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Review on production of Root Exudates and a Static Model in Sorghum

SR Kumar

Senior Scientist & Principal Investigator (AICSIP Agronomy),
National Research Centre for Sorghum, Rajendranagar, Hyderabad. 500 030.


Grain sorghum in India is mainly grown in the rainfed environments primarily during two seasons. The first commences with the onset of monsoonal rains in May-June (kharif) while the second, when these rains retreat during October (rabi). Above ground dry matter production in relation to genotype by environmental interactions has been studied in detail, but there are limited studies narrating structural and functional inter-relations with reference to below ground processes. Though soil environment with special reference to rainfed situations supports the above ground processes, the difficulties in accurate measurements and related costs limit below ground research.

Genetic engineering tools have enabled production of toxins by above ground plant parts so as to minimize chemical based pest control measures, through gene introductions. These successes have regenerated interests in genetics of crops, which act as models of allelochemical production. Sorghum is one such crop whose roots exude a mixture of biologically active hydrophobic substances of which 85-90% is a major component called sorgoleone.

A systems approach to crop production not only considers management options to increase seasonal crop productivity but also its long term sustenance. Such thinking takes a holisitic view of any system and considers various factors that help decipher the dynamics. This paper reviews research related to root exudates, and factors influencing the production and degradation of allelochemicals. A hypothetical module using multi-location data is proposed to simulate above ground dry biomass production in sorghum, considering various structural and functional inter relations.

Media summary

Functional inter-relations are important in puts to models that help understand the processes and as well simulate output of a crop like sorghum.

Key words

Simulation models, allelopathy, sorghum


A systems approach to crop production not only considers management options to increase seasonal crop productivity but also its long term sustenance. The growing knowledge base has enabled researchers to decipher the interactions that take place with in a given system. For example, introduction of a legume in a cereal based cropping system introduces a symbiotic sub-component which helps sustain the soil health. Similarly it brings about a break in the cereal pest continuum and thus nullifies the related losses due to biotic stresses. Systems thinking skill helps to generate these dynamics as a function of time, and to understand the implications of a change in a component and its ramification on the output.

Kumar (2004), proposed a method that captures the dynamics of dry matter production in Sorghum through the counteractive sub-system of leaf area index that is bell shaped when plotted as a function of time. One of the main effects of increased nitrogen availability appears to be increased leaf area (Lafitte and Loomis, 1988, Muchow, 1988a). The genotype by nitrogen interaction can thus be captured through structural changes in leaf area. The functional dimension could be introduced through an inter-relation between photosynthetic assimilation and maintenance respiration so as to capture the S-shaped growth pattern in sorghum. Past research in the area of allelopathy was reviewed to understand the below ground dynamics of root exudate production.

The objectives of this paper are:

1. To review the past findings in allelopathy with special reference to sorghum

2. To generate hypothetical sub-components summarizing the influence on sorghum productivity.

Review of literature

Roots have remarkable ability to secrete both low and high molecular weight molecules in response to biotic and abiotic stresses. Synthesis and exudation of allelochemicals is typically enhanced by stress conditions like extreme temperature, drought and UV exposure (Inderjit and Weston, 2003).

Root exudates represent one of the largest direct inputs of plant chemicals that serve as an important source of carbon and energy for microorganisms in root rhizosphere (Quian et al., 1997). Information related to the amount, composition and the dynamics of root produced carbon containing compounds is meager. Difficulty in terms of accuracy of their estimation, inability to separate and characterize minute quantities and lack of field studies that examine the short and long term effect of root exudates on soil microbial populations are some of the reasons (Merbach et al., 1999). Comprehensive knowledge on exudates chemistry is lacking and this limits our understanding of such mechanisms.

Sorghum is a potential model plant for biological studies (Yang, 2003). Among the grass species, the sorghum genome (750 Mb) is double the size of rice genome (400 Mb), but three times smaller than maize (2504 Mb) (Mullet et al., 2002). Sorghum roots exude a mixture of biologically active hydrophobic substances of which 85-90% is a major component called sorgoleone (Netzly and Butler, 1986). A considerable variation in production exists among the genotypes and sorgoleone is a potent bio-herbicide that inhibits broad leaf and grassy weeds at as low a concentration as 10μM (Nimbal et al., 1996). Yang, (2003) has cloned and sequenced a key gene associated with sorgoleone production called SORI.

Concentration and the activity of root exudates or allelochemicals in soil are influenced by various factors. Bowen and Rovira (1999) identified diffusion property and moisture level of the soil as important ones, while Anderson and Coats (1995) had found that their degradation in the soil zone is dependent on the microbial activity. Physical, chemical and biological soil barriers limit the phytotoxicity of allelochemicals in many situations (Schmidt and Ley, 1999). Little is known about the soil dynamics of sorgoleone.


All India Coordinated Project

The Indian Council of Agricultural Research (ICAR) funds a coordinated program in field crops that facilitates linkages between National Research Centres (NRC) and State Agricultural Universities (SAU). This program helps conduct multi-location trials across India and datasets generated annually are summarized and discussed in workshops each year. Datasets from such experiments help in generating information.

Table 1. Pooled analysis of variance and level of significance for all characters recorded (Mean of Dharwad, Parbhani, Surat, Udaipur, Mauranipur, Palem)


Plant stand
( lakh ha-1)

Production efficiency

Total biomass
(t ha-1)

Harvest index

Grain yield
(t ha-1)

Test weight

Fodder yield
(t ha-1)

Plant height (cm)

Days to 50% flowering

Fertility levels (F)










Genotypes (G)










F * G










Pooled mean










C.V. (%)










* Significant (5%), ** Significant (1%), ns Not significant

Data Analysis

Table 1 above indicates the kind of observations that are recorded across locations within India. The pooled analysis was carried out using a statistical software package called MSTATC and the experimental design was a split plot design with fertility levels as main plots and genotypes as sub plots.

Hypothetical sub-components

  • Crop duration or the developmental process is a function of photoperiod and temperature. These remain fairly similar for a given location (latitude) or season (weather). But the most important factor is the soil moisture supply which in turn is dependent on the monsoonal pattern.
  • Sorghum has the ability to remain dormant for long periods under soil moisture stress conditions, and the crop duration gets extended under such conditions.
  • Total dry matter production by sorghum in a rainfed environment is driven by the efficiency with which the crop is able to use the soil resources like moisture and nutrient. Soil moisture is dependent on the rainfall intensity and distribution, while nutrient release/availability is linked to soil moisture. These interactions are represented by the production efficiency (PE=g/sq.m/day).
    Dry biomass production = Crop duration * PE
  • Grain yield is dependent on the ability of the genotype/cultivar to partition the produced biomass into different parts of the plant. The local cultivars have higher proportion of vegetative matter, while the improved cultivars have a higher reproductive component. Grain yield is a function of total dry matter produced and the harvest index. Harvest index has been found to be linearly related to nitrogen response (kg grain . kg nitrogen-1) in different growing environments. The South West monsoonal (SW) rains drive the crop production system during kharif (rainy) season, while stored soil moisture from the rainy season drives the rabi (post-rainy) season. Intermittent rains help replenish the soil moisture and improve nutrient response, while under stored soil moisture the drying front could leave the nutrient in a dry layer.

a. Kharif environment

b. Rabi environment

Fig. 1. Harvest index linear relation with nitrogen response in sorghum (a) a rainfed environment where intermittent rainfall is received and (b) a receding soil moisture environment where the crop thrives on stored soil moisture.


The hypothetical model (Figure 2) was run using STELLA (1997) software to generate the yields of sorghum. Current year data was used to validate the model. The predicted and observed values given in Table 2 indicate that the model predicted the trends in both the dry biomass and grain yields of sorghum cultivars. The predicted values were lower than the observed yields. The model could simulate crop dry matter with reasonable accuracy using functional relations in a biological system. Linking such functional processes with environmental factors that influence them can help to understand the system dynamics so as to make interventions whereever necessary to improve final output.

Fig 2. Hypothetical model that generates crop biomass and grain yield.

Table 2. Comparison of predicted total biomass and grain yields of sorghum genotypes tested across multi-locations in India.


Total biomass yield (t ha-1)

Grain yield (t ha-1)






SPH 1290





SPH 1410





SPH 1413





SPH 1420





SPV 1616





CSH 14





CSH 16






Modeling genotype dependent functional relations helps quantify the potential of a given environment. The framework to compare the treatment effects consists of total aboveground biomass production which has structural and functional dependencies. Functionally dry biomass produced is related to the production efficiency and the crop duration. The sub-model has been validated across varying nitrogen environments and could simulate the dry biomass accumulation process. The simple model framework would be useful to help understand the differential response of sorghum cultivars, as well the physiological basis of their productivity.


Anderson TA, and Coats JR (1995). An overview of microbial degradation in the rhizosphere and its implication for bioremediation. p. 135-14 In ‘Bioremediation science and application’ (Ed. H.D.Skipper and R.F. Turco). SSSA Spec. Publ. 43. SSSA, Madison, WI.

Bowen GD and Rovira AD (1999). The rhizosphere and its management to improve plant growth. Adv. Agron. 66, 1-102.

Inderjit and Weston IA (2003). Root exudation: an overview. In ‘Root Ecology’. (Ed. De Kroon and E.J.W. Visser). Springer Verlag. Heidelberg, Germany.

Kumar SR (2004). Modeling structural and functional relation in sorghum to predict the consequences of genotype by management interaction. Poster paper accepted at the International Crop Science Congress, 26th September to 1st October, 2004, Brisbane, Australia.

Lafitte HR and Loomis RS (1988). Growth and composition of grain sorghum with limited nitrogen. Agron. J. 80, 492-497.

Merbach W, Mirus E, Knof G, Remus R, Ruppel S and Russow R. (1999). Release of carbon and nitrogen compounds by plant roots and their possible ecological importance. J. Plant Nutr. Soil Sci. 162, 373-383.

Muchow RC (1988). Effect of nitrogen supply on the comparative productivity of maize and sorghum in a Semi-arid tropical environment. I. Leaf growth and leaf nitrogen. Field Crops Res. 18, 1-16.

Mullet JE, Klein RR and Klein PE (2002). Sorghum bicolor – an important species for comparative grass genomics and a source of beneficial genes for agriculture. Curr. Opin. Plant Biol. 5, 118-121

Netzly DH and Butler IG (1986). Roots of sorghum exude hydrophobic droplets containing biologically active components. Crop Sci. 26, 775-778

Nimbal CI, Pedersen JF, Yerkes CN, Weston IA and Weller SC (1996). Phytotoxicity and distribution of sorgoleone in grain sorghum germplasm. J. Agric. Food Chem. 44, 1343-1347

Quian JH, Doran JW and Walters DT (1997). Maize plant contributions to root zone available carbon and microbial transformations of nitrogen. Soil Biol. Biochem. 29, 1451-1462

Schmidt SK and RE Ley (1999). Microbial competition and soil structure limit the expression of allelochemicals in nature. p. 339-351. In Principles and practices in plant ecology: Allelochemicals interactions. (Ed. Inderjit, Dakshini KMM and Foy CL). CRC Press, Boca Raton, FL.

STELLA 1997. High Performance Systems, Inc. 45 Lyme Road, Suite 200, Hanover, N.H. 03755.

Yang, X (2003) Development of new technology for isolation of key bioherbicidal genes in sorghum root hairs. Cornell University.

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