1. Introduction

2. Materials and Methods

2.3. Methods

2.3.1. Estimation of NPP

The model built on the basis of NDVI, SR, and NPP, along with the light use efficiency method [50], was used to assess carbon sequestration over the time series. The model evaluation was primarily based on the CASA algorithm (Carnegie-Ames-Stanford approach) [23]. NDVI and SR were applied to calculate the fraction of incident photosynthetically active radiation (FPAR) and the absorbed incident photosynthetically active radiation (APAR), which allowed for the estimation of NPP [51].

$$NPP=APARxLUE$$

(1)

$$NPP=FPAR\times PAR\times LUE$$

(2)

$$LUE=\mathsf{\epsilon }\max \times f(T1,T2,W)$$

(3)

The light use efficiency factor (LUE) is a function of T1, T2, and W, which are limits imposed by two temperatures and water stress on the energy use rate. εmax is the maximum light use efficiency under ideal conditions, with a value adopted at 0.389 g C/MJ [23].

According to Hatfield, Asrar [52] et Los, Justice [53], FPAR has a linear relationship with NDVI or SR. Thus, FPAR can be calculated from NDVI and SR using the following equations [43].

$$FPARndvi\left(x,t\right)={\displaystyle \frac{\left(NDVI(x,t)-NDVIi,min\right)\times \left(FPARmax-FPARmin\right)}{(NDVIi,max-NDVIi,\mathrm{m}\mathrm{i}\mathrm{n})}}+FPARmin$$

(4)

$$FPARsr(x,t)={\displaystyle \frac{\left(SR(x,t)-SRi,min\right)\times (FPARmax-FPARmin)}{(SRi,max-SRi,\mathrm{m}\mathrm{i}\mathrm{n})}}+FPARmin$$

(5)

FPARmax and FPARmin are assumed to be 0.95 and 0.001, respectively.

FPAR estimates based on NDVI are generally higher than actual values, while estimates based on SR tend to be lower than actual values [53]. To reduce errors in FPAR evaluation, averaging the results derived from NDVI and SR has been strongly suggested [54]. In this regard, the following equation was applied:

$$FPAR\left(x,t\right)=\propto FPARndvi+\left(1-\propto \right)FPARsr$$

(6)

(∝ = 0,5 is the adjustment factor for NDVI and [55])

According to [56], for clear skies and tropical countries, PAR is 0.51.

The light use efficiency factor (LUE) T1 reflects the limitation imposed by the biochemical action of plants on photosynthesis at low and high temperatures [23,57].

$$T1=\mathrm{0,8}+\mathrm{0,02}\times T_{opt}–\mathrm{0,0005}\times {T_{opt}}^2$$

(7)

where Topt is the average monthly temperature in the month when NDVI reaches its maximum in a given year. T2 shows the declining trend of effective light use as ambient temperature increases or decreases relative to the optimal temperature value (Topt). It can be calculated using the following formula:

$$T2=\mathrm{1,1814}/(1+e^{\mathrm{0,2}\times \left(T_{opt}-10-T\right)})/(1+e^{\mathrm{0,3}(-T_{opt}–10–T)})$$

(8)

The water stress factor reflects the influence of water effectively used by plants for an optimal energy conversion rate. As the available water in the environment increases, W also increases, ranging from 0.5 (extremely arid condition) to 1 (very humid condition).

$$w=\mathrm{0,5}+\left(0.5\times {\displaystyle \frac{ETR}{ETP}}\right)$$

(9)

Potential evapotranspiration (ETP) corresponds to moist soil and plants with enough water. For this study, the method proposed by [58] which is based primarily on air temperatures, was used.

$$ETP=16 {\left(10 {\displaystyle \frac{t}{I}} \right)}^a. Ki=( {{\displaystyle \frac{t}{5}} ) }^{\mathrm{1,5}} et I=\sum \begin{array}{c}12\\ I\end{array}ia={\displaystyle \frac{\mathrm{1,6}}{100}}I+\mathrm{0,5}$$

(10)

ETP is the potential evapotranspiration for the considered month t

t=average monthly temperature for the considered month;

a = 6,75.10-7 × I3 - 7,71.10-5 × I2 + 1,79.10-2.I + 0,49239

K= monthly adjustment coefficient, which depends on the latitude of the area (Table 1)

i= monthly heat index calculated from average monthly temperatures

I= annual heat index, which is the sum of monthly heat indices.

