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Journal of Arid Land  2020, Vol. 12 Issue (4): 630-639    DOI: 10.1007/s40333-020-0020-y
Research article     
Physio-biochemical and nutrient constituents of peanut plants under bentazone herbicide for broad-leaved weed control and water regimes in dry land areas
S SAUDY Hani1,*(), M EL-METWALLY Ibrahim2, A ABD EL-SAMAD Goma1
1 Agronomy Department, Faculty of Agriculture, Ain Shams University, 68-Hadayek Shoubra 11241, Cairo, Egypt
2 Botany Departments, National Research Centre, El-Behos St. Dokki 12622, Cairo, Egypt
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The abundance of broad-leaved weeds in peanut fields represents the handicap in weed management programs, since limited specific herbicides can be recommended to control them. Moreover, the physio-biochemical constituents and nutritional status in peanut plants as affected by available herbicides, i.e., bentazone under water stress conditions are not well known. Therefore, field trials were conducted during the growing seasons in 2016 and 2017 to investigate the interactional impact of irrigation levels (I50, I75 and I100, representing irrigation by 50%, 75% and 100% of crop evapotranspiration, respectively) and weed control practices (bentazone, bentazone+hoeing once, hoeing twice and weedy check as control) on dominant broad-leaved weeds as well as peanut physiological and agronomic traits. Result indicated that the efficiency of weed control for each weeded treatment under I50 significantly equaled with its counterpart under I75 or I100. Bentazone+hoeing once diminished weed biomass by 89.3% and enhanced chlorophyll content of peanut plants by 51.2%. Bentazone relatively caused a reduction in carotenoides. Hoeing twice and bentazone+hoeing once under I100 in both growing seasons as well as hoeing twice under I75 in 2017 were the superior combinations for boosting pod yield of peanut plants. Treatment of bentazone+hoeing once and I75 recorded the lowest reduction in N utilization percentage and the highest increase in potassium utilization percentage of peanut plants. Eliminating weeds enhanced water use efficiency by 37.8%, 49.6% and 34.7% under I50, I75 and I100, respectively. In conclusion, peanut seems to be tolerant to bentazone at moderate water supply, thus it can be safely used in controlling the associated broad-leaved weeds.

Key wordsbiochemical traits      drought      nutritional status      peanut productivity      weed growth     
Received: 04 November 2019      Published: 10 July 2020
Corresponding Authors: S SAUDY Hani     E-mail:
About author: *Corresponding author: Hani S SAUDY (E-mail:
Cite this article:

Hani S SAUDY, Ibrahim M EL-METWALLY, Goma A ABD EL-SAMAD. Physio-biochemical and nutrient constituents of peanut plants under bentazone herbicide for broad-leaved weed control and water regimes in dry land areas. Journal of Arid Land, 2020, 12(4): 630-639.

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Particle size (%) Texture BD
Percentage of water contents on weight basis (%)
Sand Silt Clay FC PWP AW
0-15 89.0 6.7 4.3 Sandy 1.54 12 4.1 7.9
15-30 88.4 7.4 4.2 Sandy 1.57 12 4.1 7.9
30-45 88.1 7.9 4.0 Sandy 1.60 12 4.1 7.9
Table 1 Physical traits and water status of the soil at the El Nubaria District
Fig. 1 Biomass of broad-leaved weeds associated with peanut plants as influenced by irrigation level×weed control. I50, I75 and I100 are irrigations at 50%, 75% and 100% of crop evapotranspiration, respectively. LSD0.05 was 9.6 in 2016 and 7.3 in 2017; different lowercase letters indicate significant differences among different treatments at P<0.05 level.
