The Economic Potential Impact of Climate Change on The Gambia’s Agriculture Sector: A Multi-Market Model Analysis

Given the global climate challenges, countries must comprehend how climate change will impact agricultural commodity prices, production and consumption, agricultural trade, and food security. The study used the General Algebraic Modelling System (GAMS), a multi-market model approach, with the base year of 2015, to assess the impact of climate change on the agriculture sector, given three scenarios—hydro-meteorological impact, agricultural productivity impact and their combined total effect. The results show a decline in demand for food across The Gambia. Seed demand will increase, whilst animal feed demand will decline over time. Crop yields for groundnuts (the main cash crop) and rice (the main staple crop) by 2040 will decline by 11.09% and 13.32%, respectively, given the combined total effect. The Gambia will continue to depend on the importation of basic necessities, given its food trade deficits over time. Average food prices will increase by 16.2%; consequently, The Gambia will not be able to attain food self-sufficiency; rather, food insecurity will continually be acute, leading to increased prices and decreased production and consumption. Thus, the government should increase investment in the agriculture sector, especially in crops and livestock that are resilient to adverse climate conditions.

Introduction

Climate Change Overview

Climate change has taken a central stage in policy and academic debates, given the current and imminent existential threat posed by climate variation on human and animal survival. Climate change has a far-reaching impact on hydro-meteorological conditions, affecting crop yields, livestock rearing, forestry cover, aquatic resources, biodiversity, food security, and economic growth and development. Climate distress and shocks will impede the global, regional, and national economies from attaining their desired sustainable growth. The global volatility in atmospheric and climatic conditions will cause sluggish growth resulting from low labour productivity due to the destruction of capital stock and weak labour supply, thus causing inflation because of the rising cost of food and other human necessities. These phenomena will gravely impact the global economy, thus sustainably increasing the global costs of climate change [1].

The emerging climate catastrophe will adversely impact human livelihoods and survival; thus, there is a need to comprehend factors that will negatively affect the global climate. Hansen et al. [2] show that global warming is accelerating from the late 19^th century to the 21^st century with a steady increase of 0.2°C per decade, thus elucidating the rate of increase over time that will unfavourably affect humans and their quest for existence. The Earth’s surface temperature, both ocean and land, fluctuates between the range of 0.1°F and 0.9°F from 1980 to 2015. The increase in temperature in the long term will disrupt vulnerable populations of the world, causing diseases, ill-health, and death, especially where the populace cannot easily adapt to the changing nature of the environment [3].

Most climate models possess a superior capability in predicting the impact of temperature on the climate compared to precipitation, thus making it difficult to estimate the impact of precipitation on the climate. Some regions in Asia and parts of Africa have experienced the occurrence of frequent droughts over the past decades. Predominately in these regions, climate variability is influenced by multidecadal and interdecadal, most notably caused by a shift in El Niño–Southern Oscillation (ENSO), thus affecting the climate of the tropics and sub-tropical regions of the world [4]. Much evidence shows that precipitation changes will occur in a fashion where dry regions such as the tropics and sub-tropics are projected to be drier. In contrast, wet regions such as mid to high latitudes will be wetter, thus aggravating their future climatic condition and means of adaptation [5].

Global food production is affected by climate change, especially on the ever-warming planet. The various regions of the globe react differently to this changing phenomenon. Tropical regions experience higher temperatures and limited rainfall, thus resulting in low crop productivity. However, the opposite effect is experienced in temperate regions where very low temperature is the major cause of poor crop productivity. In temperate regions, crop producers possess the technical acumen to cultivate their produce to respond positively to the natural environment, whereas this is a challenge in the tropical and sub-tropical regions of the globe, thus resulting in food and economic crises in the tropics [6]. The global food crisis, as a result of climate change, will consequentially impact global socio-economic growth. The quality and quantity of food outputs will not be distributed equally and equitably, thereby resulting in erratic changes in farmers’ incomes, food market prices, international trade, and investment in the agriculture sector, which will affect economies across the world [7], [8].

Africa’s Situation

Africa is severely affected by climate variability because the continent is vulnerable to natural and human climate shocks, thus reducing the continent’s ability to be resilient to climate shocks. Accordingly, the continent faces numerous challenges and difficulties in combating climate change. The disruption of climate change will affect Africa’s infrastructure, healthcare systems, food production, nutrition, etc., forcing the populace to migrate in search of better livelihoods [9]. In the Sahel region where The Gambia is located, there is a need for more studies to be conducted using various climate models to better understand the impact of the climate on the region, given the vulnerability of the Sahel to climate shocks. Therefore, the region must devise coping strategies to combat climate shocks and avert the socio-economic and political crisis that will emanate from climate change [10], [11].

