A RICARDIAN ANALYSIS OF THE CLIMATE CHANGE IMPACT ON AGRICULTURE (BAC KAN PROVINCE,VIETNAM)

Pham Thi Lam,Nguyen Ngoc Thanh,Nguyen An Thinh,Dang Huu Manh

ABSTRACT：The study deals with the Ricardian approach to assess impacts of climate change on agriculture in the Bac Kan province， Vietnam. Selected climate variables are temperature （T）， precipitation （P）， hours of sunshine （S）， and humidity （H） monitored during 1996 and 2017. Log-linear function is used to improve the reliability and to assess the level of climate change impacts over time. The result shows that the change in hours of sunshine has the greatest impact on agriculture， especially during winter， summer and autumn seasons （about 39 to 47 million VND per hectare）. The change of marginal temperature in winter， spring and summer seasons leads to increasing marginal value of agricultural land from 2 to 4 million VND per hectare. Rainfall during spring season affects significantly to the marginal value of agricultural land， which is projected changing about 38 million VND per hectare when rainfall during spring rises 1 mm more. The figure for humidity during summer is about 9 million VND per hectare， although the humidity affects less than on the marginal value of agricultural land.

Key words： Ricardian analysis， climate change impact， agriculture， Bac Kan， Vietnam

1. Introduction

Rapid development of economies for recent decades relates to both positive and negative changes in climatic systems and environment globally. The variation of temperature， rainfall， humidity， and natural hazards effects significantly economic sectors. Among economic sectors， agriculture is the greatest affected by climate change because it depends on natural resources such as land， water， and climate （Nordhaus， 1991; Cline， 2007; Mendelsohn， 2008）. Ricardian model， which was first introduced by Mendelsohn et al. （1994）， shows its advantage in assessing the impact of climate change on agriculture. Liu et al. （2004） undertook Ricardian model combined with five climate scenarios until 2050 to assess for 1275 agriculture dominated counties in China. The results show that all parts of China benefit from climate change in most scenarios. According to a consideration of over 100 countries world-over， Cline （2007） concluded that by 2080s global agricultural field will fall by about 15.9% if global warming continues unabated. Developing countries will be seen a larger decline with about 19.7%. Total values of crops and livestock are produced on land. Production process is affected by climate factors such as temperature， precipitation， hours of sunshine， and humidity. Ricadian model is integrated with household level data to analyze long-term impact of climate on farm profitability over countries worldwide （Mendelsohn and Reinsborough， 2007; Lippert et al.， 2009）.

In Vietnam， Trinh （2017） used Ricardian technique to examine the relationship between climatic variables and agricultural output in Vietnam. Higher temperature affects negatively net revenue of irrigated farms for a long period. Future climate scenarios are used to project potential impacts of climate changes to farmers. In the paper， the Ricardian approach is selected to assess the impact of climate change on marginal value of agricultural land in Bac Kan province. The text is organized in the following way： Section 2 presents the methodology of Ricardian model; results of Ricardian regression are described in Section 3; finally， a conclusion entailing the main considerations on impacts of climate change to agriculture are shown in detail in the Section 4.

2. Methodology

2.1. Study area

The selected Bac Kan locates in Northern Mountains of Vietnam. The annual average temperature is of 22.5 degree Celsius. The lowest average temperature is about 15.7 Degree Celsius. The highest average temperature is about 28 degree Celsius in June. Bac Kan is affected by Southeast wind in the summer and Northeast in the winter. The annual rainfall is 1，400 mm to 1，900 mm. The highest rainfall is in July and lowest in February. Rain season during February and September accounts for 75 to 80% of total annual rainfall. Annual average air humidity is from 80% to 85%. Besides advantages about climate， Bac Kan province also has disadvantages like hoarfrost， hail， tornados which have adverse effects on lives and production activities of farmers.

Temperature and humidity changed lightly， the temperature increased 1 degree celcius， whereas humidity fluctuated. The number of hours of sunshine and the annual rainfall changes significantly. Rainfall rose from 1，000 mm （2003） to 2，000 mm （2008） berore falling down about 1，250 mm （2016）.  The data from Meteorological stations in Bac Kan province shows the difference of water level and water flow， greatest difference was seen in 2013. The maximum water level was higher than the minimum water level about 300 cm， the figure for water flow was about nearly 400 cm.

2.2. Ricardian approach

Ricardian approach reflects changes and controls of farmers currently on each land area to adapt to climate （Mendelsohn et al， 1994）. This model provides effective tool to project the changes of agricultural production in the context of global climate variability （Timmins， 2006）. However， this model does not consider the difference in productivity based on spatial climate variables （Deschênes and Greenstone， 2007）. An effective strategy adopted by farmers is changing or control their plants （Kurukulasuriya  and  Mendelsohn，  2008）. The climate change has affected directly agricultural production of farmers who have been limited to access to adaptive opportunities with these changes （Massetti and Mendelsohn， 2010）.

