Issue 4 (204)/2025

ALSE and ACS Style
Agbor, C.F.; Dada, B.M.; Adegboyega, S.A.-A. Solar energy interception and maize yield variability in the Forest-Savanna Transition Zone, Oyo State, Nigeria. Journal of Applied Life Sciences and Environment 2025, 58 (4), 549-564.
https://doi.org/10.46909/alse-584192

AMA Style
Agbor CF, Dada BM, Adegboyega SA-A. Solar energy interception and maize yield variability in the Forest-Savanna Transition Zone, Oyo State, Nigeria. Journal of Applied Life Sciences and Environment. 2025; 58 (4): 549-564.
https://doi.org/10.46909/alse-584192

Chicago/Turabian Style
Agbor, Chukwuka Friday, Boluwatife Mosebolatan Dada, and Suleiman Abdul-Azeez Adegboyega. 2025. “Solar energy interception and maize yield variability in the Forest-Savanna Transition Zone, Oyo State, Nigeria” Journal of Applied Life Sciences and Environment 58, no. 4: 549-564.
https://doi.org/10.46909/alse-584192

Solar energy interception and maize yield variability in the Forest-Savanna Transition Zone, Oyo State, Nigeria

Chukwuka Friday AGBOR^1*, Boluwatife Mosebolatan DADA² and Suleiman Abdul-Azeez ADEGBOYEGA³

¹Environmental Modelling and Biometrics, Forestry Research Institute of Nigeria, Jericho, Ibadan, Oyo State, Nigeria

²Department of Meteorology and climate Science, School of Earth and Mineral Science, Federal University of Technology, Akure, Nigeria; email: boludada77@gmail.com

³Remote Sensing and Geoscience Information Systems, School of Earth and Mineral Science, Federal University of Technology, Akure, Nigeria; email: saadegboyega@futa.edu.org

*Correspondence: chukwuka_friday@yahoo.com

Received: May 18, 2025. Revised: Oct. 09, 2025. Accepted: Nov. 12, 2025. Published online: Dec. 23, 2025

ABSTRACT. The diverse agricultural activities in the Forest-Savannah Transition Zone of Oyo State, Nigeria, provides an excellent opportunity to study the effect of photosynthetically active radiation ( ) interception on maize yield variability. The intercepted PAR was derived from Landsat 8 imagery, using the Beer-Lambert spectrophotometric principle. Maize yields were obtained by direct field measurements and a crop growth model (CGM). Yields were also projected using predictive models developed from the regression operations between yields and fraction of absorbed energy, enhanced vegetation index, normalised difference vegetation index, and chlorophyll vegetation index. The impact of surface solar energy on yields was determined. Results revealed that the mean fraction of PAR ranged 286–  across plots. Average measured yield was 452 kg/plot, while the mean estimate by CGM was 448 kg/plot. Mean projected yield ranged 443.366– 506.753 kg/plot. The model and field-based yields were closely related with an average The multiple regression model outperformed others, with a standard error of 6.69. The yields across the plots increased with increased . The findings underscore the potential of integrating satellite-derived biophysical indicators with absorbed solar radiation estimates in crop yield modeling. The finding that absorbed solar energy is a key yield driver underlines the importance of considering climate-driven variables in future climate-resilient crop yield prediction models.

Keywords: agricultural activities; crop yield prediction models; maize yield variability; photosynthetically active radiation; vegetation index.

 

INTRODUCTION

Research scientists and relevant stakeholders have called for increased food production to reduce the risks of food insecurity at local and global levels (FAO, 2017; Long et al., 2003; Zhang et al., 2015). Increasing food production requires the understanding of the relationship between environmental factors and crop growth (Yang et al., 2022). Solar energy availability is a major determinant of crop productivity (Rajan et al., 2019) The component of solar energy or radiation intercepted by the crop, i.e., the photosynthetically active radiation (PAR), serves as the primary energy source that drives photosynthesis in plants, thereby influencing crop growth and productivity (Ying et al., 2018; Zhu et al., 2010). PAR influences crop physiology and productivity (Murchie et al., 2013; Peng et al., 2020). Kromdijk et al. (2016) emphasised that an adequate amount of PAR is important for increasing the rate of photosynthesis, carbon absorption, and biomass buildup across different crops.