The temperature for Africa, based on various models, has manifested that the region will experience a rise in surface temperature between 3°C and 4°C. This increased temperature is predicted to be over 100% greater than the world’s mean temperature. The foregoing will negatively affect crops, animals, and human survival [12], [13]. Projected temperature across West Africa between the late 20^th to the end of the 21^st century, using various models and emission scenarios, shows a more severe situation since temperature rise in the region will range between 3°C and 6°C [14]. It is further elucidated that temperature will be extreme in the Sahel region of West Africa, with coastal and hinterland areas experiencing a rise in temperature of 3°C and 4°C, respectively. Thus, the region will become hotter in the long term, affecting the lives and livelihoods of the dwellers of that region [15]–[17].

Climate change’s impact on rainfall in Sub-Saharan Africa (SSA) shows a significant decrease. In West Africa, the trend shows that in the Sahel region, precipitation will reduce in the long run, especially in the semi-arid areas of the sub-region. In the Sahel, historical data shows that average rainfall declined from 20% to 49% compared with the continent’s 5% to 49%. These statistics are alarming for a continent heavily dependent on rainfed agriculture to feed its rapidly growing population [18]. In southern Africa, the scenario is similar. Climate variation continuously causes a decline in rainfall over time, thus posing a threat to the availability of water supply to meet the demand for food production. The foregoing will diminish the region’s food productivity and self-sufficiency in the long term, which will have a multiplier effect on increasing poverty and impeding economic development [19].

The Significance of the Multi-Market Model

The multi-market model is a robust system that can utilise various models to study climate change influence on various subjects of interest empirically. The model can be used in a modest system—as a predictor, with weather condition types and linear regression. The assemblage of the system permits for clear interpretation of the system at each phase of the analysis. This makes the system easy to use using probabilistic predicting techniques for numerous periods [20]. The multi-market model is significant for agricultural policy transformation impact analysis, thus illustrating how well an agricultural reform policy will impact an economy. A multi-market model can address salient issues as they relate to climate change, income, poverty, trade, welfare, growth, investment, agricultural productivity, etc., so that policymakers can formulate and implement policies that will ensure optimal benefit to society [21], [22].

The multi-market model is important in analysing the effects of agricultural trade liberalisation. A study conducted in Vietnam on the rice market shows that export liberalisation would increase the price of rice, thereby negatively affecting the urban poor inhabitants. However, the study shows that the rural settlements will benefit from this scheme, especially farmers in rice-producing regions. It was concluded that the positive effect is greater, thus culminating in slight poverty alleviation and increasing household and national wealth [23], [24]. Another important aspect of the multi-market model is its effectiveness in comprehending the intentional and unintentional effects of agricultural policies at a sectoral level, thus making it possible to analyse a single market, for example, a focus on the agricultural income of a crop which may be affected by seasonal changes as was the case in a study done in Madagascar [25].

The Application of the Multi-Market Model

Multi-model analysis has been used to test climate variation projections empirically. The various models used in this approach are meant to provide some facts about accurately projecting future climate change. Consequently, the impact of climate change will be based on the data from current climate simulations against future climate simulations. The multi-models’ metric-based approach accurately measures the climate change effect, indicating future climate variations. The foregoing provides a realistic estimation of climate change for a particular location [26]. The multi-model analysis using various General Circulation Models (GCMs) corrects the systemic model error from driving and nested models, thus illustrating the robustness of the multi-model analysis in projecting climate variation. This approach can show how future trends in seasonal changes impact a particular region, thus affecting that region’s hydrological patterns, food productivity, and economic performance [27].

Francois and Reinert [28] expounded that the multi-market equilibrium model applies to various scenarios. The model could be sophisticated or simple; its application and scope will determine its features. The complexity of the model is determined by data accessibility, affordability, and availability. Thus, the robustness of the model takes into account an understanding of the subject matter to be investigated and the market and economic problems to be scrutinised. The multi-market partial equilibrium is applied in conducting empirical policy analysis with different scenarios instead of assessing an optimal policy obtained from a specific objective function, as in the case of general equilibrium. The multi-market analysis is expedient in analysing price and non-price policies’ impact on a commodity, interactions of markets, agricultural production in a country or a region, reforms of agricultural policies, household income, government revenue and expenditures, and trade balance. This amplifies the wide application of the model in agricultural studies as it relates to salient issues affecting society [29].

Agricultural Studies that Utilised Multi-Market Model

Braverman et al. [30] discussed how farmers can change their customary produce to varieties that produce higher yields, thus achieving production efficiency in the agriculture sector of South Korea. It was further explained that utilising the multi-market model approach helps elaborate policy implications related to price disparities in agricultural produce. This explains the model’s usefulness in the agriculture sector as it relates to cost reduction, pricing, and agricultural policy reforms, to mention a few. In a related study in India, Quizón and Binswanger [31] developed a multi-market model that factored changes in rural and urban incomes caused by changes in agricultural market forces. The model features focused on the demand and supply of agricultural produce, the supply and demand for resources, and finally, the real income brackets for rural and urban dwellers. Both studies illustrate the significance of the multi-market model in understanding producer and consumer behaviour in the agriculture sector in urban and rural markets.