Table 1. Monthly Values of the K Coefficient.

Table 1. Monthly Values of the K Coefficient.

North latitude	J	F	M	A	M	J	J	A	S	O	N	D
5	1,02	0,93	1,03	1,02	1,06	1,03	1,06	1,05	1,01	1,03	0,99	1,02
10	1	0,91	1,03	1,03	1,08	1,06	1,08	1,07	1,02	1,02	0,98	0,99

The actual evapotranspiration or runoff deficit was calculated using Turc's formula:

$$ETR={P\left[\left(\mathrm{0,9}+{\displaystyle \frac{P^2}{l^2}}\right)\right]}^{-\mathrm{0,5}}$$

(11)

where P is the average annual rainfall (in mm) and ETR represents the actual evapotranspiration (in mm/year). The parameter L, a function of the average annual temperature t (in °C), is expressed as follows:

$$L=300+25t+\mathrm{0,05}{t}^3$$

(12)

2.3.2. Validation of NPP Estimation

The 500m resolution MODA17A3HGF v006 reference data from the year 2022 for Togo were acquired to validate the estimated NPP of Togo using the model applied in this study. The absence of ground-measured data at the scale of Togo explains the choice of the widely used MODA17 reference data by other authors to validate NPP estimation [59]. When ground reference data are lacking, the use of satellite-derived datasets such as MODIS can serve as an alternative for validation [60]. However, it is essential to account for potential biases and uncertainties associated with using these reference data. The correlation between NPP and the MODA17 reference NPP was used to validate the results. Figure 2 clearly shows that the estimated NPP corresponds to the simulated NPP (r² = 0.8).

2.3.3. Impacts of Climate Change and Land Use

The NPP of vegetation is the production per unit of land area. Therefore, the total production (PT) is calculated as follows:

$$PT=Sup\times PPN$$

(13)

where Sup = area of the study zone.

The change in PT between two different dates (2000 and 2022) will be estimated as:

$$∆PT={PT}_{2022}-{PT}_{2000}={Sup}_{2022}\times {PPN}_{2022}-{Sup}_{2000}\times {PPN}_{2000}$$

(14)

Since Sup2022 = Sup2000 + ∆Sup and NPP2022 = NPP2000 + ∆NPP, we have:

$$∆PT=\left({Sup}_{2000}+∆Sup\right)\times \left({PPN}_{2000}+∆PPN\right)-{Sup}_{2000}-{PPN}_{2000}$$

(15)

$$∆PT={Sup}_{2000}\times ∆NPP+∆Sup\times \mathrm{N}{PP}_{2000}+∆Sup\times ∆\mathrm{N}PP$$

(16)

The work of [61] linked this to three parameters: climate change, land use changes, and the interaction between climate change and land use. Indeed, the variation of PT according to the last equation is a function of the induced change in NPP (Sup_2000 × ∆NPP), the change in area (∆Sup × NPP_2000), and the interactions (∆Sup × ∆NPP).

The relative contribution of the three parameters to the change in PT (∆PT) can be estimated as follows:

$${\mathrm{Ƞ}}_{clim}={\displaystyle \frac{{Sup}_1\times ∆NPP}{\left|∆PT\right|}}\times 100$$

(17)

$${\mathrm{Ƞ}}_{land}={\displaystyle \frac{∆Sup\times {NPP}_{2000}}{\left|∆PT\right|}}\times 100$$

(18)

$${\mathrm{Ƞ}}_{intersect}={\displaystyle \frac{∆Sup\times ∆NPP}{\left|∆PT\right|}}\times 100$$

(19)

where Ƞ_clim, Ƞ_land, and Ƞ_intersect are respectively the contributions of climate change, land use changes, and their interactions; |∆PT| represents the absolute value of ∆PT.

