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Impacts of geese on weed communities in corn production systems and associated economic benefits
Biological Control 99 (2016) 47–52
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Biological Control journal homepage: www.elsevier.com/locate/ybcon
Impacts of geese on weed communities in corn production systems and associated economic benefits Yuyang Zhang a, Zhipeng Sha a, Fachun Guan a,b,⇑, Chao Wang a,c, Yajun Li d,⇑ a
Agriculture and Animal Husbandry College, Tibet University, Nyingchi, Tibet 860000, China Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China c Platform of Wild Characteristic Biological Resources in Tibet, Nyingchi, Tibet 860000, China d College of Mathematics, Jinlin University, Changchun 130012, China b
h i g h l i g h t s We evaluated three corn (Zea mays) production methods: raising geese in corn fields; conventional corn production with weed management; and corn
fields without weed management. The first of these (i.e. raising geese) minimized herbicide application, maintained higher weed diversity, and results in a greater overall economic benefit
compared to the other two methods. Raising geese in corn fields should be considered more widely for sustainable crop production.
a r t i c l e
i n f o
Article history: Received 13 October 2015 Revised 20 April 2016 Accepted 21 April 2016 Available online 22 April 2016 Keywords: Geese Corn fields Weed populations Community Biodiversity Economic benefit
a b s t r a c t Weed pests directly impact crop quality and yield. We compared three different treatments on weed diversity and structure, and assessed the economic benefits of each on corn (Zea mays) production. The treatments included: raising geese in corn fields (hereinafter referred to as RGICF), conventional corn production with weed management (CCP) and corn fields without weed management (CK). A ShannonWiener diversity index and richness indicated that fields with RGICF and CK had higher weed diversity than CCP fields at early growth stages (60 and 90 days after planting, hereinafter referred to as d.a.p.), but low evenness. In RGICF fields the dominance of the major weed species populations sharply decreased because of geese feeding and trampling activity. As a consequence, weed population abundances were more evenly distributed and the evenness index, richness, and Shannon-Wiener index differed from CK and CCP treatments at 120 d.a.p. The RGICF treatment resulted in a yield reduction of corn. This loss, however, was compensated by the economic gains obtained from geese production and RGICF production without herbicide application should be considered as a production approach for sustainable agriculture operations. Ó 2016 Elsevier Inc. All rights reserved.
1. Introduction Competition for sunlight, water, and soil nutrients between crops and weeds can reduce crop yield and quality (Kropff and Spitters, 1991). Chemical control of weeds is typically used in modern agriculture (Harker and O’Donovan, 2013). Hume (1987) reported that herbicides reduce populations of susceptible weeds while enabling resistant weed species to increase. Despite intensive use of herbicides in corn, abundances of certain weed species
⇑ Corresponding authors at: Agriculture and Animal Husbandry College, Tibet University, Nyingchi, Tibet 860000, China (F. Guan). E-mail address: [email protected] (F. Guan). http://dx.doi.org/10.1016/j.biocontrol.2016.04.011 1049-9644/Ó 2016 Elsevier Inc. All rights reserved.