In their study on wheat and rice price stability and trade liberalisation in India, the multi-market model, Srinivasan and Jha [32] demonstrated that contrary to conventional thinking, the liberalisation of the aforementioned crops will result in domestic price stabilisation. It was further buttressed that price stability in India would lead to a great chance of price stability worldwide. The study shows how the multi-market model is relevant in studying agricultural trade price stability for both domestic and international markets. It is important to note that the multi-market model became widely utilised in assessing the impact of agriculture-related policies in the 1980s by the World Bank in countries across the globe like Senegal, South Korea, and Cyprus, buttressing the popularity and reliability of the model in agricultural policy analysis [33].

An agricultural study utilising the multi-market model by incorporating livestock feeds and production methods into the model demonstrates the practicality for policy scrutiny and advocacy to justify national food self-sufficiency and security in light of the challenges and constraints the sector may encounter [34]. A related study on poultry in Ghana examined the impact of maise prices on the poultry sector. In that study, it was revealed that households witnessed an insignificant change in income due to low household dependence on poultry as a source of income. The foregoing demonstrates how robust the multi-market model is in informing policy to attain an optimal societal welfare decision [35].

Materials and Methods

Data Used for the Model’s Calibration

The study used a newly built, up-to-date Social Accounting Matrix (SAM), with complete agricultural economic transactions data for 2015/16, constituting the study’s base year. It is important to note that, hitherto, the SAM for The Gambia was rudimentary and uncomprehensive. Thus, they are unable to provide a fair situational analysis of the sector. The model incorporates an array of data from various sources. The data on domestic supply and demand for agricultural commodities and data on yield and crop areas were retrieved from the Food and Agriculture Organization (FAO) Statistics data portal. The data on imports and exports were obtained from the Ministry of Trade. Population data, as well as the data on consumption by household type and prices, were taken from The Gambia Bureau of Statistics (GBoS). It is worth emphasising that price changes capture market margins in the model. The parameter used is the price elasticity of land allocated for the cultivation of crops (groundnuts, maise, millet, rice, and sorghum) in addition to the supply and demand elasticities.

The Model

The multi-market approach extends the analysis of price and nonprice policy tools from their impact in commodity or factor-specific partial equilibrium models (PE) to the interactions between markets on both the product and production factor sides. It details the nature of one country as a whole or several regions (or farm sizes or farming systems) agricultural production systems, each of which is represented by a profit function derived from product supplies and production factor demands. This core of producer is complemented with systems of final demands, income equations, factor supplies, and market equilibrium conditions. The multimarket model allows one to track the impact of price and nonprice policies and reforms on production, factor use, the prices of non-tradable and net exports of tradable (products and factors), the incomes and consumption of households, government revenues and expenditures, and the terms of trade.

The multimarket approach goes beyond a consistency approach in that it attributes fundamental importance to market equilibria and the role of prices. It differs from the programming approaches in that it seeks to conduct policy analysis by simulating alternative scenarios instead of deriving an optimum policy from a stated objective function. The multimarket stresses, in particular, the income effects of these policies on different categories of households and the impacts these effects have on market equilibria. The cost of this is that it greatly simplifies the technological and constraint specifications to what can be captured by a profit function with fixed factors, and it does serve to derive optimal solutions. It also falls short of the general equilibrium models in that it takes the other sectors of the economy as given and does consider the feedback effects that macro equilibrium constraints may impose on the sector. More details about the multimarket model structure and equations can be found at Sadoulet and Janvry [29] and Hans and Quizón [36].

Structure of the Model

The study’s model’s structure comprises 6 equations: consumer and producer prices, production demand, supply, consumption, income, and equilibrium situations. Cataloged is a summarised explanation of the equations.

Price Equations

Producer prices (PP) and consumer prices (CP), the latter being considered lower than the former by a domestic market margin (MARG), given the improvements in infrastructure, for example, the cost of transportation, etc. Equation (1) factored situation where producers are given producer subsidy (PSUB):

(1) $$\begin{array}{r}P{C}_{c,h,r}=P{P}_{c,h,r}\times (1+MAR{G}_{c,h})\times (1-PSU{B}_c)\end{array}$$

where subscript c, h, and r products, households, and regions, respectively. It should be noted that border prices (PM) are related to world price (PW) via exchange rate (ER), import tariffs (tm), and international market margins (RMARG) of imports (im).

The prices of goods not traded in The Gambia are adjusted by the equilibrium of aggregated demand and supply. Fixed world prices determine the price of tradable goods. Equations (2)–(6) demonstrate the differences in the relationship between consumer and border prices.