Ricardian model is developed based on the assumption that farmers always expect to maximize their profits replied on land characteristics. That means that famers know how to choose the types of plants and animals， inputs for per unit acreage to get a largest income.

The Ricardian model also assumes that farmland value per hectare （V） of each farm i in location r is equal to the present value of future net revenues from farm activities：

Where： Pr is the market price of each crop at location r; Qi，r is the output of each crop at farm i at location r; Xi，r is a vector of inputs for each crop at farm i; Mr is a vector of input prices at location r; Zi，r is a vector exogenous variables at location r and φ is the interest rate.

Vi，r = f（Zr）

Exogenous variables can be grouped into four subgroups： climate variables （including temperature （T）， precipitation （P）， hours of sunshine （S） and humidity （H））， geographic variables （G）， soil variables （O） and socio-economic variables （H）.

2.3. Data collection

In this study， differences between regional features， input and output prices of crops and livestock， geographic and soil characteristics， is not showed obviously. Therefore， variables such as Geography （G）， Soil （S） and Socio-Economics （H） are exclusive in this study. An alternative is considered as a good choice is that observations are time variables of climate， including temperature （T）， precipitation （P）， hours of sunshine （S） and humidity （H）.

Climate factors Variable description

Temperature （T） Average temperatures， rainfalls， number of sunshine hours， humidity of each season （winter， spring， summer and fall） are calculated on average temperature of 3 months in a season from 1996 to 2017.

Rainfall （R）

Number of sunshine hours （S）

Humidity （H）

The study will use log-linear functional form with hope that it will have a more uniform predictive power compared to the linear model.

We therefore estimate the following model for each year i and season k in Bac Kan province as model below：

Vi： the value of agricultural land in Bac Kan province， the value is calculated by total of all products produced on the land （including plants and farming animals）.

The expected marginal impact and marginal effect of seasonal climate variables can be calculated as table below：

Whereis an average value of used agricultural acreage compared to the total of agricultural acreage of the research site.

Data about agricultural land acreage and the value of agricultural production over time from 1996 to 2017 is collected in Department of Agriculture and Rural Development of Bac Kan province. It should be considered that the production value of farmland in 1996 is different to in 2017， so the study will convert the land value of all years prior to 2017 into 2017 by the formula： FV = PV （1+i） n

Where： FV is Future Value of money; PV is Present Value; i is interest rate; n is number of years

Data related to climate is captured in Meteorological stations in Bac Kan， including temperature， precipitation， hours of sunshine and humidity from 1996 to 2017. Climate data will be collected by month over seasons and years， and then these data will be calculated for seasons during one year.

3. Results

3.1. Existing agricultural production

Food crops and aquaculture acreages in Bac Kan province are shown on the chart above over a period of 22 years from 1995 to 2016， the trend of food crops and aquaculture acreages rose over majority of time shown. Namely in 1995， area of food crops was around 21 thousand hectare， the figure for 2016 increased to about 40 thousand hectare. The area of corn rose with a faster speed from about 3.3 thousand hectare in 1995 to about 16.4 hectare in 2016 （Figure 1）.

The acreages of other food crops such as cassava and sweet potato have had a tendency to decrease or remain stable. Rice and corn were main food crops of Bac Kan province and brought the greatest source of income for farmers from agriculture production.

According to General Statistics office of Vietnam， total number of buffaloes and cows did not have a lot of changes over 22 years （from 1995 to 2016）. The number of cows slightly increased between 1995 and 2007 before significantly decreased from 2008 to 2016.

At the same time， the number of pigs and poultries had obvious changes. Especially in 2004， the number of pigs and poultries significantly fell， about 1000 thousand pigs and 850 thousand poultries. However， the figures rose lightly again after 2005.

3.2. Ricardian regression

The descriptive statistics of independent and dependent variables shows in table below.

Table 1. Descriptive statistics

Variables Mean Minimum Maximum

V 18.35 2.15 31.23

T_winter 16.27 13.80 18.07

T_spring 23.39 21.47 24.90

T_summer 27.75 27.13 28.70

T_autumm 26.96 22.43 28.80

P_winter 21.66 5.40 52.77

P_spring 108.59 64.10 183.47

P_summer 269.60 183.77 365.10

P_autumm 73.58 33.70 185.57

S_winter 231.78 86.00 368.60

S_spring 103.09 80.33 135.00

S_summer 164.14 132.33 201.00

S_autumm 146.21 114.33 168.33

H_winter 80.47 76.33 83.33

H_spring 82.31 79.33 85.67

H_summer 96.27 82.67 319.33

H_autumm 83.43 80.67 86.33

With different reliabilities， namely 90%， 95% and 99%， the result of the Log-linear model shows that there is a clear about the impacts of factors on each farmland value in Bac Kan province.