Variable Chlorophyll
Total carbohydrates
Total soluble sugars
Oil (%)
2016 2017 2016 2017 2016 2017 2016 2017 2016 2017
I50 Bentazone 4.52e 4.24f 1.19ef 1.40cd 261.6g 276.1e 23.2f 24.6h 19.2e 20.1de
Bentazone+hoeing once 4.88e 4.82e 1.40de 1.62bc 281.9ef 314.7d 24.4e 25.3gh 20.6d 20.7cd
Hoeing twice 4.75e 4.89e 1.51de 1.64bc 311.6d 323.0d 26.3d 25.5g 20.7cd 20.9cd
Weedy check 3.35g 3.50g 0.94ef 1.15d 222.9h 231.0f 19.9g 21.1j 17.8f 18.1f
I75 Bentazone 5.52d 5.70d 1.41de 1.44bcd 320.1d 330.4cd 27.3cd 28.4e 21.3bcd 21.1bcd
Bentazone+hoeing once 5.70cd 5.78cd 1.70cd 1.79b 346.6c 360.7bc 27.6c 29.4d 21.7abc 21.2bcd
Hoeing twice 6.13bc 6.24c 2.12ab 2.31a 376.0ab 379.0ab 30.6b 32.5c 21.8ab 21.5bc
Weedy check 3.69fg 3.91fg 1.22ef 1.20d 273.7fg 269.2e 22.4f 23.2i 18.4ef 18.4f
I100 Bentazone 6.43b 6.92b 1.66cd 1.69bcc 361.9bc 373.4ab 28.3c 30.2d 21.6abcd 21.5bc
Bentazone+hoeing once 7.33a 7.50a 1.87bc 2.15a 365.5ab 390.4ab 31.2b 34.5b 21.9ab 22.2ab
Hoeing twice 7.55a 7.88a 2.41a 2.48a 379.9a 406.4a 34.7a 35.6a 22.4a 22.9a
Weedy check 4.02f 4.15f 1.48de 1.52bcd 291.4e 302.0de 25.1e 26.5f 18.8ef 19.1ef
LSD0.05 0.44 0.50 0.30 0.35 16.3 35.3 1.1 0.8 1.0 1.1
Table 2 Leaf pigments and seed biochemical components of peanut as influenced by irrigation level×weed control
Variable Pod number/plant Pod weight/plant (g) Seed index (g) Pod yield (t/hm2)
2016 2017 2016 2017 2016 2017 2016 2017
I50 Bentazone 34.4f 31.8g 32.4f 31.3de 60.2e 62.7d 4.34e 4.36e
Bentazone+hoeing once 41.1e 35.08f 43.3d 27.4f 65.6d 68.1cd 4.58e 4.67e
Hoeing twice 45.1cd 45.2cd 51.4c 34.0cd 72.1c 75.2b 4.68de 4.87de
Weedy check 24.1i 25.0h 20.4g 18.0g 35.0h 38.6g 3.17f 3.63f
I75 Bentazone 43.3de 41.2e 48.8c 32.8d 65.9d 73.2bc 4.89de 5.38cd
Bentazone+hoeing once 46.5c 46.9bc 54.6b 34.1cd 72.0c 75.1b 5.57bc 5.43cd
Hoeing twice 53.3ab 52.2a 62.2a 41.2b 77.0b 79.2ab 5.80abc 6.11ab
Weedy check 27.6h 25.1h 31.1f 21.3g 42.1g 44.8f 3.72f 3.65f
I100 Bentazone 42.2e 43.0de 51.7c 37.4c 71.7c 74.1bc 5.25cd 5.58bc
Bentazone+hoeing once 51.7b 50.0ab 50.8c 43.0b 78.2ab 77.3b 5.93ab 6.43a
Hoeing twice 54.6a 52.8a 59.6a 51.2a 81.3a 84.3a 6.18a 6.73a
Weedy check 30.8g 31.0g 40.0e 27.8ef 49.2f 52.4e 4.38e 4.39e
LSD0.05 2.4 3.4 2.8 3.6 3.8 5.9 0.56 0.61
Table 3 Agronomic traits of peanut as influenced by irrigation level×weed control
Variable Seed Straw
N (%) P (%) K (%) N (%) P (%) K (%)
2016 2017 2016 2017 2016 2017 2016 2017 2016 2017 2016 2017
I50 Bentazone 2.51fg 2.51fg 0.427e 0.438fg 2.15a 2.30a 1.80cde 2.04de 0.174f 0.251efg 1.61cd 1.57cd
hoeing once
2.58ef 2.49fg 0.447de 0.466efg 2.25a 2.33a 2.06bcd 2.21cde 0.232e 0.272ef 1.73bc 1.81abc
Hoeing twice 2.75def 2.75e 0.473de 0.512de 2.28a 2.40a 2.19sc 2.23cde 0.263d 0.338d 1.76bc 1.83abc
Weedy check 2.21g 2.33g 0.386e 0.409g 2.09a 2.25a 1.54e 1.62f 0.152f 0.212g 1.37d 1.36d
I75 Bentazone 3.01cd 3.15d 0.524cd 0.547cd 2.27a 2.42a 2.19bc 2.29cd 0.269d 0.278e 1.70bc 1.80abc
hoeing once
2.94cde 3.53c 0.561bc 0.573c 2.35a 2.47a 2.40ab 2.52bc 0.323c 0.376cd 1.68bc 1.90abc
Hoeing twice 3.45ab 3.67bc 0.571abc 0.597abc 2.43a 2.48a 2.73a 2.85ab 0.373b 0.409bc 1.93ab 2.10ab
Weedy check 2.54fg 2.70ef 0.422e 0.434fg 2.22a 2.33a 1.72de 1.81ef 0.218e 0.234fg 1.58cd 1.65cd
I100 Bentazone 3.12bc 3.66bc 0.596abc 0.591bc 2.33a 2.43a 2.41ab 2.48bc 0.327c 0.356d 1.84bc 1.91abc
hoeing once
3.56a 3.78ab 0.633ab 0.635ab 2.44a 2.55a 2.68a 2.90ab 0.375b 0.441ab 2.12a 2.15a
Hoeing twice 3.73a 3.92a 0.651a 0.655a 2.42a 2.56a 2.78a 3.12a 0.410a 0.464a 2.19a 2.16a
Weedy check 2.72def 3.07d 0.472de 0.472ef 2.39a 2.30a 1.91cde 2.17cde 0.269d 0.277e 1.68bc 1.73bc
LSD0.05 0.33 0.20 0.079 0.056 ns ns 0.41 0.39 0.031 0.039 0.25 0.33
Table 4 N, P and K contents in peanut seed and straw as influenced by irrigation level×weed control
Fig. 2 Variation of nutrient utilization percentage of peanut as influenced by irrigation level×weed control. I50, I75 and I100 are irrigations at 50%, 75% and 100% of crop evapotranspiration, respectively.
Fig. 3 Water use efficiency (WUE) of peanut as influenced by irrigation level×weed control. I50, I75 and I100 are irrigations at 50%, 75% and 100% of crop evapotranspiration, respectively.
[1]   Allen R G, Pereira L S, Raes D, et al. 1998. Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. Rome, Italy, 1-15.
[2]   AOAC (Association of Official Agriculture Chemists). 2005. Official Methods of Analysis (18th ed.). Maryland: AOAC International, 1-97.
[3]   Awal M W, Ikeda T. 2002. Recovery strategy following the imposition of episodic soil moisture deficit in stands of peanut (Arachis hypogaea L.). Journal of Agronomy and Crop Science, 188: 185-192.
doi: 10.1046/j.1439-037X.2002.00558.x
[4]   Bradshaw L D, Barrett M, Poneleit C G. 1992. Physiological basis for differential bentazon susceptibility among corn (Zea mays) inbreds. Weed Science, 40(4): 522-527.
doi: 10.1017/S0043174500058070
[5]   Burton J D, Maness E P. 1992. Constitutive and inducible bentazon hydroxylation in shattercane (Sorghum bicolor) and johnsongrass (S. halapense). Pesticide Biochemistry and Physiology, 44(1): 40-49.
doi: 10.1016/0048-3575(92)90007-M
[6]   Casella G. 2008. Statistical Design. Gainesville: Springer, 207-241.
[7]   Clewis S B, Everman W J, Jordan L D, et al. 2007. Weed management in North Carolina peanut (Arachis hypogaea) with s-metolachlor, diclosulam, fiumioxazin and sulfentrazone system. Weed Technology, 21(3): 629-635.
doi: 10.1614/WT-06-139.1
[8]   Cottenie A, Verloo M, Kiekens L, et al. 1982. Chemical Analysis of Plant and Soil. Belgium: Laboratory for Analytical Chemistry and Agrochernistry, State University of Ghent, 63.
[9]   Doorenbos J, Pruitt W O, Aboukhaled A, et al. 1977. Guidelines for predicting crop water requirements-FAO irrigation and drainage. Rome, Italy, 24: 35-95.
[10]   Gill S S, Tuteja N. 2010. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiology and Biochemistry, 48(12): 909-930.
doi: 10.1016/j.plaphy.2010.08.016
[11]   Gronwald J W, Connelly J A. 1991. Effect of monooxygenase inhibitors on bentazon uptake and metabolism in maize cell suspension cultures. Pesticide Biochemistry and Physiology, 40(3): 284-294.
doi: 10.1016/0048-3575(91)90100-Z
[12]   Hess D F. 2000. Light-dependent herbicides: An overview. Weed Science, 48(2): 160-170.
doi: 10.1614/0043-1745(2000)048[0160:LDHAO]2.0.CO;2
[13]   Jackson M L. 1973. Soil Chemical Analysis (2nd ed.). New Delhi: Prentice Hall of India Private Limited, 144-197.
