DRIVING FACTORS OF CROP RESIDUE LAYER EFFECTS ON SUGARCANE DEVELOPMENT AND WATER USE

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1 SHORT, NON-REFEREED PAPER DRIVING FACTORS OF CROP RESIDUE LAYER EFFECTS ON SUGARCANE DEVELOPMENT AND WATER USE OLIVIER FC 1, SINGELS A 1,2 AND SAVAGE MJ 2 1 South African Sugarcane Research Institute, P/Bag X02, Mount Edgecombe, 4300, South Africa 2 School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, 3209, South Africa francois.olivier@sugar.org.za abraham.singels@sugar.org.za savage@ukzn.ac.za Abstract Our understanding of how the surface energy balance and microclimate are changed by the presence of a residue layer and the effect thereof on crop development and water use, is limited. A drip irrigated field trial was conducted in Komatipoort with and without a residue layer in order to quantify the components of the energy balance, soil and growing point temperatures, crop development and water use. In the presence of a residue layer, less energy was partitioned to heat the soil and more of the available energy flux was partitioned to heat the air and less to evaporate water. Crop responses (initial reduction in tiller emergence rate followed by accelerated tiller production and reduced crop water use, CWU) could be explained by the changes brought about to the microclimate (lower soil temperature and higher growing point temperature). Using growing point temperature, instead of air temperature, will eliminate overestimation by the Canesim model of the delay in canopy development due to a residue layer. Reduction in CWU due to a residue layer was simulated reasonably well when the simulation of canopy development was corrected. The proposed refinements to modelling of crop response to residue layers could improve the accuracy of CWU prediction for crops grown with residue layers. Keywords: soil temperature, surface renewal, energy balance, irrigation, crop modelling, residue layer Introduction Green cane harvesting and the retention of a crop residue layer is practiced in many sugarproducing countries of the world, and crop responses are well documented (Sandhu et al., 2013). Less understood are the changes in the surface energy balance and microclimate and the effects thereof on crop growth and water use. This lack of knowledge could potentially limit the applicability of crop models as residue routines are mostly empirical. Net irradiance (Rn) is defined as the balance between incoming and reflected solar irradiance and outgoing and returned infrared irradiance. This energy flux is used primarily for heating the soil (soil heat flux, G), evaporation of water (latent heat flux, LE) and heating of the atmosphere above the soil (sensible heat flux, H). The shortened energy balance can be written as: Rn G = LE + H (1) 144

2 In the Canesim crop simulation model, crop residue layer effects of delayed canopy development and reduced evaporation are simulated based on the work of van den Berg et al. (2006). Changes to the energy balance are not taken into account. The aim of this study was to quantify the impact of a residue layer on the energy balance and its effects on the crop microclimate, crop water use and crop development. Potential improvements to the Canesim crop model (Singels, 2007) are also investigated. Materials and Methods A drip-irrigated trial was conducted on a first ratoon crop of sugarcane variety N46 (cut back in April 2009) near Komatipoort, Mpumalanga (25 37 S; E, 187 masl) on a Glenrosa form soil. Two treatments were applied: (i) a residue layer applied at a rate of 18 t/ha, 175 mm thick (Residue), and (ii) control treatment with no residue layer (Bare). A detailed description of the methodology to measure crop development and soil (TS) and growing point (TG) temperature is provided by Olivier et al. (2010). Energy fluxes were determined by direct measurement (Rn and G) and by the surface renewal technique (H) (Mengistu and Savage, 2010). Latent heat flux (LE) was derived by difference using Equation 1. Daily average flux values for the daylight period (07h00 to 17h30) were calculated for each month. Thermal time was calculated using air (TTA) and growing point (TTG) temperatures using a base temperature of 16 C. Measured canopy cover (fractional interception of PAR) and crop water use (CWU) were compared to Canesim simulated values. Results and Discussion Seasonal energy balance variation and effect on microclimate During the partial (May to October) and full canopy (November to February) periods, G accounted for 21% and 6% of Rn in the Bare treatment and 6% and 3% of Rn in the Residue treatment respectively (Table 1). Compared to the Bare treatment, more (+25% and +15%) of the available energy flux (Rn G) in the Residue treatment was distributed to H and less (-11% and -4%) to LE during the partial and full canopy periods. As a result, daily average TS was about 3.7 C and 0.1 C lower and TG 1.3 C and 1.1 C higher in the Residue treatment during the partial and full canopy periods. These findings are in agreement with the daily case studies reported by Olivier et al. (2010). Crop development and water use Slower shoot emergence, lower initial tiller population (50%) and the delay in the timing of peak tiller population (16 days) observed in the Residue treatment were directly related to the lower average TS caused by lower G early on in the season (Table 1). However, this initial delay in crop and canopy development quickly gave way to accelerated development as a result of higher TG caused by the higher H in the Residue treatment. Tillering rates during the partial canopy period were found to be similar for the two treatments when expressed in terms of TTG (0.2 tillers per C d), but different for TTA (0.07 and 0.03 tillers per C d for Residue and Bare treatment respectively). Canesim overestimated the reduction in canopy development in response to the residue layer, requiring an additional 281 C d to reach 50% canopy. TG can potentially be used as a driver, instead of air temperature (TA), to improve the simulation of tiller and canopy development responses to residue layers in models. 145

