Climate change and Rice yield in Cameroon

doi:10.21203/rs.3.rs-2221886/v1

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Climate change and Rice yield in Cameroon

https://doi.org/10.21203/rs.3.rs-2221886/v1

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Rice is one of the most consumed cereals in Cameroon, the production of which remains affected by several factors, including excessive temperatures and the irregular increase in rainfall. This study used the VAR model to explore the reciprocal influence of climate change with rice yield via the evolution of rainfall and temperature as well as the intensity of CO2 emissions per rice product in the northern zone of the Cameroon. The data used comes from FAOSTAT and the “World Bank Group Climate Change Knowledge Portal over the period between 1975 and 2021. The following results were obtained. i) The constant increase in rainfall during the rainy season reduces the yield of rice and that of temperature improves the yield of rice. ii) the CO2 emission intensity per unit of rice production has a negative effect on its yield. iii) This CO2 emission reduces the rainfall pattern and stimulates the rise in temperatures and therefore reinforces climate change. We therefore recommend that rice farmers be attentive to the reciprocal effects of climate change and rice cultivation and to use new techniques allowing the reduction of CO2 emission intensity by using more organic fertilizers than chemical ones.

Agricultural Economics & Policy

Climate change

VAR model

rice yield

northern zone of Cameroon

As Dinar & al. (2008) and Seo & al. (2009) noted, African agricultural systems, which are mainly rainy, are still highly dependent on unpredictable climate change. Indeed, climate change is a long-term change in the average weather patterns that define the Earth’s local, regional and global climate due to the anthropogenic emissions of greenhouse gases. It is characterised either by an excessive and unusual rise or fall in temperature, or by an abundance or lack of rainfall resulting in floods and droughts. However, anthropogenic emissions of greenhouse gases, which are the main driver of climate change, are often caused by human activities. It is acknowledged that the uncontrolled growth of these emissions increases the Earth’s temperature. The consequences include melting glaciers, irregular rains, leading to more and more extreme weather events and changing seasons. The accelerated pace of climate change, coupled with global population growth and income contractions, threatens food security everywhere (Nelson et al., 2009).

It is commonly accepted that greenhouse gas emissions are mainly due to industrial, energy and agricultural productions. The effects of greenhouse gas emissions on climate change are felt around the world on physical, biological and human systems (Parrenin and Gargas; 2020). The migration of marine species over tens of kilometres to the Polar Regions, or even over 100 kilometres every ten years, is an illustration. In addition, agriculture is particularly vulnerable to climate change as higher temperatures for example, are likely to reduce yields of certain crops while stimulating the proliferation of certain invasive species such as weeds, flies, locusts, rats, etc., long-term crop failures and long-term production declines (Nelson et al., 2009).

It has also been observed that crop yields such as wheat or maize have decreased and that acidification of seawater has affected many marine ecosystems that can disturb shellfish. Climate change is observed by increasing temperature differences over the same season, by the frequency of extreme weather events and by the changing distribution of precipitation. These phenomena have had and continue to have uncontrollable impacts on agriculture in both northern and southern countries (IPCC, 2015). Manifestations of climate change with direct impacts on agriculture include: changes in soil moisture (for example: desertification and water scarcity, changes in crop vegetation cycles leading to yield declines, disturbance of crop growth by floods or droughts, amplification of the effects of crop pests or some of the diseases due to a wider area of action) and changes in the distribution of animal and plant species evolving to adapt. All these phenomena will lead to a fall in yields in any activity related to agriculture. But it should also be mentioned that these phenomena also have an impact on human life and the environment. There are two main aspects to agricultural production: animal production and crop production. Crop production refers to all vegetation-related production, including forestry, the production of food crops and cash crops (FAO, 2013). Cash crops include cocoa, coffee, oil palm, rubber, cotton, sugar cane, tea, banana and even rice. This article focuses on the study of the interaction between rice yield and climate change in the northern zone of Cameroon which is constituted, in the context of this article, by the North region and the Far-north region.

The rest of the article is organized as follows. Section 2 sets out the problem. Section 3 reviews the literature. Section 4 presents the study methodology. Section 5 summarizes the main findings and discussion, while Section 6 concludes and draws some policy implications.