Temperature variables group： Summery temperature has greatest impact on the value of each farmland hectare with the significant 0.1， control parameter of temperature variables is 0.91. This means that if temperature increases 1 degree Celsius， the farmland value of each hectare will change 0.91 % （units）.

Table 2. Regression result about impact of climate variables on farmland value

Variables Coef Se Variables Coef Se

T_winter 0.0012** 0.001 S_winter -1.362** 0.01

T_wintersq 0.0009* 0.004 S_wintersq -0.012*** 0.031

T_spring 0.012*** 0.002 S_spring 1.001*** 0.004

T_springsq 0.0003* 0.219 S_springsq -0.0017** 0.001

T_summer 0.91* 0.023 S_summer 1.023* 0.007

T_summersq -0.0061*** 0.005 S_summersq 0.245** 0.055

T_autumn 0.092** 0.025 S_autumn 0.0034*** 0.021

T_autummsq -0.0027* 0.001 S_autumnsq -0.023* 0.09

P_winter 0.0978*** 0.016 H_winter 1.005** 0.042

P_wintersq -0.003** 0.002 H_wintersq -0.004* 0.046

P_spring 0.912* 0.003 H_spring 0.921** 0.091

P_springsq 0.025*** 0.009 H_springsq 0.007* 0.072

P_summer 0.267** 0.045 H_summer 0.033* 0.008

P_summersq 0.00081*** 0.014 H_summersq -0.0083* 0.024

P_autumn 0.125** 0.026 H_autumn 0.612** 0.032

P_autumnsq -0.0034* 0.018 H_autumnsq 0.0034* 0.083

* p = 99%; ** p = 95%; *** p = 90%

The impact of rainfall autumn on farmland value in summer and are higher， with control parameters are 0.627 and 0.125 respectively. By contrast， the number of hours of sunshine in winter and spring has negative control parameters on farmland value. Each increased hour of sunshine in summer will reduce 1.362 of agricultural land value and the figure for spring 1.001.

Humidity plays an important factor in assessing the impact of climate change to the farmland value， the study shows that the parameters of summer， spring and autumn on the value of agricultural land are from 0.6 to 1.

Table 3. Marginal effects of climate change variables on farmland value

Marginal Effects （ME）

（1000 VND/ha） Marginal Effects （ME）

（1000 VND/ha）

T_Winter 2，370 S_Winter -41，083

T_Spring 3，714 S_Spring 3，859

T_Summer 3，390 S_Summer -47，091

T_Autumn -216 S_Autumn -39，880

P_Winter -191 H_Winter 2，143

P_Spring 37，621 H_Spring -1，372

P_Summer 4，175 H_Summer -9，286

P_Autumn -2，227 H_Autumn 265

All temperature variables change the value of agricultural land， with each an increase of temperature  in the winter， spring and summer， marginal value of agricultural land rise 2.37， 3.714 and 3.39 million VND each hectare （US $154 each hectare）. However， the increase in temperature in the autumn leads to a decrease in the farmland value， about 216 thousand VND （US $9.8 each hectare）.

Rainfall in the spring and summer plays an important role in increasing the value of agricultural land. With each a rise of 1 mm of rainfall in the spring， the farmland value will increase approximately 38 million VND each hectare （equal to US $1727 each hectare）， the figure for the summer is lower， about 4.2 million VND （about US $191 hectare）. However， the change of rainfall in the winter and autumn has a negative effect on the farmland value.

The number of hours of sunshine has a greatest impact on the value of agricultural land， and this is shown throughout its marginal value and control parameter. If the number of hours of sunshine rises 1 hour， the value of agricultural land reduces from 39 million VND to 47 million VND （equal to US $1773 and US $2136）. The change of farmland value in the spring caused by the number of hours of sunshine is lower， about 4 million VND each hectare （equal to US $182）.

The marginal impact of humidity on the farmland value is lower than other climate variables. The increase of humidity in the winter and autumn causes to a rise of marginal value of farmland， whereas this factor causes to a fall of farmland value in the spring and summer.

4. Conclusion

Changing climate variables significantly influence on marginal value of farmland in Bac Kan province. Ricardian approach has been applied successfully in assessing the impact of climate change to agriculture in Asian， Europe， South American and African countries. Ricardian approach can be applied in Vietnam， which is an emerging economy and with diversity climate conditions and agricultural production is greatly affected by natural and climate factors.

The result illustrates that all climate variables effect on the value of farmland. However， the level of impact will be different depending on the season characteristics in Bac Kan province. The amount of rainfall in the spring affecting on the marginal value of agricultural land is over 38 million VND each hectare. By contrast， the impact of the number of sunshine hours in the summer and autumn on marginal value of farmland are -39 million VND each hectare and -47 million VND per hectare. The increase of temperature in the summer， winter and autumn also has an impact on the marginal value of agricultural land.

Acknowledgment： This study is in partial fulfillment of the Vietnamese National Project code KHCN-TB.04T/13-18 funded by the Vietnam National University （VNU）， Hanoi.

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