However, the quantity and quality of this energy significantly affect crop yield (Li et al., 2024; Pashiardis et al., 2017; Wane et al., 2022). It is therefore important to assess the role of intercepted solar energy to increase agricultural productivity (Himani et al., 2020; Qin et al., 2018). Ran et al. (2021) clearly stated that understanding how solar energy affects crop yields can help farmers adapt their practices and select appropriate crop varieties to maintain productivity under changing solar energy conditions.

Studies have identified the need to develop a method of estimating and projecting crop yields to ensure agricultural sustainability (Ahmed et al., 2023; Garrachón-Gómez et al., 2024; Tewes and Schellberg, 2018). This involves developing crop yield projection models that can help farmers more efficiently apply inputs like water, fertilisers, and pesticides, which leads to reduced waste, lower costs, and optimised resource usage (Mulla, 2013). It has been reported that, by projecting yields, farmers can better plan their labour needs, reduce unnecessary labour costs, and ensure availability during critical periods (Science News Today, 2025). Yield models can also provide early warnings of potential crop failures due to adverse weather conditions, pests, or diseases. This helps in proactive decision-making to mitigate losses (Lobell et al., 2008; Weston et al., 2024). These authors added that crop yield models can allow for better planning in the food supply chain, ensuring that demand is met and reducing the risk of food shortages. The models can also help farmers and agribusinesses plan for market demand, reducing the risk of overproduction or underproduction (Mulla, 2013). Accurate yield models are essential for crop insurance schemes, allowing for more accurate premium setting and risk assessment to protect farmers from financial losses (Kandel, 2020; Weston et al., 2024). By optimizing input usage, yield models help reduce the environmental footprint of farming, lower emissions, and prevent overuse of water and chemicals (Mulla, 2013; Weston et al., 2024).

Our research used empirical and geospatial techniques to (i) examine absorbed solar energy variability, (ii) explore strategies to estimate and project maize yields, and (iii) assess the impacts of solar energy on the crop. The outcome of this study will highlight the value of energy-based metrics in yield modeling, which is often underrepresented in the study area. The understanding of how solar energy absorbed by maize crop influences yield will enhance agricultural sustainability in the face of continued climate dynamics.

 

MATERIALS AND METHODS

Study area

The study area comprising Akinyele, Ido, and Ibarapa communities is within 7°15ꞌ and 7°55ꞌ N and 30°0ꞌ and 3°3ꞌ E in western Oyo State, Nigeria. The area is approximately 2,496 km² in geographical size and falls within the Forest-Savanna Transition Zone (Ogundele et al., 2012).

Most of the land lies at elevations ranging 120–200 m above sea level. The predominant occupation of the people is farming. The soil type is lateritic clay, loamy sand, and sandy loam. The vegetation consistes of mixed deciduous and semi-deciduous forests, with grassland and shrubs in open areas.

The region has a tropical climate with two distinct seasons, the rainy season and the dry season, which offer the opportunity to assess the radiant flux in the region and its impact on agricultural productivity.

 

 

Data collection

Surface elevation data (Asterdem) and Landsat satellite images from 2023 were downloaded from the official website of US Geological Survey. The study area is in Landsat path 191 and row 55, with spatial resolution of 30 × 30 m with the exception for the thermal infrared band 10, which has 100-m resolution bands (Makinde et al., 2019). In situ crop biomass and actual crop yields (Ya) in kg per plot were directly obtained from 150 randomly selected maize plants in 15 plots within the study area. Solar energy data were taken from agrometeorological stations located within the study area.

The study estimated solar energy and maize yields by using remote sensing and ground data. The solar energy values were derived from Landsat imagery using solar radiation models. Maize yields were estimated with direct field measurements and a Crop Growth Model (CGM). Yields were projected using key parameters such as fraction of absorbed solar energy or radiation (fAPAR) and selected vegetation indices.

To obtain quantitative information, the images were converted to reflectance measures (Equation 1) (Giannini et al., 2015; Maciel et al., 2023).

where ρλ is the planetary top of atmosphere (TOA) reflectance (unitless), π is the mathematical constant approximately equal to 3.14159 (unitless), TOArLλ is the spectral radiance at the sensor’s aperture [w/(m² sr µm)],  d2 is the earth-sun distance (astronomical unit), ESUN is the mean exoatmospheric solar irradiance [w/(m² sr µm)], and θ_SZ is the solar zenith angle (degree). 

The cosine of this angle is equal to the sine of the sun’s elevation θSE (Equation 2).

Estimation of solar energy using a solar radiation model

The global solar energy l0 striking the earth’s surface was calculated (Equation 3).

where d_s is the incoming shortwave (Equation 4) and d_l is the incoming longwave (Equation 7) (Ryan et al., 2017).

where t is the transmissivity of the atmosphere as a function of the elevation above sea level, s is the solar constant (820W/m²) specific to the study location and an average over 30 years, is the eccentricity correction factor of the earth’s orbit about the sun (Equation 6), and cos_z is the cosine of the solar zenith angle z.

where h is the elevation above sea level.

where  is the Julian day of the year. The incoming long wave is given as:

where e_a is air emissivity (Equation 8) and T_s is

where T_a is the air surface temperature (Equation 9), N_r, G.

where TOAr is top of atmosphere radiance, k₁ is calibration constant 1 (666.09 for ETM+) and (774.89 for OLI band 10), and K₂ is calibration constant 2 (1282.71 for ETM+) and (1321.08 for OLI band 10).

Maize yield estimation using the CGM

The key parameters of the CGM include fraction of absorbed solar energy, crop biomass, harvest index, and energy use efficiency. The fraction of absorbed radiation per plot was estimated using the Beer-Lambert spectrophotometric principle, which is a mathematical relationship that describes the absorption of light by a material (Colin et al., 2005; Skoog et al., 2017). This calculation helps in understanding the distribution of solar energy within the visible region of the electromagnetic spectrum. It is crucial for understanding fundamental plant physiological processes such as photosynthesis and development. Crop productivity depends on its ability to intercept incident solar energy (Tewes and Schellberg, 2018). The total energy intercepted by the plant, based on Beer-Lambert model, can be expressed as (Equation 10):

where α is the amount of energy absorbed, l0 represents the energy flux density across spectral ranges and fAPAR is the fraction of energy intercepted (Equation 11) (Campillo et al., 2012; Colin et al., 2005; Schaefer et al., 2014):

where k is the extinction coefficient (also known as the molar absorptivity or molar extinction coefficient) of maize that measures how strongly a substance absorbs light at a particular wavelength (Emil et al., 2002). A higher extinction coefficient indicates stronger absorption of light by the substance at a given concentration and wavelength, while a lower value indicates weaker absorption. The  value of maize is estimated to be between 0.40 and 0.65 (Adel et al., 2006; Josefna et al., 2021), with a mean value of 0.53, which was adopted in our study. LAI is the leaf area index (Equation 12) (Norman et al., 2002; Zheng and Moskal, 2009).

where NDVI is the Normalised Difference Vegetation Index (Equation 13).

where nir_ref is near-infrared reflectance and red_ref is red reflectance. NDVI values typically range between -1 and 1; the higher the value, the healthier and denser the vegetation (Chen et al., 2005; Siqi et al., 2019; De la Iglesia Martinez et al., 2023; Gascon et al., 2016; Yasmina et al., 2019). The LAI was corrected using canopy coverage CC (Equation 14).

where n_veg is the number of vegetation pixels and n_0 is the number of bare surface pixels (Li et al., 2020)

Crop biomass is usually expressed in terms of dry weight per unit area (e.g., kg/ha). It is an important measure in agriculture and ecology as it provides an indication of the productivity of a crop and can be used to assess crop health, growth rates, and potential yield. To calculate total crop biomass, six plants were randomly selected from each plot of 30 × 30 m for a representative sample (Murrell and Chivenge, 2023). The planting space was 50 cm along the row and 75 cm between rows, and each plot had 2,400 plants. The plants were dried on the farm. The dried stems, leaves, and cobs were weighed together, and the weight was recorded in kg. The total biomass per plot was calculated (Equation 15 and Equation 16).

where b is the total biomass per plot, and x is the mean dry weight of the six sampled plants from the plot. The harvest index (hi) is the ratio of dry weight of grains per plot to crop biomass per plot (Himani et al., 2020) (Equation 17).