(2) $$\begin{array}{r}P{M}_{im}=P\overline{W}\times ER\times (1+RMAR{G}_{im})\times (1+T{M}_{im})\end{array}$$

The border price (PX) for exportable goods (ix) such as groundnuts, milk, rice, sorghum, and fish in the model is related to world price via exchange rates (ER), tariffs on exports (te), and international market margins:

(3) $$\begin{array}{r}P{X}_{ix}=\frac{P\overline{W}\times ER}{(1+RMAR{G}_{ix})\times (1+t{e}_{ix})}\end{array}$$

The prices of importable consumer goods are linked to border prices by consumer subsidy (CSUB) and market margin from the border to the market:

(4) $$\begin{array}{rl}P{C}_{im,urban}& =P{M}_{im}\times (1+IMAR{G}_{im})\\ & \times (1-CSU{B}_{im})\end{array}$$

The market margin from the border to the domestic market is IMARG. The prices of exportable consumer goods are linked to border prices by market margin from the market to the border:

(5) $$\begin{array}{r}P{C}_{im,urban}=\frac{P{X}_{im}\times (1+MAR{G}_{ix})}{(1+IMAR{G}_{ix})}\end{array}$$

The consumer prices in rural areas are lower than in urban areas due to transportation and marketing costs:

(6) $$\begin{array}{r}P{C}_{im,urban}=P{C}_{c,rural}\times (1+INMAR{G}_c)\end{array}$$

The study used various marketing margins, thus enabling the model to identify transportation costs from farm to rural areas (MARG), rural to urban settlements (INTMARG), and finally, from urban areas to international borders (IMARG).

This model assumes that different households are charged the same price. The differences only occur in the various regions. In the model, each household’s price index indicates changes in the weighted price of consumption bundles:

(7) $$\begin{array}{r}PINDE{X}_h=\sum _i[W_{h,r,i}\times \left(\frac{P{C}_{h,r,i}^1}{P{C}_{h,r,i}^0}\right)]\end{array}$$

where W refers to the household budget in each region. PC, as explained earlier, is consumer price in (7), illustrating two periods, i.e., the commencement and end prices of the study. PINDEX refers to the adjusted weight of consumption bundles.

Supply Equations

The supply of each household is determined by the land availability of the household for crop and livestock production. The land share (SH) allocated to a particular household is h, f is a function of the prices of all crops:

(8) $$\begin{array}{r}\log \left(S{H}_{h,f}\right)={\alpha }^s+\sum _f{\beta }^s\times \log \left(P{P}_{h.f}\right)\end{array}$$

where f is farm commodity. The sum of the share of land area may be equal to 1 or not. Land area is assumed to be endogenous even though it is not explicitly traded. The yield (YLD) for crops (f) in households (h) is a function of production inputs, price, and lands:

(9) $$\begin{array}{rl}\log \left(YL{D}_{h,f}\right)& ={\alpha }^y+\sum _f{\beta }^y\times \log \left(P{P}_{h,f}\right)+\sum _{in}{\gamma }^y\\ & \times \log \left(P{C}_{h,in}\right)\end{array}$$

The elasticities of land and yield are the coefficients α^s,β^s,α^y,β^y and γ^y. Total household supply (HSCR) to the market is arrived at, given the producer’s initial crop cultivation, land share for crop cultivation, and the yield. The model factor losses and other conversion issues:

(10) $$\begin{array}{rl}HSC{R}_{h,f}& =AREA\times S{H}_{h,f}\times YL{D}_{h,f}(1-los{s}_f)\\ & \times (1-con{v}_f)\end{array}$$

Consequently, the total supply of each commodity is computed by the summation of the household’s supply:

(11) $$\begin{array}{r}SC{R}_f=\sum _{h}HSC{R}_{h,f}\end{array}$$

Livestock production supply (HSLVSTK) is a function of animal production prices and fodder. The good group (af) is the animal feed produce:

(12) $$\begin{array}{rl}HSLVST{K}_h& ={\alpha }^{lvstk}+{\beta }^{lvstk}\times \log \left(P{P}_{h,lvstk}\right)\\ & +{\gamma }^{lvstk}\times \log \left(P{C}_{h,af}\right)\end{array}$$

The total supply of the production of livestock yields is in (13):

(13) $$\begin{array}{r}SL=\sum _{h}HSLVST{K}_h\end{array}$$

Production Demand Equations

Household demand for inputs (HDIN) is a function of input prices (PC) and production prices (PP):

(14) $$\begin{array}{rl}\log \left(HDI{N}_{h,in}\right)& ={\alpha }^{in}+\sum _f{\beta }^{in}\times \log \left(P{P}_{h,f}\right)\\ & +\sum _{in}{\gamma }^{in}\times \log \left(P{C}_{h,in}\right)\end{array}$$

where the superscript and subscribe (in) refer to inputs and feed for livestock. The aggregate demand for inputs yields (15):

(15) $$\begin{array}{r}DI{N}_{in}=\sum _{h}HDI{N}_h\end{array}$$

Consumption Equation

The model for demand for consumption goods (HS) by households in rural and urban is given below:

(16) $$\begin{array}{rl}\log \left(H{C}_{h,i}\right)& ={\alpha }_{h,i}^d+\sum _f{\beta }_{h,i}^d\times \log \left(P{C}_{h,i}\right)\\ & +{\gamma }_{h,i}^d\times \log \left(Y{H}_h\right)\end{array}$$

In (16) above, (i) is the commodity group of goods purchased by households, (YH) is household income, and (PC) is consumer prices.

