultural practices are still based on the indigenous customs and knowledge as mentioned earlier, which do not apply much chemicals and scientific approach (Central Bureau of Statistics 2006). These are consistent with our observation on Nepalese agriculture, and thus we thought that omitted variable biases should be negligible.

  8. We have calculated it by using acreage and rice yield in low altitude regions based on our samples.

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Acknowledgements

Our gratitude goes to the Promotion and Mutual Aid Cooperation for Private School of Japan for their support. We are also grateful to three anonymous referees, Eiji Mangyo, Makoto Kakinaka, Hiroaki Miyamoto, Darrel N. Florescence, Prabhat Barnwal and Namgay Dorji for their helpful supports, discussions and comments.

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Authors and Affiliations

  1. Forum for Rural Welfare and Agricultural Reform for Development (FORWARD), Bharatpur-2, Kshetrapur, PO Box-11 (Bhp)., Kathmandu, Nepal

    Santosh Poudel

  2. Graduate School of International Relations, International University of Japan, 777 Kokusai-cho, Minami-Uonuma, Niigata, 949-7277, Japan

    Koji Kotani

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  1. Santosh Poudel
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Correspondence to Koji Kotani.

Appendix

Appendix

In this appendix, we present the results of some additional tests and estimations of alternative specifications in the model with respect to the wheat equation.

1.1 Wheat equation

Crop area of wheat production exhibits an upward trend over time together with wheat yield. Therefore, there may be an endogenous problem. To check this, we have implemented an endogeneity test of Durbin–Wu–Hausman for a variable of wheat crop area. The result is as follows: F (1, 228) = 0.54 and Prob > F = 0.462. This implies that we cannot reject the null hypothesis of no endogeneity. Furthermore, we have also checked the robustness of our wheat equation by running an alternative specification which does not include wheat crop area. The result is shown in Table 11. We confirm that Just-Pope approach still generates a qualitatively similar result to the base model in Table 8, irrespective of the existence of rice crop area in model specifications.

Table 11 The estimation result without acreage
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Poudel, S., Kotani, K. Climatic impacts on crop yield and its variability in Nepal: do they vary across seasons and altitudes?. Climatic Change 116, 327–355 (2013). https://doi.org/10.1007/s10584-012-0491-8

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