Energy or radiation use efficiency (RUE) is a measure of the efficiency by which plants convert absorbed PAR into biomass (Equation 18).

where  is typically measured in units of mass (e.g., grams or kilograms) per plot and APAR is the absorbed PAR, which is the amount of light energy in the photosynthetically active region (400-700 nm) of the electromagnetic spectrum absorbed by the plant canopy, usually measured in mega joules, joules, or moles per unit area. Therefore, the CGM (Himani et al., 2020; Tewes and Schellberg, 2018; Stöckle and Kemanian, 2009) is expressed as:

Projecting crop yield

Yield projection models were developed by relating crop yield with certain indices as independent variables, such as fAPAR, the Enhanced Vegetation Index (EVI), NDVI, and the Optimised Chlorophyll Vegetation Index (OCVI) (Table 1). These indices were calculated at an early stage (v6) of the crop. These indices were selected because they are directly related to crop growth. They measure vegetation biomass, chlorophyll content, and plant condition (Yasmina et al., 2019). They are sensitive to changes in crop growth, which supports early detection of growth limitations (Lu et al., 2018; Yasmina et al., 2019).

Another fundamental reason for the choice of these indices is that they can be calculated from commonly available satellite data, making them practical for large-scale applications (NASA, 2022).

Regression analysis produced predictive mathematical functions in the form of a simple model (Equation 20) or multilinear (hybrid) model (Equation 21).

where Y_cis the crop yield, a is the intercept, b is the slope, and x is the independent variable (index).

Validation of the predictive yield models

Validation involved a comparison of projected yields with actual yields to determine uncertainties in the projections. We employed R2 and standard error (SE) as metrics to evaluate the performance of the projection models developed based on absorbed solar energy and vegetation indices.

 

Table 1
Selected Vegetation Index Models (VIM) for yield prediction

 

The SE measures the variability and precision of projection while the R2

show the proportion of variance in actual yields. Values range from 0 (no explanation) to 1 (perfect explanation) (Udofia, 2011; Wasserstein et al., 2016).

 

Effect of solar energy on crop yield

The amount of solar energy absorbed affects crop productivity directly or indirectly (Greve et al., 2019; Forster et al., 2007; Stevens et al., 2013; Swann et al., 2016).

Variation in solar energy input influences evapotranspiration, which in turn affects crop growth (Cook and Vizy, 2020; Smith et al., 2021). Understanding the interplay between crop yield and solar radiation is crucial for developing resilient agricultural systems, especially in the face of climate change (He et al., 2021).

The interaction between crop yield and solar energy was measured using Equation 20.

 

RESULTS

The distribution of total solar energy in the study area is shown in Figure 2. Table 2 shows the solar radiation and variation in potential crop yield factors across the 15 sampled plots. Mean values of incident solar radiation, fAPAR, crop biomass, RUE, and harvest index were 904.50wm^-2, 0.393, 516.94 g/plot, 2.94 g/μmoles, and 0.36, respectively. 

The absorbed solar energy ranged 286–572 μmoles, while the incident solar radiation was 897–912 wm^-2. Therefore, the amount of energy absorbed was approximately 40% of incident radiation.

Table 3 presents the actual, estimated, and predicted yields. The average actual yield was 452 kg/plot, while the estimated yield based on the yield model was 515.283 kg/plot. 

The mean projected yields using EVI, OCVI, NDVI, and fAPAR were 447.678  447.652, 533.143, and 506.751 kg/plot, respectively. A combination of the models in a multiple regression produced average maize yield of 443.366 kg/plot. Figure 3, Figure 4, Figure 5 and Figure 6 display the relationships between mean actual yield and the indices. R² values ranged 77–84%.