(17) $$\begin{array}{r}\log \left(P_{h,r,i}\right)=\sum _{i}w\times \log \left(P{C}_{h,i}\right)\end{array}$$

The aggregate demand for goods yields (18):

(18) $$\begin{array}{r}TCO{N}_i=\sum _{h}H{C}_{h,i}\end{array}$$

Income Equations

Agricultural income (YHAG) for rural household communities is computed as the summation of crop revenue minus input costs:

(19) $$\begin{array}{rl}YHA{G}_h& =\sum _f(P{P}_{h,f}\times SC{R}_{h,f})+P{P}_{h,lvstk}\\ & \times HSLVST{K}_h-(P{C}_{i,in}\times DI{N}_{h,in})\end{array}$$

Household income (YH) is the summation of both agricultural income (YHAG) and non-agricultural income (YHNAG), which is endogenously determined. The equation shows that non-agricultural income is adjusted using the price index:

(20) $$\begin{array}{r}Y{H}_h=YHA{G}_h+YHNA{G}_h\times PINDE{X}_h\end{array}$$

Equilibrium Equation

An equilibrium economic condition is attained when the aggregate demand of households and animal fodder equals aggregate supply, i.e., domestic supply plus net imports.

(21) $$\begin{array}{r}SC{R}_f+M_f+STOCK{△}_f=CON{S}_f+FEE{D}_f\end{array}$$

Aggregate livestock demand equals the net import supply of domestic livestock.

(22) $$\begin{array}{r}LVSTK+M_{lvstk}=DI{N}_{lvstk}\end{array}$$

where imported quantities are (M), human consumption is (CONS), and animal feed is (FEED).

Results and Discussion

The Study Scenarios and its Expected Impact on The Gambia’s Agriculture Sector

The study adopts three scenarios: scenario 1−S1 (less pessimistic scenario) and scenario 2−S2 (pessimistic scenario), which factored the impact of hydro-meteorology and agricultural productivity in the model. Subsequently, due to the combined impact of S1 and S2, the third scenario is adopted and incorporated into the model, i.e., S3 (worst-case scenario), which is the Total Effect (S3 = S1 + S2). Hydro-meteorological conditions are among the main drivers of climate change in the Sahel region of West Africa. The country will experience an increased temperature due to global warming, whilst rainfall will substantially decline, given the vulnerability of the Sahel region to extreme climate conditions [37]. Similarly, other studies have shown that the Sahel hydro-meteorological conditions will negatively affect the region [38], [39]. The impact of S1 will lead to S2, which will exacerbate conditions of the agriculture sector from a less pessimistic to a pessimistic situation, given that soils of the region will swiftly degrade, whilst the region’s population will rapidly increase, thus jeopardising the food security and economic development of the Sahel [40], [41]. Thus, using S1 and S2 in the multi-market model gives a realistic picture of climate change’s impact on The Gambia’s Agriculture sector. S3 is the worst-case scenario that combines S1 and S2, as stated before.

Baseline Simulation Results

The baseline simulation represents a hypothetical situation in 2015, where the agriculture sector is characterised by the “status quo” with limited climate change impact. The trajectory of climate impact was not severe by 2015. Thus, 2015 is the base year of the study. At baseline, the historical impact of hydro-meteorology (reduced rainfall and increased temperature) is not acute in the sector. Consequently, agricultural productivity decline is not alarming, given that the pace of climate change was assumed to be in a “steady-state” with limited disruptions. The baseline projected results for food demand show that urban demand for agricultural commodities is 60% higher than rural demand. The major drivers (disposable income and population size) for food demand in urban communities are relatively higher. The highest demands for food commodities in The Gambia, as shown in Tables I and II, are rice, millet, cattle, chicken, maise, groundnuts, and goats. Tables I and II reflect the Gambians’ consumption pattern. Rice (staple food) is consumed in rural and urban settlements. Cattle and chicken are widely consumed as sources of protein. The baseline results show fish is the least consumed protein. This may be attributed to its exorbitant prices and the relative proximity of fish markets to the River Gambia or the Atlantic Ocean.