First and foremost, rice is a cereal whose production is mainly intended for self-consumption by industrial decortication. In Cameroon, the first attempts to develop irrigated rice were made in 1959 with the creation of the Yagoua Rice Experimentation and Modernisation Society (YREMS). As part of Cameroon’s development and economic development objectives to reduce massive rice imports, the 5th five-year programme (1985–1990) to improve agricultural productivity and production of 150,000 tonnes of paddy (equivalent to 57,000 tonnes of commercial rice) was planned between 1985 and 1986. Subsequently, the “Société du Développement du Riz Mbo” (SODERIM) in the West, and the Upper Noun Valley Development Authority (UNVDA) in the North-West were created to strengthen national rice production in Cameroon.

Despite the creation of these three structures to boost the quantity of local rice production, its importation has continued to grow. As evidence, in 2020, according to the MINADER, local production was estimated at 140,710 tonnes while demand was estimated at 576,949 tonnes, so 436,239 tonnes was imported to meet this excess demand. According to these data, local rice production meets only 24% of national demand, with the remaining 76% satisfied by imports. Cameroon’s inability to meet the demand for rice is explained by the decline in yields, which would be linked to various factors, such as farmers' lack of knowledge of rice cultivation, fertility or the nature of the soil, high temperatures and irregular rainfall. Rice yields are still linked to climatic characteristics, the Guide for the Management of Rice Cropping Systems, published by the African Soil Health Consortium (ASHC, 2014), describes the rice plant as the one that grows best in a place with permanently flooded soils and therefore rich in organic matter.

In the past, Courtois (2007) showed that the vegetative cycle of rice is more demanding in water than that of other cereals. For a good rice crop, the soil must have an optimal pH of 6 to 7 (slightly acidic or neutral soil). The minimum temperature should be above 15°C and the maximum below 35°C. The specific vegetative characteristics of rice are influenced by soil type and climate. The rice plant needs a warm and humid climate with lots of sun, coupled with precipitation and temperature regulations. Based on the specific characteristics of rice farming above, we can say that the rice growth cycle is closely related to climate elements. Thus, it can be deduced that climate change has an influence on rice farming. The figure below shows the average evolution of rice yield in the Far North of Cameroon in T/ha between 1975 and 2021.

The interannual analysis of this graph shows that the years 1984, 1991, 2002 and 2010 each recorded a sharp decline. It should also be recalled that during the period 1975–2021 there were years of strong yield growth, such as 1985, 1989 and 2009. The increase in yield in 1985 can be attributed to the implementation of the Fifth Plan Five-year period from 1984 to 1990 within the framework of the New Agricultural Policy. It should be noted that the New Agricultural Policy (NAP) and the Fifth Five-Year Plan, the objective of which was to strengthen the improvement of rural sectors by relying on agricultural sectors, have been beneficial to the rice sector. The graph amply shows that the period 19881 − 1991 remains the one when Cameroonian rice yields were the highest. Subsequently, the 1993–2001 period also saw a slightly higher return, although it was still lower than the 1981–1991 period. The period 2002–2016 shows a constant trend with a high peak in 2009, but also remains lower than the period 1993–2001. In conclusion, this graph shows a general downward trend in paddy rice yields In the North Cameroon zone, which is explained, among other things, by the demotivation of rice farmers through the devaluation of Cameroonian rice compared to imported rice, the increase excessive temperatures and irregular rainfall. However, on the humanitarian level, rice is the most consumed cereal in Cameroon and its consumption continues to increase from year to year. In 2009, for example, it was estimated at 300,000 tonnes and will be around 576,949 tonnes in 2020, showing an increase of 31.58% after 12 years. As rice is still widely consumed, there are concerns that local production may not meet actual demand, in a global context where global net anthropogenic emissions continue to rise (IPCC, 2022).

Returning once again to the notion of climate change, the fundamental elements of which are the uncontrolled variations in temperature and rainfall, and even the violence of the winds, it should be emphasized that it has become a subject of great concern for the daily life of Cameroonians especially in the northern zone. These climate change indicators are alternatively illustrated in the figures below.

Observation of Fig. 2 confirms a priori that the evolution of the average annual temperature during the rainy seasons is subject to random variations. These variations reflect the phenomenon of climate change, which is on the agenda all over the world, wreaking havoc without limit and without concrete control. The evolution of the average temperature during the rainy seasons in the northern zone over the period 1975–2021 shows a general upward trend. This variation oscillates around an average of 27.15°C, with a maximum of 27.77°C in 1988, representing the year whose rainy season remains the hottest in the study interval in the northern zone followed by year 2015 (27.76°C) and a minimum of 26.32°C in 1975 which represents the year during which the rainy season remains the coolest. Climate change is also manifested by the irregularities and violent rainfall observed in Fig. 3 below.