Table 2
Average crop yield potential factors for yield estimation per plot

Plot	`X	Y	lo W/plot	PAR μmoles	fAPAR	APAR μmoles	b g/plot	RUE μmoles
A	599069.3	829004.9	897.89	448.945	0.441	197.9847	600	3.03
B	599226.5	829039.9	903.68	451.84	0.4	180.736	579.16	3.20
C	599397.9	829084.3	908.32	454.16	0.316	143.5146	437.5	3.05
D	599485.2	829003.4	909.73	454.865	0.315	143.2825	516.66	3.61
E	599309	828954.1	903.52	451.76	0.388	175.282	529.16	3.02
F	599148.7	828895.4	900.6	450.3	0.399	179.669	529.16	2.95
G	599221.7	828782.7	899.68	449.84	0.435	195.680	525	2.68
H	599366.2	828833.5	908.87	454.435	0.342	155.416	541.66	3.49
I	599485.2	828870	912	456	0.315	143.64	541.66	3.77
J	599253.4	828863.7	901.52	450.76	0.507	228.535	595.83	2.61
K	599423.3	828928.7	908.25	454.125	0.442	200.723	587.5	2.93
L	599350.3	829024	905.03	452.515	0.393	177.838	395.83	2.23
M	599174.1	828973.2	901.32	450.66	0.4	180.264	400	2.22
N	599304.4	828749.3	900.4	450.2	0.445	200.339	583.33	2.91
O	599455.2	828799.5	906.67	453.335	0.36	163.200	391.66	2.40
Mean			904.498	452.249	0.393	177.740	516.94	2.94

Source: Computed by the authors using satellite and ground data. W/plot is watt per plot, μmoles is micromoles of absorbed solar energy (APAR)

 

Table 3
Actual, estimated and predicted crop yields

Plot id	EYM kg/plot	CYM kg/plot	NYM kg/plot	fAPAR kg/plot	Hybrid kg/plot	CGM kg/plot	Ya kg/plot
A	493.128	493.003	599.808	553.826	489.701	705.654	552.00
B	451.712	464.243	539.722	513.450	456.47	553.736	432.00
C	375.994	376.844	427.720	430.728	369.677	367.987	382.00
D	374.470	366.774	425.438	429.744	363.422	467.978	412.00
E	439.843	445.942	522.350	501.632	441.081	564.588	482.00
F	450.749	454.390	538.314	512.465	450.796	507.662	432.00
G	487.023	477.053	591.002	547.917	478.778	562.533	482.00
H	397.581	390.763	459.939	456.333	389.608	431.488	362.00
I	374.789	372.395	425.915	429.744	366.594	430.007	362.00
J	568.612	558.454	707.248	618.821	549.863	703.220	532.00
K	494.285	504.393	601.475	554.811	496.199	681.778	527.00
L	444.704	455.090	529.472	506.556	448.289	377.888	432.00
M	451.494	453.372	539.403	513.450	450.607	383.438	432.00
N	497.214	493.253	605.691	557.765	491.454	656.930	507.00
O	413.567	408.817	483.649	474.059	407.948	334.355	387.00
Mean	447.678	447.652	533.143	506.753	443.366	515.283	452.00

Source: Computed by the authors using satellite and ground data. EYM is EVI yield model, CYM is chlorophyll yield model, NYM is NDVI model, Hybrid is multiple indices-based yield model

 

 

 

 

 

 

Validation of projection models

Table 4 shows the linear regression statistics for the relationship of each model actual grain yield. The  values indicate strong relationships. The SE between the actual and predicted yields show that the average variabilities or uncertainties in the respective yields varied widely.

While the EVI and NDVI model yields were 9.98 kg different from the actual yields, the predicted yields from the CYM, Hybrid, and fAPAR models were 12.66, 6.69, and 7.69 kg, respectively, different.

These results indicate that the models produced values relatively close to the actual values, with the Hybrid and fAPAR models standing out. The results are reasonably accurate, but there are still some variabilities or uncertainties in the estimates.

 

Table 4
Predictive crop yield model accuracy and significance

Model		R2	SE	P-value
	NDVI model	0.80	9.98	0.0000016
EVI model		0.81	9.98	0.000016
CYM		0.51	12.66	0.012768
fAPAR model		0.795	7.69	0.0000014
CGM		0.798	29.22	0.000007
Hybrid		0.84	6.69	0.0000015

NDVI = Normalised Difference Vegetation Index; EVI = Enhanced Vegetation Index; fAPAR = fraction of absorbed photosynthetically active radiation; CGM = Crop Growth Model.