Table I. Projected Percentage Change in Food Demand in Urban and Rural Settlements for The Gambia

Settlement	Base (tons)	Scenario 1	Scenario 2	Scenario 3
		Hydro-meteorological impact	Agricultural productivity impact	Total effect of scenarios 1 & 2
Urban
Rice	6136.8	−5.30	−6.31	−11.62
Millet	3812.4	−1.84	−2.19	−4.04
Maise	735	−1.51	−1.80	−3.31
Groundnuts	624.6	−5.26	−6.26	−11.51
Sorghum	3776.4	−1.92	−2.29	−4.21
Cattle	2488.2	−3.53	−4.20	−7.73
Chicken	494.4	−1.14	−1.35	−2.49
Goats	202.6	−5.53	−6.58	−12.12
Sheep	283.2	−2.50	−2.97	−5.47
Fish	129.6	0.00	0.00	0.00
Rural
Rice	4091.2	−1.02	−1.22	−2.24
Millet	2451.6	−1.51	−1.80	−3.31
Maise	490	−1.40	−1.66	−3.06
Groundnuts	428.4	−1.01	−1.20	−2.21
Sorghum	2517.6	−0.17	−0.21	−0.38
Cattle	1658.8	−0.40	−0.47	−0.87
Chicken	292.6	−0.45	−0.54	−0.99
Goats	136.4	−1.10	−1.31	−2.42
Sheep	188.8	−1.75	−2.08	−3.83
Fish	84.6	0.00	0.00	0.00

Table II. Projected Percentage Change in Food Demand for The Gambia

Total	Base (tons)	Scenario 1	Scenario 2	Scenario 3
		Hydro-meteorological impact	Agricultural productivity impact	Total effect of scenarios 1 & 2
Rice	10228	−6.33	−7.53	−13.86
Millet	6354	−3.36	−4.00	−7.35
Maise	1225	−2.91	−3.46	−6.37
Groundnuts	1071	−6.26	−7.45	−13.72
Sorghum	6294	−2.09	−2.49	−4.58
Cattle	4147	−3.93	−4.67	−8.60
Chicken	844	−1.59	−1.89	−3.48
Goats	341	−6.64	−7.90	−14.54
Sheep	472	−4.25	−5.05	−9.30
Fish	216	0.00	0.00	0.00

The baseline results for seed demand, as shown in Table III, indicate that groundnut seed demand is 8784 tonnage, which is relatively higher than that of all other crops. This could be associated with groundnuts being The Gambia’s major cash crop. Rice seed demand is 4553 tons, and the demand for rice is expected to be higher than millet, maise, and sorghum because rice is the country’s staple food. Table III also shows animal feed demand; as of the base year 2015, chicken, cattle, and fish feed demands were 9, 6, and 3 tonnages, respectively. Finally, baseline results in Table IV show the rural producer price and urban consumer price of agricultural commodities as of the base year 2015. The consumer price shows that sheep, goats, and cattle are relatively more expensive in the order mentioned, whilst on the crops side, rice, millet, and maise are also relatively expensive in the order mentioned. When compared with the percentage increase from the producer price to the market price, sheep, goats, and cattle were 1.15%, 2.24%, and 2.27%, respectively. The percentage increase from the producer price to the market price for rice, millet, and maise shows an exorbitant pattern –37%, 45.15%, and 107%, respectively. It is for the aforementioned price disparity between the producer price and consumer high price that most of the domestically produced rice is exported rather than domestically consumed since it would not be affordable for the average Gambian.

Table III. Project Percentage Change in Demand for Seed and Feed

Seed/Feed	Base (tons)	Scenario 1	Scenario 2	Scenario 3
		Hydro-meteorological impact	Agricultural productivity impact	Total effect of scenarios 1 & 2
Rice	4553	3.25	4.39	7.64
Millet	4109	2.21	2.98	5.19
Maise	932	1.25	1.69	2.94
Groundnuts	8754	6.15	8.30	14.45
Sorghum	1266	1.48	2.00	3.48
Cattle	6	−6.22	−7.78	−14.00
Chicken	9	−9.34	−11.67	−21.01
Goats	1	−1.04	−1.30	−2.33
Sheep	1	−1.04	−1.30	−2.33
Fish	3	−3.11	−3.89	−7.00

Table IV. Projected Percentage Change in Urban Consumer Price and Rural Producer Price