This figure suggests a priori that the evolution of average annual rainfall in the North Cameroon area shows random variations without resorting to statistical analysis. To deepen the understanding of these graphic observations, we can say that from 1975 until 2021, the average rainfall is 889.33mm with a minimum of 635.56mm (in 1984) and a maximum of 1052.44mm in 2016 (the year in which the Far North region recorded floods with innumerable damage). The evolution of precipitation shows a constant overall trend despite its interannual variability.

On the humanitarian front, rice is the most consumed cereal in Cameroon and its consumption continues to increase from year to year. In 2009, for example, it was estimated at 300,000 tonnes and about 576,949 tonnes by 2020, an increase of 31.58% after 12 years. As rice is still widely consumed, there is concern that local production will not be able to meet real demand in a global context where global anthropogenic emissions continue to rise (IPCC, 2022).

As a result of the above and the observations of the graphs presented above, i.e. the general downward trend in rice yields, the increase in average annual temperatures and the interannual variability of rainfall, the rapid growth of the population and the growing demand for rice consumption in Cameroon, this document will concern itself with determining the interaction between climate change and rice yields in the northern zone of Cameroon. In other words, we will be concerned about the question of what are the interaction effects between climate change and rice production in Cameroon? In fact, agriculture, land use, forestry and food systems both contribute to climate change and could also be used to mitigate the effects of climate change. However, there are increasingly close links between climate change mitigation, development pathways and the pursuit of the Sustainable Development Goal (IPCC, 2022).

The link between climate change and agricultural yield in general is not new. According to Lobell and Gourdji (2012), climate variability generally exposes plant cells in different crops to pests and diseases, affecting agricultural production. The two authors point out that four main factors have and will continue to affect agricultural production in the future: i. Increase in temperature; ii. Intensified hydrological cycle; iii. increased carbon dioxide (CO₂) concentration; iv. High Ground-level ozone (O₃). Mendelsohn and Schlesinger (1999) argue that at certain temperature thresholds crop yields tend to fall and that the delay in the growth process leads to poor cereal production.

Later, Mendelsohn and Dinar (2003) studied the interaction between climate, water and agriculture in the United States. Using the standard Ricardian model modifications, they found out that the value of irrigated cropland is not sensitive to rainfall but increases in value with temperature. Climate also affects the soil fertility and quality; the plant respiration rates and the grain filling. Ahmad & al. (2014) analyse the impact of climate change on wheat productivity in Pakistan. Using the production function approach applied to the data for the period 1981–2010, they found out that a 1°C increase in average temperature during the planting period would reduce crop yield by 7.4%. The same average temperature increase in January and February improves wheat productivity by 6.2%. A decade earlier, Gbetibouo and Hasan (2005) found out that when farmers do not use climate change adaptation techniques, a 2°C increase in normal temperatures results in an 11% decrease in winter crop yields, whereas, a 5% decrease in rainfall over the same period results in a 4% increase in crop yields.

On a different note, Gérardeaux and Giner (2012), in their exploratory study in low-input rainfed production areas, found positive effects of temperature and increased CO₂ on cotton growth. They also showed that the overall effects on cotton yields are positive in the most pessimistic climate change scenarios, due to the fertilizing role of CO₂. They also found out that climate change in northern Cameroon can be anticipated by implementing alternative farming systems and techniques. Loison & al. (2017) added to these studies by indicating that farming practices (Nonoptimal planting dates) and lack of genetic gain are the main causes of the decrease in crop yields in this region. Thus, management techniques and planting dates are essential to the productivity of arable land (Dobor & al. 2016). In this context, Molua (2011) reviews a section of the literature showing that climate change can be very damaging to crops and livestock in Africa. Likewise, Roudier & al. (2011) reviewed 16 studies conducted in West Africa and concluded that in most cases the effect of climate change on crop yields is rather negative.