 

Equation 22 describes a multi-linear relationship between the predictors and actual yields with a lower SE of 6.69 at R²= 0.84.

Effects of solar energy on crop yield

Figure 7 shows 80% lationship between fAPAR and actual maize yield. The lower 61% regression for CGM estimated yields (Figure 8) also indicates that the model captures the general trend.

 

 

 

DISCUSSION

The average actual (in situ) and model-estimated yields fall within the recommended maize yield range (4–5.7 t/ha) stipulated by FAO (FAO, 2023; Michael et al., 2022;). This translates to 360–513 kg per 900 m². The yield prediction models in this study showed strong performance, particularly the Hybrid (R² = 0.84, SE = 6.69), EVI (R² = 0.81), and NDVI (R² = 0.80) models, indicating that satellite-derived indices such as NDVI, EVI, and fAPAR can reliably estimate in situ maize yield. These findings are in agreement with past studies (Franch et al., 2021; Johnson, 2016) that reported high predictive power (R² >75) when using vegetation indices to model crop yield. Similarly, the strong performance of the fAPAR-based model (R² = 0.795) aligns with results from Peng et al. (2011) and Fodor and Kovács (2020), who highlighted absorbed solar energy as a key yield determinant. However, the relatively low R² and high error of the CYM (R² = 0.51, SE = 12.66) and CGM (SE = 29.22) models suggest variability in model robustness, depending on the indices used. Rembold et al. (2013) found that NDVI and EVI were highly predictive of cereal yields across different countries. Mulla (2013) and Lobell et al. (2008) argued that yield projection can provide early warnings of potential crop failures due to adverse weather conditions, pests, or diseases. This aids proactive decision-making to mitigate losses.

Our study further revealed direct impacts of absorbed solar energy on actual and CGM yields. The 77% regression between the intercepted solar energy and actual grain yield suggests a strong relationship, while it is 61% regression between solar energy and CGM yields, which indicates that the model captures the general trend but may underestimate or not fully account for certain yield-influencing factors (Yang et al., 2022, 2023). The observed positive relationship between maize yields and intercepted solar energy corroborates the findings of Takahashi et al. (2020) and Zhou et al. (2021), which makes it clear that higher solar radiation absorption can contribute to accelerated growth and development of crops, leading to earlier maturity and potentially higher yields.

Our study demonstrated that incorporating multiple vegetation indices (NDVI, EVI, OCVI, and fAPAR) significantly improves yield prediction accuracy compared to single-index models. It also establishes a strong relationship between absorbed solar energy and maize yield, highlighting the value of energy-based metrics in yield modeling, which is often underrepresented in the study area. These findings suggest the potential for early-season prediction models that offer practical implications for food security monitoring and precision agriculture.

 

CONCLUSIONS

The study found out that maize yield increased with increased absorbable solar energy. It also revealed efficiency in estimating and modeling crop yields using ground and remote sensing methods. The results from both methods are closely related. The overall output is a pointer to the fact that predicting yields could ensure sustained food security. Among the predictive models, hybrid and energy absorption-based modesl stand out. Their performance explains the importance of involving multiple factors in developing models, as well as the role of solar energy in crop production.

 

LIMITATIONS AND FUTURE RESEARCH

The yield prediction models developed in this study demonstrated strong performance, but their validation was limited to only 15 plots within a single season and geographic region. The limited number of plots may affect the applicability of the model to other regions.

To improve the robustness and applicability of these models, future research should include validation across various growing seasons, diverse agroecological zones, and larger datasets. Increasing the spatial and temporal coverage will help ensure that the models remain reliable under varying environmental and management conditions. The models depend heavily on vegetation indices and fAPAR derived from satellite imagery. Any issues with cloud cover, spatial resolution, or sensor noise can affect model inputs and hence prediction accuracy.

 

Funding: There was no external funding for this study.

Author contributions: Conceptualization: CFA; Methodology: CFA; Analysis: CFA; Investigation: BMD; Resources: CF; Data curation: CFA; Writing: CFA; Review: SAAA; Supervision: BMD, SAAA. All authors declare that they have read and approved the publication of the manuscript in this present form.

Data availability statement: The data presented in this study are available on request from the corresponding author.

Conflicts of interest: All authors declare no conflicts of interest.

 

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