Scinovex
article Open Access

Modeling solar radiation interception and its effect on sugarcane biomass accumulation

International Journal of Research in Agronomy · 2024 · Vol. 7(4) · pp. 128–131

Abstract

While sugarcane is one of the most efficient C4 crops at converting solar energy into biomass, actual radiation use efficiency (RUE) in tropical plantations often falls below the theoretical maximum. This research quantified solar radiation interception, canopy development, and RUE in plant and first-ratoon sugarcane at Universiti Putra Malaysia during 2021-2022. PAR was measured at four canopy heights using line quantum sensors. The plant crop intercepted 90.2% of incoming PAR at peak canopy (8 months); the ratoon reached 84.7%. Cumulative intercepted PAR was 2,108 and 1,534 MJ/m² for plant and ratoon, respectively. Above-ground biomass reached 34.2 and 23.8 t DM/ha, giving RUE values of 1.62 and 1.55 g DM/MJ. A linear model of cumulative iPAR versus biomass explained over 94% of variance in both crops.

Full Text

Tables and figures are in the full-text PDF.

Introduction

Sugarcane (Saccharum spp. hybrid) can theoretically convert 6-8% of incoming solar radiation into biomass, yet commercial fields achieve only 1-2% [3,7] . This gap represents a major yield improvement opportunity through better understanding of canopy dynamics [4,19] . Monteith [4] established that biomass accumulation is proportional to intercepted PAR, with RUE values of 1.40-1.80 g DM/MJ reported for tropical sugarcane [3,5] . Ratoon crops often intercept less radiation than plant crops because of slower canopy establishment and lower stalk density [6,12] . Few studies have measured radiation at multiple canopy heights in both crop classes under Southeast Asian conditions. This research measured PAR distribution, quantified RUE, and tested linear biomass accumulation models for plant and ratoon sugarcane in peninsular Malaysia.

Materials and Methods

Theoretical background

Radiation interception by crop canopies follows Beer's law: I/I₀ = exp(-k × LAI), where I/I₀ is the fraction of transmitted PAR, k is the extinction coefficient, and LAI is leaf area index [4] . The extinction coefficient for sugarcane typically ranges from 0.40 to 0.65 depending on canopy architecture and leaf angle distribution [5,7] . RUE is calculated as the slope of the linear regression between cumulative intercepted PAR and above-ground dry matter [3] . This framework assumes that RUE remains approximately constant over the crop cycle when water and nutrients are non-limiting [7] .

Simulation parameters PAR was measured using 1-m line quantum sensors (LI-191R, LI-COR Biosciences) placed above the canopy and at three heights within the canopy (0.5, 1.0, and 2.0 m) at monthly intervals from planting through harvest. Measurements were taken on cloudless days between 11:00 and 13:00 local time, with sensors positioned perpendicular to the row direction. Daily incoming PAR was estimated from pyranometer data (CMP11, Kipp & Zonen) assuming PAR = 0.48 × total solar radiation [10] . Intercepted PAR was calculated as: iPAR = incoming PAR × (1transmitted fraction). LAI was measured destructively on five plants per plot every two months using a leaf area meter (LI-3100C).

Materials

Sugarcane variety SP80-1816, a widely grown commercial clone in Southeast Asia, was planted in February 2021 at 1.4 m row spacing using three-bud setts at 10 per linear meter. The trial site at UPM Serdang (2.98° N, 101.73° E, elevation 31 m) has a tropical rainforest climate (Af) with mean annual rainfall of 2,417 mm and mean temperature of 27.3 °C. Soils were Munchong series (Typic Hapludox, clay, pH 4.8). Fertilizer was applied at 200:60:180 kg N:P₂O₅:K₂O/ha in four split applications [9] . The plant crop was harvested at 12 months and the first ratoon at 10 months.

Methods

Above-ground biomass was measured by harvesting 2 m of row from each plot at monthly intervals starting month 2. Fresh weight was recorded, and subsamples were oven-dried at 80 °C to constant weight for dry matter determination. Stalk number per meter was counted at each harvest. RUE was estimated as the slope of the linear regression of cumulative biomass on cumulative iPAR. Data were analyzed by regression analysis and paired t-tests comparing plant and ratoon parameters in R 4.2 [9] .

Results

Comprehensive interpretation

The plant crop intercepted more total PAR (2,108 MJ/m²) than the ratoon (1,534 MJ/m²) due to its longer duration and faster canopy development (Table 1). Peak fPAR reached 0.902 at month 8 in the plant crop versus 0.847 at month 8 in the ratoon. The upper canopy (>2.0 m) captured 47.3% of intercepted PAR in the plant crop but only 41.6% in the ratoon (Figure 1), reflecting the ratoon's shorter stature and lower stalk density. The linear iPAR-biomass relationship was strong for both crops (R² > 0.94; Figure 2), with RUE of 1.62 and 1.55 g/MJ, respectively (Table 2).

Discussion

The RUE values of 1.62 (plant) and 1.55 (ratoon) g DM/MJ fall within the range for well-managed tropical sugarcane [3,5] . The slightly lower ratoon RUE is consistent with Park et al. [6] , who attributed age-related decline to reduced stalk vigor and lower leaf nitrogen in successive cycles. The canopy profile data show the upper canopy dominates PAR capture. The 14.8% of PAR reaching the soil in the ratoon represents lost productivity recoverable through narrower spacing or early intercropping [2,12] . The strong linearity of iPAR-biomass supports radiation-driven models like APSIM-Sugar for yield forecasting [8,16] . The extinction coefficients (k = 0.58 plant, 0.52 ratoon) can be directly incorporated into these models for Malaysian conditions [14,20] .

Conclusion

Solar radiation interception was the primary determinant of sugarcane biomass accumulation under non-limiting conditions at UPM Serdang. The plant crop intercepted 37.4% more cumulative PAR than the ratoon, translating into 43.7% higher final biomass. RUE was 1.62 g/MJ for the plant crop and 1.55 g/MJ for the ratoon. Linear models of iPAR versus biomass explained over 94% of variation in both crops. These results provide site-specific radiation interception parameters for calibrating sugarcane simulation models in tropical Southeast Asia and highlight the ratoon's canopy as a target for management interventions to close the plant-ratoon yield gap.

References

  1. Inman-Bamber NG, McGlinchey MG. Crop coefficients and water-use estimates for sugarcane based on long-term Bowen ratio energy balance measurements. Field Crops Res. 2003;83(2):125-138.
  2. Singels A, Smit MA, Redshaw KA, Donaldson RA. The effect of crop start date, crop class, and cultivar on sugarcane canopy development and radiation interception. Field Crops Res. 2005;92(2-3):249-260.
  3. Robertson MJ, Wood AW, Muchow RC. Growth of sugarcane under high input conditions in tropical Australia: I. Radiation use, biomass accumulation and partitioning. Field Crops Res. 1996;48(1):11-25.
  4. Monteith JL. Climate and the efficiency of crop production in Britain. Philos Trans R Soc Lond B. 1977;281(980):277294.
  5. Muchow RC, Spillman MF, Wood AW, Thomas MR. Radiation interception and biomass accumulation in a sugarcane crop grown under irrigated tropical conditions. Aust J Agric Res. 1994;45(1):37-49.
  6. Park SE, Robertson M, Inman-Bamber NG. Decline in the growth of a sugarcane crop with age under high input conditions. Field Crops Res. 2005;92(2-3):305-320.
  7. Sinclair TR, Muchow RC. Radiation use efficiency. Adv Agron. 1999;65:215-265.
  8. Keating BA, Robertson MJ, Muchow RC, Huth NI. Modelling sugarcane production systems: I. Development and performance of the sugarcane module. Field Crops Res. 1999;61(3):253-271.
  9. Gomez KA, Gomez AA. Statistical procedures for agricultural research. John Wiley and Sons; 1984. p. 1-680.
  10. Liu DL, Kingston G, Bull TA. A new technique for determining the thermal parameters of phenological development in sugarcane. Agric For Meteorol. 1998;90(3):169-183.
  11. Smit MA, Singels A. The response of sugarcane canopy development to water stress. Field Crops Res. 2006;98(23):91-97.
  12. Bell MJ, Garside AL. Shoot and stalk dynamics and the yield of sugarcane crops in tropical and subtropical Queensland. Field Crops Res. 2005;92(2-3):231-248.
  13. Lingle SE, Wiegand CL. Soil salinity and sugarcane juice quality. Field Crops Res. 1997;54(2-3):259-268.
  14. Marin FR, Jones JW, Singels A, Royce F, Assad ED, Pellegrino GQ, et al. Climate change impacts on sugarcane attainable yield in southern Brazil. Clim Change. 2013;117(1):227-239.
  15. De Silva ALC, De Costa WAJM. Growth and radiation use efficiency of sugarcane under irrigated and rain-fed conditions in Sri Lanka. Sugar Tech. 2012;14(3):247-254.
  16. Cheeroo-Nayamuth FC, Robertson MJ, Wegener MK, Nayamuth ARH. Using a simulation model to assess potential and attainable sugar cane yield in Mauritius. Field Crops Res. 2000;66(3):225-243.
  17. Donaldson RA, Redshaw KA, Rhodes R, Van Antwerpen R. Season effects on productivity of some commercial South African sugarcane cultivars: I. Biomass and radiation use efficiency. Proc South Afr Sugar Technol Assoc. 2008;81:517-527.
  18. Inman-Bamber NG. Sugarcane water stress criteria for irrigation and drying off. Field Crops Res. 2004;89(1):107122.
  19. Allison JCS, Pammenter NW, Haslam RJ. Why does sugarcane have a low net conversion efficiency? Proc South Afr Sugar Technol Assoc. 2007;80:86-97.
  20. Jones MR, Singels A, Ruane AC. Simulated yield and water use of sugarcane under climate change. Agric Syst. 2015;139:189-199.
Citations
0
FWCI
0.00
field-weighted impact
References
0
Percentile
74%
vs. same field & year
Citation Network

How this paper connects to the literature. Drag to explore, click any node to open that paper.