Relationship between solar radiation and meteorological variables in predictive models for crop yields

Adam, Abdelkarem M.; Zheng, Yuan

doi:10.1590/1807-1929/agriambi.v29n4e285794

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Original Article • Rev. bras. eng. agríc. ambient. 29 (4) • Apr 2025 • https://doi.org/10.1590/1807-1929/agriambi.v29n4e285794 linkcopy

Relationship between solar radiation and meteorological variables in predictive models for crop yields¹

Relação entre a radiação solar e variáveis meteorológicas em modelo preditivo de produtividade agrícola

Authorship SCIMAGO INSTITUTIONS RANKINGS

Knowledge of the complicated correlation between meteorological variables and crop yield is crucial for food security and agricultural sustainability. This study aimed to investigate how incident solar radiation has affected crop production in the Gadarif region of Sudan over the last 41 years. Using a predictive framework, trends in annual incident solar radiation and temporal variations during sorghum and sesame growing seasons were examined and machine learning (ML) with Extreme Gradient Boosting (XGBoost), Boosted Regression Forest (BRF), and K-Nearest Neighbors (K-NN) was used to predict crop yield. Significant relationships between incident solar radiation indicators and crop yields were identified via detrending approaches and correlation analyses. Results indicate a significant inverse correlation between solar radiation and sorghum yield, and a positive correlation between sesame yield and solar radiation. For both sorghum and sesame yield, K-NN was the most accurate model, demonstrating the significance of incident solar radiation and temperature in predicting crop yield. These findings highlight the potential of ML to improve agricultural forecasting models and inform adaptive agricultural practices in the region. In general, this study provides valuable insights into the dynamic relationship between incident solar radiation and crop yield, emphasizing the importance of considering meteorological factors in agricultural planning and management.

Key words:
solar radiation; crop yield; Gadarif region; Sudan; machine learning

From 2011 to 2021, the correlation between solar radiation and yield was inverse for sorghum and positive for sesame.

A significant upward trend in solar radiation was observed from 2011 to 2021, especially in August and September.

The Mann-Kendall test revealed a significant rise in solar radiation from 2011-2021, with positive Z values and p < 0.05.

Decade	Incident solar radiation (MJ m^-2 per day)	MaxT (°C)	MinT (°C)	RH (%)	Wind speed (m s^-1)	Rainfall (mm)
1981-1990	166.2	33.16	21.7	58.59	4	92
1991-2000	171.2	32.96	21.8	58.96	5	102
2001- 2010	154.6	37.27	22.7	42.94	3	66
2011-2021	177.2	34.26	21.9	53.97	3	100

Variable	Xmin	Xmax	Xmean	CV	SD
Tmax (°C)	32.54	35.39	33.717	0.0202	0.6804
Tmin (°C)	20.42	22.38	21.42	0.0227	0.4856
Rain (mm)	322	910.7	600.16	0.2197	131.84
H (MJ m^-2 per day)	140.21	201.23	167.56	0.0956	16.024
WS (m s^-1)	2.46	7.52	4.9688	0.2449	1.217
RH (%)	31	81.88	53.473	0.2353	12.584
Sesame yield (kg ha^-1)	330.85	1025.2	601.1	0.2597	156.1
Sorghum yield (kg ha^-1)	254.96	1015.4	514.63	0.3788	194.96

Time period	Jul		Aug		Sept		Oct		Growing season
Time period	Z	p-value	Z	p-value	Z	p-value	Z	p-value	Z	p-value
1981-1990	1.25	0.21	0.44	0.65	0.44	0.65	1.52	0.12	0.80	0.42
1991-2000	-0.71	0.47	0.08	0.92	-0.53	0.59	0.62	0.53	0.44	0.65
2001-2010	1.52	0.12	0.53	0.59	1.52	0.12	1.43	0.15	1.34	0.17
2011-2021	1.96	0.04	2.68	≤ 0.007	1.78	≤ 0.01	1.69	0.08	2.23	≤ 0.01

Crop	Models	Training				Testing
		R²	MAE	MAPE	RMSE	R²	MAE	MAPE	RMSE
		R²	MAE	MAPE	(kg ha^-1)	R²	MAE	MAPE	(kg ha^-1)
Sorghum	XGBoost	0.871	21.1	9.3	17.5	0.828	20.2	10.6	19 .5
	K-NN	0.937	16.2	4.4	15.4	0.892	19.1	8.5	16 .2
	BRF	0.898	19.1	7.1	16.2	0.854	21.2	9.3	18.3
Sesame	XGBoost	0.882	22.5	8.1	17.3	0.831	21.3	9.1	19.1
	K-NN	0.951	10.2	4.04	13.1	0.906	12.2	5.2	14.2
	BRF	0.901	18.2	6.3	16.4	0.881	20.7	6.5	17.3

Crop	Time period	Jul		Aug		Sept		Oct		Growing season
Crop	Time period	r	ρ	r	ρ	r	ρ	r	ρ	r	ρ
Sorghum	1981-1990	0.10 ^ns	0.06 ^ns	0.1 ^ns	0.09 ^ns	0.02 ^ns	-0.04 ^ns	0.52 ^ns	0.64 ^ns	0.26 ^ns	0.46 ^ns
	1991-2000	0.03 ^ns	-0.2 ^ns	0.39 ^ns	0.23 ^ns	0.05 ^ns	-0.17 ^ns	0.06 ^ns	0.13 ^ns	0.24 ^ns	0.15 ^ns
	2001-2010	-0.36*	-0.31*	-0.38**	-0.34*	-0.43**	-0.53**	0.19 ^ns	0.03 ^ns	-0.5**	-0.47**
	2011-2021	-0.55**	0.58 ^ns	-0.24 ^ns	-0.31*	-0.35*	-0.22 ^ns	-0.36*	0.41**	0.04 ^ns	-0.06 ^ns
Sesame	1981-1990	-0.16 ^ns	0.28 ^ns	-0.04 ^ns	-0.05 ^ns	-0.15 ^ns	-0.19 ^ns	0.63 ^ns	0.53 ^ns	0.15 ^ns	0.17 ^ns
	1991-2000	0.68**	0.57**	-0.04 ^ns	-0.07 ^ns	-0.11 ^ns	-0.14 ^ns	-0.14 ^ns	0.11 ^ns	-0.33*	-0.23 ^ns
	2001-2010	-0.37*	0.45**	-0.23 ^ns	-0.41**	0.32 ^ns	0.06 ^ns	0.4 ^ns	0.2 ^ns	0.19 ^ns	0.23 ^ns
	2011-2021	0.03 ^ns	0.14 ^ns	0.61 ^ns	0.67 ^ns	-0.03 ^ns	-0.17 ^ns	0.13 ^ns	0.12 ^ns	0.08 ^ns	-0.12 ^ns

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[TFN1] MaxT (°C) - Maximum temperature; MinT (°C) - Minimum temperature; RH (%) - Relative air humidity

Relationship between solar radiation and meteorological variables in predictive models for crop yields1

Relação entre a radiação solar e variáveis ​​meteorológicas em modelo preditivo de produtividade agrícola

Relationship between solar radiation and meteorological variables in predictive models for crop yields¹

Relação entre a radiação solar e variáveis meteorológicas em modelo preditivo de produtividade agrícola