and yield losses due to weed competition have increased (Keller et al., 2014). On the other hand, increased awareness of the vulnerability of arable weed populations is reflected in the UK Biodiversity Action Plan (BAP), which lists 20 arable plants that are endangered by destructively weeding, of which 12 arable plant species are under prioritized protection (Storkey and Westbury, 2007). Weeds can be viewed as primary producers within agricultural systems and they play important roles in arable system food webs. The weed community can provide food and habitats for higher trophic groups, supporting a diverse community of insects and birds (Marshall et al., 2003; Holland et al., 2006). Weeds can also add ecological value to arable systems by improving soil
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properties, facilitating soil nutrient cycling, and preventing soil erosion and nutrient leaching (El Titi, 1995; Altieri, 1994; Wortman et al., 2010). It is therefore useful to consider protecting the ecological functions of weeds if this can be done while maintaining acceptable crop yields. An alternative to herbicide weed control is Integrated Weed Management (IWM) which combines management techniques that decrease the density of weeds emerging in crops, reduce their relative competitive ability, and reduce the effects of weeds on crop yield to below the economic threshold (ET). The goal is to reduce the need for herbicide applications at the cropping system level (Deytieux et al., 2012; Mézière et al., 2013). Within the concept of IWM, many non-chemical weed control techniques have been developed. These include diversified crop rotations (Derksen et al., 2002; Murphy et al., 2006); notillage, minimal tillage; delayed autumn sowing, post-emergence harrowing (Rasmussen, 2004), competitive cultivars and competitive crop species (Blubaugh and Kaplan, 2015). These methods usually require more labor than chemical weed management, resulting in greater cost (Rask et al., 2013). It is therefore important to explore alternative methods to control weeds while maintaining some weeds for economic benefit. Raising geese in cornfields is a compound production model based on the principle of ‘‘Agro-pastoral Integration,” first proposed in 2011 (Guan and Wang, 2011). It is a production model that uses waste resources such as weeds, lower leaves of crops from the tillage system to raise poultry (Guan et al., 2013a,b). This study evaluated three treatments: Raising Geese in Cornfield model (RGICF), weed-unmanaged corn fields (CK), and the Conventional Corn Planting model (CCP). Our goal was to provide a comprehensive understanding of the effects of raising geese on the control of weed populations, changes of weed community structure (populations, diversity), and associated economic benefits.
2. Materials and methods 2.1. Location and study site The study was conducted in the Niyang River valley in Southwest China near the town of BaYi, Tibet (29°330 N, 94°210 E). The area topography is sloping fields at 2980–3100 m elevation above mean sea level. The climate is warm and sub-humid. The annual mean temperature at the study site was 8.6 °C, with 159.2 days on average exceeding a mean daily temperature of 10 °C. Frostfree period at the study site was 177 days. Assuming a base temperature of 10 °C, the site accumulates 2225.7 degree days. The mean annual sunshine is 1989 h and the 46% of the days have sunshine. The study was conducted in 2014. Three treatments were established. These were RGICF, CK, and CCP. Each treatment was set up in split-split plot design with three blocks, and each sub-plots covered an area of 100 m2. The corn rows were spaced 70 cm apart. A layer of plastic film was mulched and fertilizers were applied at planting (compound fertilizer, 240 kg/ha, which consisted of N-33%, P-17%, K-17%, and organic matter-20%). The sub-plots of the RGICF production model were enclosed by nylon net of 0.5 m height. No herbicide was applied nor manual weed removal conducted in the RGICF production model. On August 7, we conducted rotational grazing of geese (ten geese, 30 d old) in the three subplots of the RGICF production model. The geese were captured and confined in the evening to prevent loss from predation. Additional food was provided (mixed feed, 100 g/goose, consisting of ground corn grain-70%, wheat bran-20%, soybean meal-5%, fish meal-5%).