2.3.4. Correlation Analysis between NPP and Climatic Variables

The correlation analysis was conducted to evaluate the relationships between NPP and climatic variables. Several studies have demonstrated the necessity of this relationship in studies on ecosystem performance. Using Pearson's correlation coefficient R, the linear relationship between two variables (NPP and a climatic variable, an input parameter of the CASA model in conjunction with the Thornthwaite model) was evaluated. This coefficient measures the strength of the association and ranges from -1 to +1 [62]. The correlation coefficient was calculated between the climatic parameters (precipitation, temperature, actual evapotranspiration, potential evapotranspiration, effective light use factor) and the NPP.

$$R={\displaystyle \frac{\sum _{i=1}^n\left(X_i-\overline{X}\right)\left(Y_i-\overline{Y}\right)}{\sqrt{\sum _{i=i}^n{\left(X_i-\overline{X}\right)}^2}\sum _{i=1}^n{\left(Y_I-\overline{Y}\right)}^2}}$$

(20)

where R is the correlation coefficient between variables X and Y. Xi and Yi indicate X and Y of year i, and represent the multi-year average of X and Y, respectively. n is the number of years of the study. The t-test method is used to test the significance of the correlation coefficient R, and p<0.05 is considered statistically significant.

3. Results

3.2. Spatial Dynamics of Natural Productivity in Togo

3.2.1. Characteristics and Spatial Distribution of Natural Productivity in Togo

Figure 6 shows graphically the distribution of NPP in 2022. According to the figure, NPP in that year varies from 452.08 to 11510.9 Kg C. ha-1, with a total annual NPP of 28.51 Pg C.

The distribution of NPP from the South to the northeast is unevenly distributed (Figure 4). The Atakora mountain range, which crosses the country diagonally, remains the most productive area of the country. In contrast, the landscapes of the Bombouaka Cuesta, the Kara River basin, and the coastal area remain the least productive areas of the country.

3.2.2. Detection of NPP Changes in Togo

Between 2000 and 2022, productivity changes were detected at the pixel level (Figure 7). Some areas, such as the savanna region, the central-eastern zone, the Nangbéto dam area, and the lagoon area, sequester less carbon in 2022 than in 2000 (Figure 6). These landscapes have experienced a loss of NPP up to 2417.65 Kg C.ha⁻¹.year⁻¹. Gains in terms of carbon sequestration are mainly observed in the dense semi-deciduous forest areas of the southwestern mountains. In these areas, the increase in NPP of the pixels between 2000 and 2022 goes up to 5177.24 Kg.C.ha⁻¹.year⁻¹.

3.2.3. Spatio-Temporal Dynamics of Ecosystem NPP

The analysis of land use types from 2000 and 2022 derived from the post-classification of GLCLU images showed that the present land cover units are Forests, Savanna Mosaics, and Crops/Agroforestry Parks/Fallow, marshy vegetation, water bodies, and Habitations/Infrastructure/Quarries (Figure 8). The statistics for the areas occupied by each land cover unit are recorded in Table 1.

Table 1. Area Statistics of Land Cover Units in 2000 and 2022.

Table 1. Area Statistics of Land Cover Units in 2000 and 2022.

Land Use Units	Year 2000			Year 2022			Trends
	km2	ha	%	km2	ha	%	ha	%
Forests	25464	2546445	44,99	19469	1946876	34,40	-599569	-23,55
Savanna Mosaic	19527	1952739	34,50	13972	1397218	24,69	-555521	-28,45
Crops/Agroforestry Parks/Fallow	9360	936003	16,54	20014	2001382	35,36	1065379	113,82
Swamp Vegetation	708	70802	1,25	679	67893	1,20	-2909	-4,11
Water Bodies	247	24658	0,44	266	26563	0,47	1905	7,73
Dwellings/Infrastructure/Quarries	1294	129400	2,29	2201	220114	3,89	90713	70,10
	56600	5660047	100	56600	5660046	100

The highest average annual NPP observed in 2000 was in forests, which was 4813.79 kg C.ha-1.year-1. It increased to 7050.33 kg C.ha-1.year-1 by 2022 (Table 2). Some ecosystems such as Forests, Savanna Mosaic, Wetlands, and Water Bodies saw their average annual NPP increase between 2000 and 2022, while Croplands/Agroforestry/Pastures and Residential/Infrastructure/Quarries became less productive in terms of NPP accumulation.