Price	Baseline (USD/tonne)	Scenario 1	Scenario 2	Scenario 3
		Hydro-meteorological impact	Agricultural productivity impact	Total effect of scenarios 1 & 2
Urban Consumer Price
Rice	557.00	4.00	6.20	10.20
Millet	437.20	2.35	3.69	6.04
Maise	419.80	2.20	2.90	5.10
Groundnuts	343.40	1.50	2.15	3.65
Cattle	6,102.30	5.02	9.28	14.30
Chicken	4,589.60	6.25	10.35	16.60
Goats	6,196.60	4.35	5.25	9.60
Sheep	11,981.30	5.68	9.59	15.27
Sorghum	399.60	1.80	2.50	4.30
Fish	400.00	2.50	5.58	8.08
Producer Price
Rice	421.00	3.58	5.51	9.09
Millet	301.20	1.20	2.85	4.05
Maise	282.80	1.02	2.11	3.13
Groundnuts	270.40	1.00	1.92	2.92
Cattle	5,966.30	3.95	7.65	11.60
Chicken	4,453.60	4.25	8.62	12.87
Goats	6,060.60	2.16	3.91	6.07
Sheep	11,845.30	4.28	8.22	12.50
Sorghum	263.60	1.08	2.02	3.10
Fish	301.00	1.25	3.57	4.82

The Future Impact of Climate Change on The Gambia’s Agriculture Sector Due to Hydro-Meteorological Effects (Less Pessimistic Scenario)

The model simulation starts by adopting a less pessimistic situation for The Gambia’s agriculture sector by assuming that rainfall and temperature will impact the sector over time; the former will decline whilst the latter will increase. The impact of rainfall and temperature will affect the sector, as manifested by the model results. Tables I and II show that the decline in food will be more pronounced in urban communities. The commodities whose consumption will decline above 3% in the urban settlements will be sheep, rice, groundnuts, and chicken. Rice has been the country’s staple; its decline in consumption will increase urban food deprivation, and the decline in sheep and chicken, which are major sources of protein for urban dwellers. The situation in the rural communities shows that the three commodities whose decline will be in the highest will be sorghum, millet, and maise. The aforesaid crops are the main food source in rural areas. Hence, their decline in demand will lead to hunger and starvation.

From Table III, the seed demand given S1 will increase for all crops. The reason for this increased demand can be attributed to the decline in the output of crops illustrated in Fig. 1. The supply of the crops will be limited compared to their demand as shown in Tables I and II, hence the likelihood of storing excess seeds for the next planting season will be extremely low or impossible, thus increasing the demand for seed crops. It is for this cited reason that in 2020, international organisations like the FAO gave seeds (36.5 metric tons of maise, 72 metric tons of rice, 132.8 metric tons of groundnuts, etc.) aid to The Gambia [42], [43]. The looming continuous decline in food availability over demand will further aggravate the increased seed crop demand, thus worsening the situation over time. From Table III, the feed demand, given S1, declines over time. As a result of low rainfall over the years in the Sahel region, livestock and chicken production will gradually decline, thus causing the farmer to reduce their demand for animal feed. Table IV shows the percentage change in the price of agricultural commodities over time. It could be observed that prices will increase for all commodities over time as the impact of the hydro-meteorological effect is being felt across the sector. Commodity prices of poultry and livestock—chicken, sheep, and cattle—will increase by 6.25%, 5.68%, and 5.02%, respectively. Commodity prices of crops—rice, millet, and maise—will increase by 4%, 2.35%, and 2.2%, respectively. Fish prices will also increase by 2.5%. Thus, the increase in the aforesaid prices of livestock, poultry, crops, and fish will reduce their demand, as seen in Tables I and II. Consequently, the food bundle for The Gambia will continually diminish, rendering the country’s food insecure over time.

Fig. 1. Projected percentage change in crop yields by 2040.

The Future Impact of Climate Change on The Gambia’s Agriculture Sector due to a Decrease in Agricultural Productivity (Pessimistic Scenario)

Following a simulation of hydro-meteorological impact (S1), the study further investigated a more pessimistic scenario emanating from S1, leading to low agricultural productivity (S2) of the sector over 25 years. Fig. 1 illustrates the projected percentage change in crop yields by 2040 for the five main crops produced in The Gambia—rice, millet, maise, groundnuts, and sorghum. Given S2, it is observed that all yields will decline. Rice will decline by 8.9%, groundnuts by 7%, millet by 5.1%, sorghum by 4.9% and maise by 4.1%. The decline in yields due to low productivity will be further compounded by problems such as insufficient rainfall, inadequate access to credit facilities, limited resource base, etc. The consequences of the foregoing will hamper the country’s efforts at attaining food security, hence the increasing dependency of The Gambia on food imports and aid to feed its vulnerable populace.

An adaption of S2 will deteriorate The Gambia’s international trade position since adverse rainfall and temperature will negatively impact crop yields (Fig. 1), livestock, and fish production, culminating in low agricultural productivity, leading to a gradual decrease in exports of agricultural commodities. As the study shifts from S1 to S2, exports of the country’s main cash crop, groundnuts, will decline by 26%; fish and milk will decline by 20.8%, rice by 16%, and sorghum by 7.8% by 2040 (Fig. 2A). The decline in exports will significantly reduce the country’s foreign exchange earnings, thus reducing government revenues in funding essential services and development projects. On the import side, Fig. 2B illustrates that due to S2, imports will continually rise over time, thus increasing The Gambia’s dependence on basic food necessities that are heavily consumed but whose consumption will gradually decline, as shown in Tables I and II. This situation will increase hunger, malnutrition, and food deprivation among the dwellers, thus aggravating the impact of climate change on the agriculture sector.