[14]   Jhala A P, Rathod H, Patel K C, et al. 2005. Growth and yield of groundnut (Arachis hypogaea L.) as influenced by weed management practices and Rhizobium inoculation. Communications in Agricultural and Applied Biological Sciences, 70(3): 493-500.
pmid: 16637221
[15]   Keller J, Bliesner R D. 1990. Sprinkle and Trickle Irrigation. New York: Van Nostrand Reinhold, 1-584.
[16]   Krasnovsky Jr A A. 1998. Singlet molecular oxygen in photobiochemical systems: IR phosphorescence studies. Membrane and Cell Biology, 12(5): 665-690.
pmid: 10379647
[17]   Krieger-Liszkay A. 2005. Singlet oxygen production in photosynthesis. Journal of Experimental Botany, 56(411): 337-346.
doi: 10.1093/jxb/erh237 pmid: 15310815
[18]   Loll B, Kern J, Saenger W, et al. 2005. Towards complete cofactor arrangement in the 3.0 Ǻ resolution structure of photosystem II. Nature, 438: 1040-1044.
doi: 10.1038/nature04224 pmid: 16355230
[19]   Nzokou P, Cregg B M. 2010. Growth, biomass, and nitrogen use efficiency of containerized fraser fir (Abies fraseri) as related to irrigation and nitrogen fertilization. Horticulture Science, 45(6): 946-951.
[20]   Oerke E C. 2006. Crop losses to pests. Journal of Agricultural Science, 144(1): 31-43.
[21]   Patterson D T. 1995. Effects of environmental stress on weed/crop interactions. Weed Science, 43(3): 483-490.
doi: 10.1017/S0043174500081510
[22]   Pivec J, Brant V. 2009. The actual consumption of water by selected cultivated and weed species of plants and the actual values of evapotranspiration of the stands as determined under field conditions. Soil Water Research, 4: 539-548.
[23]   Priya R S, Chinnusamy C, Manickasundaram P, et al. 2013. A review on weed management in groundnut (Arachis hypogaea L.). International Journal of Agricultural Science, 3: 163-172.
[24]   Rutherford A W, Krieger-Liszkay A. 2001. Herbicide-induced oxidative stress in photosystem II. Trends in Biochemistry Science, 26: 648-653.
doi: 10.1016/S0968-0004(01)01953-3
[25]   Saudy H S, El-Metwally I M. 2019. Nutrient utilization indices of NPK and drought management in groundnut under sandy soil conditions. Communications in Soil Science and Plant Analysis, 50(15): 1821-1828.
doi: 10.1080/00103624.2019.1635147
[26]   Singh V B, Giri G. 2001. Influence of intercropping and weed control measures on dry matter accumulation and nutrient uptake by sunflower and groundnut and their effect on succeeding maize. Indian Journal of Agronomy, 46: 50-55.
[27]   Sterling T M, Balke N E. 1989. Differential bentazon metabolism and retention of bentazon metabolites by plant cell cultures. Pesticide Biochemistry and Physiology, 34(1): 39-48.
doi: 10.1016/0048-3575(89)90139-9
[28]   Trebst A. 2003. Function of beta-carotene and tocopherol in photosystem II. Z. Zeitschrift fur Naturforschung C, 58: 609-620.
[29]   Vencill W K. 2002. Herbicide Handbook (8th ed.). Lawrence: Weed Science Society America, 310-312.
[30]   Weiss E A. 1983. Oil Seed Crops. London: Longman, 31-36.
[31]   Witham F H, Blaydes D F, Devin P M. 1971. Experiments in Plant Physiology. New York: Van Nosland Reihold Co., 55-58.
[32]   Xue Y G, Chen T T, Yang C, et al. 2010. Effects of different agronomic management practices on the yield and physiological characteristics in mid-season Japonica rice. Acta Agronomica Sinica, 36(3): 466-476. (in Chinese)
doi: 10.3724/SP.J.1006.2010.00466
[33]   Zhu J, Patzoldt W L, Radwan O, et al. 2009. Effects of photosystem II-interfering herbicides atrazine and bentazon on the soybean transcriptome. The Plant Genome, 2(2): 191-205.
doi: 10.3835/plantgenome2009.02.0010
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