It is generally accepted that the rice plant is mainly aquatic, and therefore very demanding especially in rainfall (Courtois, 2007). A study conducted on the vulnerability of rice production to the effects of precipitation and temperature variations in the Ndop marshes in the North-West Cameroon), found that decreasing and increasing rainfall intensity during the rainy season affects the production of flooded rice (Antu & al. 2013). Likewise, Tabi & al (2010) observed that the reduction in the amount of rain, change in precipitation patterns, early or late rainfall and short rainy season, lead to water shortages below acceptable thresholds, causing the degradation of wetlands and subsequently increase exposure of rice to vulnerabilities in semi-arid regions. Rice production is vulnerable to extreme temperature anomalies and occasional droughts (Antu & al. 2013). Auffhammer et al. (2011), in their study of 9 Indian states, found out that drought and extreme precipitation (due to climate change) negatively affect rice yield, especially in the rainfall areas over the period from 1966 to 2002. They also found out that the impact of drought is higher than the impact of extreme precipitation. Horie (2019) simulated a model that predicts the differential regional effects of projected global warming on rice yield in Asia, given double CO₂ emissions. The simulations show that high heat-induced sterility tolerance of spikelets and high yield potential under high CO₂ are the two important characteristics required for rice genotypes adapted to global warming.

In conclusion, there is still no consensus on the link between climate change and crop yields. Most of the studies presented above use projected data on climate change variables and CO₂ to stimulate the effect of climate change on agricultural and livestock production. This study enriches the literature by using econometric techniques to analyze the observed data and focusing on the effect of temperature, precipitation and CO₂ on rice yield.

This section presents the data sources and the empirical strategy.

4.1. Data sources and processing

In the database of the "Work Bank Group Climate Change Knowledge Portal", we extracted data on temperatures (in degrees Celsius) and monthly rainfall in the North and Far North region of Cameroon. We calculated the half-yearly averages for each region while considering that the rainy season lasts six months (May, June, July, August, September and October). We then calculated the averages of these two variables with reference to these two regions constituting the northern zone of Cameroon in which rainfed and irrigated rice cultivation is carried out. In the FAO database (FAOSTAT), we had data on paddy rice yields (in hg/ha), which we converted into tons per hectare as references from SEMRY data. Data on the intensity of CO2 emissions per rice product in gigagrams, capturing the average concentration of carbon dioxide by rice production are also provided by FAOSTAT. The study period extends from 1975 to 2021 which is long enough to perform a long-term analysis.

All data have been transformed by taking the natural logarithm (Ln) to correct for sinusoidal variations typically encountered in some time series. The variables considered in the analysis model are then: paddy yield represented by (lnR), annual temperature by (LnT); annual rainfall by (LnRf) and CO2 emissions by (LnICO2). The statistical description of the data for these variables are described in Table 1 below.

4.2. Empirical Strategy

Table 1: Statistical Data Descriptions

	LnR	LnRf	LnT	LnICO2
Mean	0.755493	6.785483	3.301161	4.343895
Median	0.848697	6.798833	3.303952	3.870422
Maximum	1.698693	6.958867	3.323956	6.178763
Minimum	0.000000	6.454507	3.270329	3.017088
Std. Dev.	0.545373	0.102379	0.013042	1.004627
Skewness	0.138079	-0.844230	-0.336236	0.688604
Kurtosis	1.527253	3.989011	2.391138	2.115279
Jarque-Bera	4.396943	7.498538	1.611572	5.247224
Probability	0.110973	0.023535	0.446737	0.072540
Sum	35.50818	318.9177	155.1546	204.1631
Sum Sq. Dev.	13.68186	0.482142	0.007824	46.42671
Observations	47	47	47	47

Source: the authors, estimated from Eviews 10

The Table 1 shows that the asymmetry coefficients (Skewness) of the temperature and rainfall variables considering their logarithm are negative and not close to zero. Thus, the distributions of these variables are symmetric about their mean, which means that the probability masses are equally concentrated on the lower and upper parts of their mean. That is to say that over the 47 years considered, the probability of falling on a year where the observations of temperature and precipitation are lower than their average is more evenly distributed than their complement.

All the Kurtosis are positive and greater than 1, so we can deduce that the variations are flatter than a normal distribution (which is justified by the significance of the Jarque-Bera test at the 5% threshold) for all the variables. The standard deviations of rice yield and CO2 emitted are high compared to those of temperature and rainfall, so there are significant deviations from the average. We can therefore suspect the non-stationary state of the time series of rice yield and CO2 emitted by taking their logarithmic evolution. It will then be necessary to do the Augmented Dickey Fuller (ADF) stationarity test, then the Granger Causality test and finally the Johannsen Co-integration test.

Since the purpose of the study is to determine the reciprocal effects of climate change and rice yields in the northern zone of Cameroon, we will use the standard VAR model, to statistically deduce the contribution of temperature and rainfall and a variable (the intensity of CO2 emitted per rice product) to rice yields and their lagged values. In general, this VAR model adapted to the objectives of the study is specified as follows.

LnRt = ω ₁₀ + ω₁₁ LnRt-1 + ω₁₂ LnTt-1 + ω₁₃ LnRft-1 + ω₁₄ LnICO2t + ε_{1 (1)}

LnTt = ω ₂₀ + ω₂₁ LnRt-1 + ω₂₂ LnTt-1 + ω₂₃ LnRft-1 + ω₂₄ LnICO2t + ε_{2 (2)}

LnRft = ω ₃₀ + ω₃₁ LnRt-1 + ω₃₂ LnTt-1 + ω₃₃ LnRft-1 + ω₃₄ LnICO2t + ε_{3 (3)}

In this system of three equations, R represents the yield of paddy rice in tonnes/ha; T, average annual temperature in degrees Celsius; Rf, annual rainfall in mm; CO2, CO2 emissions intensity per rice product in gigagrams (control variable); t refers to time in years, t = 1975, …, 2021; LnRt − 1, LnTt − 1, LnRft-1 and LnICO2t-1 are the respective lagged variables and ε1, ε2, ε3 are white noises.

The model being already specified, we will move on to the presentation and discussion of the results, including any statistical tests.

5.1. Description of Standard Test: Granger Causality Test

Before the estimation of the specified system, we will perform the augmented Dickey-Fuller test, the Granger causality test and the co-integration test. The parameters of a VAR model can only be estimated on stationary time series. Therefore, we first perform the augmented Dickey-Fuller stationarity test on the model variables. The results of this test are summarized in Table 2.

Table 2: Dickey-Fuller Augmented Stationarity Test

Variables	A niveau	Différence première
LnR	-3.400197	-9.997371***
LnT	-6.151594**	//
LnRf	-4.845036	-6.726480***
LnICO2	-1.372752	-9.182871**

Source: The authors, estimates made from Eviews 10 software (*** and ** correspond respectively to the 1% and 5% significance)

In the light of the DFA test, it is found that all the variables are not stationary at level, except the variable temperature (LnT) which is stationary at level. We made them stationary by making the first difference. Thus, they are integrated of order 1 and the notation of these variables are then LnRds, LnRfds and LnICO2ds. There is therefore a risk of co-integration. But before reviewing Johansen's Co-integration test, we will first perform the Granger causality test to see if the variables have reciprocal influence relationships or not.

5.1.1. Granger Causality Test

Performing the Granger causality test first requires the prior determination of the lag number of the VAR(p) model. On reading the information criteria (see table 1 in the appendix), the optimal delay p is equal to 1 according to the AIC and QH criteria.

Table 3: Granger Causality Test Estimate



Null Hypothesis:	Obs	F-Statistic	Prob.

LnRfds does not Granger Cause LnRds	45	0.00727	0.9325
LnRds does not Granger Cause LnRfds	45	0.26532	0.6092


LnT does not Granger Cause LnRds	45	0.25477	0.6164
LnRds does not Granger Cause LnT	45	0.63642	0.4295


LnICO2DS does not Granger Cause LnRds	45	0.02731	0.8695
LnRds does not Granger Cause LnICO2ds	45	0.40794	0.5265

Source: the authors, estimated from Eviews 10

The results of the global granger causality test shown in Table 3 above show that the variables rainfall, temperature and intensity of CO2 emitted by rice product significantly influence the yield of paddy rice at the 5% threshold. But these influences can be negative or positive, which is confirmed by the signs of the correlation coefficients listed in the correlation table between these variables. Since the LnRds, LnRfds and LnICO2ds variables are integrated at order 1, there is a chance of having a co-integration relationship between paddy rice yield, annual rainfall and the intensity of CO2 emissions per rice product.

5.1.2. Johannsen cointegration test on variables

Table 4: Results of the Johannsen cointegration test


Unrestricted Cointegration Rank Test (Trace) LnRd et LnRfds


Hypothesized		Trace	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**


None *	0.543076	62.25591	15.49471	0.0000
At most 1 *	0.468297	27.79346	3.841466	0.0000


Unrestricted Cointegration Rank Test (Trace) LnRds et LnICO2ds


Hypothesized		Trace	0.05
No. of CE(s)	Eigenvalue	Statistic	Critical Value	Prob.**


None *	0.501392	49.46646	15.49471	0.0000
At most 1 *	0.348385	18.84529	3.841466	0.0000

Source: The authors, Eviews 10 software estimates

At the critical threshold of 5%, the Johansen co-integration test reveals the presence of a co-integration relationship between paddy rice yield and rainfall (see the upper part of Table 4) and between rice yield and the emission intensity of rice (see lower part of Table 4 above). We can therefore use a vector error correction model to estimate the coefficients of the explanatory variables. This is what requires the use of a dynamic model (the VAR model) to obtain the reciprocal relationships between these variables.

5.2. Presentation of results

Table 5: Estimation of the VAR model

	Model 1	Model 2	Model 3
	LNR	LNRF	LNT


LNR(-1)	0.539459	-0.051828	0.003918
	(0.12103)	(0.04143)	(0.00503)
	[ 4.45735]	[-1.25110]	[ 0.77946]

LNRF(-1)	-0.225602	0.351734	-0.009465
	(0.43078)	(0.14745)	(0.01789)
	[-0.52370]	[ 2.38542]	[-0.52904]

LNT(-1)	0.945346	2.787836	0.225537
	(3.58382)	(1.22670)	(0.14883)
	[ 0.26378]	[ 2.27263]	[ 1.51538]

C	-0.321857	-4.646837	2.597145
	(12.7777)	(4.37365)	(0.53064)
	[-0.02519]	[-1.06246]	[ 4.89433]

LNICO2	-0.209984	-0.027572	0.004920
	(0.06786)	(0.02323)	(0.00282)
	[-3.09424]	[-1.18700]	[ 1.74576]


R-squared	0.767784	0.221007	0.216810
Adj. R-squared	0.745129	0.145008	0.140401

Source: The authors, Eviews 10 software estimates

Note: The figures in parentheses represent the standard deviation and in square brackets the Student test.

The estimate of the specified VAR model, in which rice yield, temperature and rainfall are interdependently explained variables is globally significant at 74%. In addition, the Jarque-Berra error normality test contained in the Cholesky estimation error horgonalization table shows a normal distribution of errors at the 5% threshold (see Table 2 in the appendix).

These results reveal that in Model 1 of Table 5, the yield of paddy rice lagged one period is significant and positively related to its current value. This confirms the hypothesis of continuous decline in rice yield from 1975 to 2021, as shown in Fig. 1 above. The coefficients associated with the shift in temperature and rainfall are respectively positive and negative. Thus, the rise in temperature and rainfall alternately influences the yield of rice in a positive and negative way. Similarly, the intensity of CO2 emissions per rice product negatively impacts rice yield. In model 2 (Table 5), rainfall (LnRf) is negatively influenced by the intensity of CO2 emissions per unit of rice production (LnICO2). In model 3, the results show that the intensity of CO2 emissions negatively impacts the temperature, since an increase in CO2 emissions amplifies the rise in temperature (LnT). In the northern zone of Cameroon, climate change is one of the factors that have degraded rice yields throughout the period studied. Similarly, the CO2 emission intensity in diagram per unit of rice production considerably stimulates the reduction in rice yield, the reduction in rainfall and the rise in temperature.

5.3. Discussion of results

The yield of rice is negatively influenced by the intensity of CO2 emissions and positively influenced by the increase in temperature. The positive relationship between paddy yield (LnR) and temperature (LnT) confirms work published in 2012 on "the positive effect of climate change on rice in Madagascar" by Edward Gerardeaux and Michel Giner who found positive effects of increased temperature and CO2 on rice growth and showed that the overall effects on rice yields are positive. In the most pessimistic climate change scenarios, they demonstrated that the sustainability of these systems is threatened, which confirms as much our result of negative effect of CO2 emission intensity through carbon stock per unit of production. rice.

The relationship between temperature and CO2 emissions is positive, which would mean that increasing CO2 emissions increases the temperature. On the other hand, the negative impact of the increase in rainfall on rice yield contradicts the results of the study by Brigitte Courtois (2007) which had shown that the rice plant is too demanding in water, for which a large quantity rain is still very necessary to positively influence yield. And conversely confirms the study by Auffhammer et al. (2011) which indicates that the decrease in extreme rainfall exposes rice cultivation to vulnerability.

Climate change has become a global concern whose influence on agricultural production has long remained all the more dangerous and uncontrolled as certain agricultural crops contribute to its acceleration. This study allowed us to analyse the reciprocal effects of climate change and rice yield in the North and Far North of Cameroon, taking into account changes in temperature and rainfall during the rainy seasons as well as the intensity of CO2 emissions per rice product. In the methodology, we used statistical and econometric techniques to study these reciprocal effects using the VAR model. We referred to the World Bank Group Climate Change Knowledge Portal database for data on temperature and rainfall trends and to the FAO database (FAOSTAT) for yield data (converted to T/ha) of paddy rice and C02 emissions intensity data.

The results of this study showed that rice yields in the northern Cameroonian zone are negatively affected by the increase in rainfall and positively by that of temperature and that the increase in temperature is in turn reinforced by the increase in temperature. intensity of carbon dioxide (CO2) emissions per rice product.

In the light of these observations and taking into account the policy of revival of the Cameroonian economy based essentially on the modernization of agriculture through the development and local processing of its products, it is recommended that farmers become aware of the relationship negative relationship between climate change and agricultural activities, particularly rice cultivation. Specifically, and concerning this study, rice farmers must use new rice-growing techniques emitting less CO2 to adapt the rice-growing system to these changes. These recommendations should be followed to the letter by government actions each year.

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Tableau 1 : Estimation des critères de sélection de l'ordre de décalage

VAR Lag Order Selection Criteria
Endogenous variables: LNRDS LNRFDS LNT LNICO2DS
Exogenous variables: C
Date: 09/24/22 Time: 23:24
Sample: 1975 2021
Included observations: 41


Lag	LogL	LR	FPE	AIC	SC	HQ

0	152.9863	NA	8.20e-09	-7.267624	-7.100446*	-7.206747*
1	169.1398	28.36713*	8.18e-09*	-7.275112*	-6.439223	-6.970727
2	175.9546	10.63778	1.31e-08	-6.827054	-5.322454	-6.279162
3	187.8193	16.20542	1.71e-08	-6.625331	-4.452021	-5.833931
4	204.2015	19.17914	1.89e-08	-6.643975	-3.801953	-5.609067
5	228.7224	23.92284	1.55e-08	-7.059629	-3.548896	-5.781213
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion

VAR Lag Order Selection Criteria
Endogenous variables: LNRds LNRfds LNT LNICO2ds
Exogenous variables: C
Date: 09/24/22 Time: 23:24
Sample: 1975 2021
Included observations: 41

Lag	LogL	LR	FPE	AIC	SC	HQ


0	152.9863	NA	8.20e-09	-7.267624	-7.100446*	-7.206747*
1	169.1398	28.36713*	8.18e-09*	-7.275112*	-6.439223	-6.970727
2	175.9546	10.63778	1.31e-08	-6.827054	-5.322454	-6.279162
3	187.8193	16.20542	1.71e-08	-6.625331	-4.452021	-5.833931
4	204.2015	19.17914	1.89e-08	-6.643975	-3.801953	-5.609067
5	228.7224	23.92284	1.55e-08	-7.059629	-3.548896	-5.781213

* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion

Tableau 2 : Test de normalité des erreurs d'estimation

VAR Residual Normality Tests
Orthogonalization: Cholesky (Lutkepohl)
Null Hypothesis: Residuals are multivariate normal
Date: 09/26/22 Time: 15:36
Sample: 1975 2021
Included observations: 46



Component	Skewness	Chi-sq	df	Prob.*


1	-0.379160	1.102176	1	0.2938
2	-0.441459	1.494129	1	0.2216
3	0.418522	1.342901	1	0.2465


Joint		3.939206	3	0.2681



Component	Kurtosis	Chi-sq	df	Prob.


1	5.204353	9.313416	1	0.0023
2	3.266966	0.136603	1	0.7117
3	2.740797	0.128773	1	0.7197


Joint		9.578792	3	0.0225


				//
Component	Jarque-Bera	df	Prob.

				//
1	10.41559	2	0.0055
2	1.630732	2	0.4425	//
3	1.471674	2	0.4791	//
				//
				//
Joint	13.51800	6	0.0355	//

* Les valeurs p approximatives ne tiennent pas compte des estimations de coefficients

Source : Les auteurs, à l'aide du logiciel Eviews 10

The authors declare no competing interests.

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Climate change and Rice yield in Cameroon

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1. Introduction

2. Problem Statement

3. Literature Review

4. Methodology

5. Results And Discussions

6. Conclusion And Recommendations

7. References

8. Appendices

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