3 Table 1. Monthly average daylight values of net radiation flux (R n), soil heat flux (G), available energy flux (R n-g), sensible heat flux (H), latent heat flux (LE) and energy balance crop water use (CWU EB) of the Bare (B) and Residue (R) treatments. Shown in brackets are G expressed as a percentage of R n, and H and LE expressed as a percentage of (R n-g). Monthly average canopy cover (FI), residue depth, air (T A), soil (T S) and growing point (T G) temperature as well as seasonal averages (or total in the case of CWU) are also provided. Month May Jun Jul Aug Sep Oct Nov Dec Jan Feb Seasonal average or totals FI Residue Energy balance components Surface (%) depth MJ.m cover.d -1 (mm) R n G R n-g H LE B (20.51) (31.01) (68.98) R (5.84) (48.57) (51.42) B (25.04) (42.72) (57.27) R (5.38) (51.16) (48.83) B (27.63) (48.74) (51.25) R (8.26) (58.31) (41.68) B (23.36) (43.11) (56.88) R (7.13) (50.28) (49.71) B (17.25) (43.33) (56.66) R (5.14) (40.97) (59.02) B (13.48) (42.42) (57.57) R (3.96) (40.57) (59.42) B (8.78) (29.39) 7 (70.60) R (2.94) (32.83) 1 (67.16) B (6.05) (29.23) 2 (70.76) R (3.18) (33.55) 0 (66.44) B (4.79) (22.90) 6 (77.09) R (3.11) (26.80) 5 (73.19) B (3.97) (31.37) 3 (68.62) R (2.66) (31.71) 3 (68.28) CWU EB (mm.d -1 ) T A T S T G B * R * *Seasonal total Compared to the Bare treatment, seasonal total CWU of the Residue treatment was reduced by 5% (60 mm), which corresponds to the lower average LE values measured in the Residue treatment (Table 1). It was encouraging to see that after correcting simulated canopy development (eliminating the simulated delay due to a residue layer), Canesim was able to accurately simulate differences in CWU between the Bare and Residue treatments through the season (Figure 1). The model however underestimated CWU for both treatments in the first 30 days of the growing season and overestimated CWU slightly at other times later on (days and days ) (Figure 1). These discrepancies require further investigation. 146

4 Figure 1. Measured canopy cover for both treatments are compared to the simulated canopy cover for the Bare treatment. Measured cumulative crop water use (CWU) for both treatments are compared to values simulated using simulated canopy cover for the Bare treatment. Conclusions In the presence of a residue layer, less energy was partitioned to heat the soil, more of the available energy flux partitioned to heat the air and less to evaporate water. Crop responses to a residue layer (slow initial shoot emergence followed by accelerated tiller production and canopy development) could be explained by the changes in the microclimate (lower TS and higher TG). Using TG instead of TA may improve the Canesim simulation of canopy development response to a residue layer. Canesim was able to simulate the reduction in evaporation and CWU in response to the residue layer reasonably well after the inaccurate simulation of canopy development was corrected. The proposed refinements to the simulation of crop response to residue layers could improve the accuracy of CWU predictions of crops grown with residue layers. Acknowledgements The authors would like to thank the technical staff at the Mpumalanga Research Station for their dedicated work during the execution of this trial. 147

5 REFERENCES Mengistu MG and Savage MJ (2010). Surface renewal method for estimating sensible heat flux. Water SA 36: Olivier FC, Singels A and Savage MJ (2010). The effect of a trash blanket on the energy balance of a sugarcane crop: Preliminary results. Proc S Afr Sug Technol Ass 83: Sandhu HS, Gilbert RA, Kingston G, Subiros JF, Morgan K, Rice RW, Baucum L, Shine Jr JM and Davis L (2013). Effects of sugarcane harvest method on microclimate in Florida and Costa Rica. Agric For Meteorol 117: Singels A (2007). A new approach to implementing computer-based decision support for sugarcane farmers and extension staff. The case of My Canesim. Proc Int Soc Sug Cane Technol 26: (also published in Sugar Cane International 26: 22-25). van den Berg M, Jones M and van Antwerpen R (2006). Modelling trash management and its impacts: Model performance. Proc S Afr Sug Technol Ass 80: