A Generalized Function of Wheat’s Root Length Density Distributions

temporal distributions of RLD using experimental measurements and simulations. The root length density (RLD) is an important parameter to model Experimental measurement approaches of RLD diswater and nutrient movement in the vadose zone and to study soil– tributions include the root sampling method (Kumar root–shoot–atmosphere interactions. However, it is difficult and timeet al., 1993) and rhizotron (or minirhizotron) method consuming to measure and determine RLD distributions accurately. Especially RLD distributions change with different soil environment, (Ephrath et al., 1999). The root sampling method is plant species, growing seasons, and climatic conditions. In this study, direct and reliable, however, time-consuming and demeasured data sets of wheat RLD distributions were collected from structive. The rhizotron method can be used to monitor the literature and transformed into normalized root length density root development under almost undisturbed conditions (NRLD) distributions. A total of 610 values of wheat NRLD distribuby comparing a series of root photographs taken during tions were pooled together. These data showed a general trend, indesuccessive time periods. Nevertheless, reliability of the pendent of soil environment, wheat species, growing seasons, and rhizotron technique has yet to be fully assessed. Many climates. A generalized function was established to characterize the factors, such as insertion angles of observation tubes NRLD distributions versus normalized root depths. To verify the for photographs and the calibration curve between root generalized function, we measured RLD distributions of winter wheat count and RLD can affect the accuracy of the RLD (Triticum aestivum L.) using laboratory and field experiments for distribution. Therefore, the accurate and effective meadifferent soils, growing stages of wheat, atmospheric conditions, and surement of transient RLD distributions is still a chalwater supplies. Using the generalized function, we predicted winter wheat RLD and compared the predicted results with the experimental lenging task. data and with results using other NRLD functions. The comparison Simulation approaches of RLD distributions include showed that the generalized function predicted RLD distributions root architecture models (Diggle, 1988; Grabarnik et more accurately than the other functions. Although simulated results al., 1998; Thaler and Pagès, 1998; Bidel et al., 2000), of soil water dynamics in soil–wheat systems were similar for the plant growth models (e.g., AFRCWHEAT2, CERES– different NRLD functions, the generalized function should be advanWheat, Jamieson et al., 1998; Jamieson and Ewert, tageous for applications that require accurate information of root 1999), and shoot and root models (Thornley, 1995, development and distribution. 1998). Almost all the root growing simulation models, which are usually comprehensive and complicated, contain a set of production rules and parameters based R development and distribution in soils are imon various assumptions, such as potential root water portant information for root–water and nutrientuptake, root restriction factor, assimilation and/or photouptake studies in soil–plant systems (Asseng et al., 1997). assimilation partitioning and C allocation, and root bioHowever, it is difficult and costly to measure root distribumass/root length ratio (Thaler and Pagès, 1998; Thorntions accurately, because root distributions change with ley, 1998; Jamieson and Ewert, 1999; Bidel et al., 2000). time as well as with different soil environment, plant However, it is difficult to define or evaluate these asspecies, growing seasons, climatic conditions, and other sumptions and parameters. Major issues remain confactors. In current root–water and nutrient-uptake modcerning the mechanisms and integration of uptake activels, RLD distributions are more commonly incorporated ities within a soil–root system and in modeling root than root weight density distributions (Prasad, 1988; van development when interacting with a complex soil enviNoordwijk and van de Geijn, 1996; Jamieson et al., 1998; ronment (van Noordwijk and van de Geijn, 1996). Wu et al., 1999; Musters and Bouten, 2000; Vrugt et Fortunately, a large amount of data has been pubal., 2001). Root length density distributions are often lished for RLD distributions of different crops, espeutilized to analyze soil–root–shoot–atmosphere interaccially for wheat. It is essential to collect the available tions (Smit et al., 1994; Asseng et al., 1997; Zubaidi et data of RLD distributions and to establish general rules al., 1999; Liedgens et al., 2000; Chassot et al., 2001). for root growth of wheat. Wu et al. (1999) introduced Enormous efforts have been made to obtain spatial and the concept of NRLD distribution (Lnrd) and analyzed Lnrd of wheat, maize (Zea mays L.), cotton (Gossypium hirsutum L.), and bean (Phaseolus vulgaris L.) based Q. Zuo, F. Jie, and L. Meng, Dep. of Soil and Water Sciences and on data of RLD in the literature. Their results showed Key Laboratory of Plant–Soil Interactions, MOE, College of Rethat NRLD distributions for each crop at different sources and Environment, China Agricultural University, Beijing growth stages are quite similar and that it is feasible to 100094, P.R. China; R. Zhang, State Key Lab. of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan use a single Lnrd function for each crop. Nonetheless, 430072, China; and Dep. of Renewable Resources, University of Wyothe results need additional examinations because of the ming, Laramie, WY 82071-3354, USA Received 13 Feb. 2003. Original limited data used. Research Paper. *Corresponding author ([email protected]). To further explore and apply the concept of NRLD Published in Vadose Zone Journal 3:271–277 (2004).  Soil Science Society of America Abbreviations: RLD, root length density; NRLD, normalized root length density. 677 S. Segoe Rd., Madison, WI 53711 USA