Fig. 2. Projected percentage change in exports and imports by 2040: projected percentage change in exports, (B) Projected percentage change in imports.

The Combined Effects of Hydro-Meteorology and Agricultural Productivity Impacts on The Gambia’s Agriculture Sector (Worst-Case Scenario)

The 3^rd and final scenario are the worst-case scenario, which combines S1 and S2, thus illustrating the full extent of the impact of climate change on the agriculture sector for 25 years. As climate impact aggravates, the agriculture sector will experience low food demand, a decline in yields, an increase in imports, a decrease in exports, an increase in seed demand, a decrease in feed demand, increased prices, etc. Fig. 3 shows the general price level as measured by the Consumer Price Index (CPI) in the economy, given the combined effect of S1 and S2 leading to S3. Some of the basic food necessities whose prices will increase substantially are cattle by 36%, chicken by 24.75%, sheep by 22.50%, rice and goats by 20.25%, fish by 15.75%, etc. The general price increase will trigger an economic-wide price rise, leading to a decrease in real income, rendering the vulnerable population poorer and diminishing their purchasing power in the long term.

Fig. 3. Projected CPI for all scenarios.

Given all the study scenarios, S1, S2, and S3, it was observed that the negative impact of rainfall and temperature (S1) on the agriculture sector would trigger the sector to experience low productivity (S2) over the years. The combined effects of S1 and S2 will lead to a total effect (S3), which will have dismal consequences for the sector, as shown in Fig. 4. The food self-sufficiency ratio for all agricultural commodities will continually decline over 25 years. Given that The Gambia was food-dependent before 2015, the looming climate crisis will further exacerbate the situation. As shown in Fig. 4, sheep dependency will increase by 10%. The other highly dependent commodities are cattle, millet, groundnuts, chicken, and fish–8.8%, 8%, 7.9%, 7.5%, and 6.8%, respectively. The foregoing portrays an image of a pending food catastrophe if appropriate policy measures are not implemented to address the country’s food insecurity caused by a reduction in yields, a decrease in demand and consumption, an increase in prices, and high food importation.

Fig. 4. Food self-sufficiency ratio in percentage by 2040.

Sensitivity Analysis

To estimate the sensitivity of the result elasticities, the study doubled or cut to half the own-price, cross-price, and income elasticities. Results for the supply side were found to be only moderately sensitive by doubling or cutting to half the supply parameters, particularly for crop elasticities. On the other hand, the study found that the results for the demand side were moderately sensitive to doubling or cutting to half the demand parameters.

Conclusion and Policy Recommendations

The results are comprehensively captured in the schematic multi-market model of climate impact on The Gambia’s agriculture, as illustrated in Fig. 5. The illustration shows the general effects of three different realistic scenarios on the sector. Initially, the model factored in a decrease in rainfall and an increase in temperature, thus adopting a hydro-meteorological scenario (S1). Given S1, over time, the sector will eventually experience low productivity (S2), which will negatively impact the crop, livestock, poultry, and fisheries sub-sectors, thus culminating in the total effect (S3) of the full impact of climate change on the sector. The caveat of the study shows holistically the severity of the damage the climate crisis would impact on the sector, leading to a decrease in demand and consumption. While animal feed demand will decrease, seed demand will increase. Against that backdrop, crop production will decline. The results for trade effects show that importation will increase whilst exportation will decline over the years. The general price level is expected to increase as rural producer prices increase, leading to an increase in consumer prices. Finally, the effects of all the scenarios will cause food insecurity, thus diminishing the resilience of The Gambia’s food system, rendering it vulnerable. As a result, the country’s food self-sufficiency ratio will gradually decline, as manifested by the results.

Fig. 5. Schematic multi-market model of climate change impact on The Gambia’s agriculture.

The recommendations of the study are as follows:

1. The government should increase its investment in agriculture, especially in crops and livestock that are resilient to adverse climate conditions.

2. In light of the decrease in yields over the years, there is a need to boost production across all regions by providing support to farmers.

3. There is an urgent need for The Gambia to limit its appetite for importation, thus preserving the country’s limited foreign earnings on development projects.

4. The government, in partnership with development and donor agencies, should establish projects and programmes that are pro-poor to aid the fight against hunger, malnutrition and food deprivation due to the looming climate crisis.

5. There is a need to invest in irrigated farming, thus moving from rain-fed undependable agriculture to irrigated dependable agriculture in light of the decline in rainfall and crop yields in The Gambia.