In general, it appears that all land use units have improved their performance in terms of NPP accumulation despite land use changes. In 2000, forests had a total annual NPP value of 12.25 Pg C.yr-1, representing 48.13% of the country's total annual NPP. Twenty-two years later, these ecosystems saw their performance increase to 13.73 Pg C.yr-1 despite a 23.55% reduction in forest area. It is noteworthy that despite the increase in the amount of accumulated NPP, forests have lost their share, decreasing from 48.13% in 2000 to 37.95% in 2022 (Table 3).

3.3. Impacts of Climate Change and Land Use Change on Total Production

Between 2000 and 2022, 32.08% of Togo's land area experienced changes in land use. Table 4 summarizes the areas of land use units that remained unchanged and those that underwent modifications. A significant change in the allocation of savanna mosaics is observed. Specifically, 14.31% (199,945.41 ha) and 11.18% (156,129.21 ha) of savanna mosaics have been converted respectively into Crops/Agroforestry Parks/Fallow Lands and into Forests. Forests converted into agricultural land represent a large proportion, with 15,689.06 ha, while the conversion of savanna mosaics into forests is also noted (156,129.21 ha), indicating reforestation efforts or natural regeneration.

During the time span 2000-2022, the estimates of NPP indicate an increase in Total Productivity (PT) of 3.05 Pg C. This variation in PT is influenced by climate change, which has a positive impact (58.27%), as well as changes in land use (188.63%). The intersection between these two parameters, however, negatively impacts (-146.90%) the variation in PT (see Figure 9).

Figure 9. Impacts of climate change, land use changes, and their intersection on the variation in total production.

Figure 9. Impacts of climate change, land use changes, and their intersection on the variation in total production.

Between 2000 and 2022, climate change has had a more positive impact on the variation in the productivity of Savanna Mosaics (471.74%), whereas land use changes have had a more positive impact on the variation in the productivity of Crops/Agroforestry Parks/Fallow Lands (950.39%). The intersection of these two parameters has had a more negative impact on the variation in the productivity of Settlements/Infrastructures/Quarries (-200.02%) (Table 5).

3.4. Correlations between NPP and Climatic Parameters

The correlation coefficients (R) between NPP and climatic parameters reveal positive links of varying intensities depending on the two types of coupled data (Figure 9). This figure presents the relationships between NPP and various climatic parameters: Light Use Efficiency (LUE), Actual Evapotranspiration (ETR), Potential Evapotranspiration (ETP), precipitation, and mean temperature. The graphs show the linear regressions associated with each relationship, with their equations and determination coefficients (R²).

NPP also shows a significant positive correlation with ETR, with an R² of 0.44. This means that 44% of the variance in NPP is explained by actual evapotranspiration. This relationship indicates that higher levels of ETR are linked to increased productivity. A moderate positive correlation is observed between NPP and ETP, with an R² of 0.11. Although this relationship is statistically significant (p = 0.049), it is weaker than those observed with ETR and LUE.

Precipitation shows a positive correlation with NPP, with an R² of 0.21. This means that 21% of the variance in NPP can be explained by precipitation. This relationship suggests that precipitation influences primary productivity, but not as much as LUE or ETR. Temperature shows the weakest relationship with NPP, with an R² of 0.052, indicating that about 5% of the variance in NPP is explained by temperature. This correlation is weak and not statistically significant (p = 0.18), suggesting that mean temperature has little direct impact on NPP compared to the other factors studied.

Figure 9. Results of the Correlation Analysis Between NPP and Climatic Variables.

Figure 9. Results of the Correlation Analysis Between NPP and Climatic Variables.

Figure 10 highlights the interannual variability of net primary production (NPP) and precipitation. Generally, there seems to be some correlation between variations in NPP and precipitation. For instance, peaks in precipitation in 1994 and 2016 coincide with peaks in NPP, suggesting that rainy years tend to be associated with higher net primary production. However, this relationship is not perfect. For example, in 1998, a peak in precipitation did not correspond to a peak in NPP, and in 2011, a peak in NPP is not associated with a peak in rainfall. This indicates that factors other than precipitation also influence NPP.

4. Discussion

5. Conclusion

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