Post-emergence herbicide, ‘‘Yudiao” (Binnong Technology Company, Binzhou city, Shandong province, China), consisting of 90% atrazine, and 10% mesotrione, was applied to eradicate weeds in the CCP. A manual backpack sprayer with single fan nozzle (XF-16B, Xiefeng Machinery Company, Binzhou city, Shandong province, China) with a fluid capacity of 16 L and a 1.4 L/ min spray rate was used for apply herbicide application. The herbicide was applied 30 days after planting (corn was planted on April 28, 2014). No weed management was conducted in CK during the entire growing season. No geese were grazed in either CCP or CK. 2.2. Weed sampling Four weed evaluations were conducted during the 2014 corn growing season; these were on 4 July which was 60 days after planting (hereafter referred to as d.a.p), Aug. 4 (90 d.a.p), 4 September (120 d.a.p), and 4 October (150 d.a.p). On each sampling date, three 0.5 m2 (0.5 cm 100 cm) quadrats were established in the middle of each plot to minimize edge effects. The quadrats were positioned in the plot to avoid the re-sampling of previously sampled areas. All weeds present in the quadrat were collected and identified to species. The abundance of each weed species was counted to determine species density. Plant heights of the weeds were measured. Harvested weeds were oven dried at 80 °C for 48 h and weighed to determine the above-ground biomass of each species. Corn grain yield was determined by harvesting the entire plot, drying the corn kernals to 14% moisture, weighing, and extrapolating these data to kg ha1. 2.3. Statistical methods Weed species richness, the Shannon-Weiner diversity index (H0 ) and evenness index (E) were used to characterize species diversity. Weed species richness is the number of different weed species found in each plot (Magurran, 2004). H0 is the diversity represented by the proportional abundance of species. Higher values of H0 signify a greater diversity. E is the relationship between the observed number of species and the total number of species. E values can range 0 and 1.0 where a value of 0 corresponds to a community of only one species whereas a value of 1.0 indicates a community where all species are equally abundant (Tang et al., 2014). Rank-abundance plots were used to display the ranking distribution of the species relative abundance data. The X axis is species rank; the Y axis is relative abundance by base-10 logarithms. The relative abundance of a weed species population indicates its degree of dominance in the weed community. The greater the relative abundance of a weed population, the higher its dominance. Meanwhile, abundance distribution can comprehensively describe the community diversity and evenness (Tang et al., 2014). Data were summarized as mean values and standard errors of the mean. Differences in the mean density, height, above-ground biomass and diversity indices of each weed population and community among the treatments were compared using One-way analysis of variance (ANOVA) followed by the Tukey tests for post hoc multiple comparison at a 5% level of significance. Before the ANOVA, all data were transformed by log(x + 1) to satisfy the assumption of homogeneity of variance and normalize distributions. Other data were not normally distributed even after transformation, and thus analyzed using non-parametric Kruskal– wallis test with Dunn’s procedure for multiple comparison. Non-transformed data were presented in the paper. All statistical analyses were performed using SPSS version 21.0.
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3. Results 3.1. Weed populations from the three treatments 3.1.1. Densities of weed population Digitaria sanguinalis, Polygonum nepalense, Chenopodium glaucum, Equisetum diffusum, Gnaphalium affine, and Galinsoga parviflora were the most dominant species in the weed community comprising >85% of the total weed density in each treatment. The mean density for D. sanguinalis was extremely low in CCP compared to RGICF and CK at both 60 (v2 = 19.484, df = 2, P = 0.000, Fig. 1) and 90 d.a.p. (v2 = 17.838, df = 2, P = 0.000), and the number of D. sanguinalis differed significantly between RGICF and CK at 60 d.a.p. (F1, 16 = 6.863, P = 0.019). The density of C. glaucum was higher in RGICF either 60 d.a.p. (F1, 16 = 12.815, P = 0.003) or 90 d. a.p. (F1, 16 = 9.001, P = 0.008) and was absent from CK. At 120 d.a. p. in RGICF the density of D. sanguinalis was 2.38-fold lower than in CK (F1, 16 = 10.174, P = 0.006, Fig. 1), and still showed a significant difference at 150 d.a.p. (F1, 16 = 5.668, P = 0.030). Densities of P. nepalense varied significantly between RGICF and CK at both 120 (F1, 16 = 46.559, P = 0.001) and 150 d.a.p. (v2 = 8.302, df = 1, P = 0.004), 3.1.2. Plant heights of weed species As with density, plant heights of D. sanguinalis in CCP differed significantly from RGICF and CK at 60 (v2 = 18.671, df = 2, P = 0.000, Fig. 2) and 90 (v2 = 17.760, df = 2, P = 0.000) d.a.p. The height of G. affine in CCP was 4.14 times higher than in CK at 90 d.a.p (v2 = 6.701, df = 1, P = 0.010). Plant heights of other dominant weed species were not significantly different at 60 and 90 d.a.p. After 90 d.a.p., consumption of all dominant weeds by geese had significantly affected plant heights, with D. sanguinalis (F2, 24 = 12.101, P = 0.000, Fig. 2) and G. parviflora (v2 = 15.483, df = 2, P = 0.000) in CK significantly higher than in RGICF and CCP. At 120 d.a.p., plant height of P. nepalense in CK was 2.60-fold greater than in RGICF (F1, 16 = 32.267, P = 0.000). Plant heights of G. parviflora and P. nepalense in CK were 3.41-fold (F1, 16 = 5.682 P = 0.030) and (v2 = 12.601, df = 1, P = 0.000) 13.32-fold higher compared with RGICF at 150 d.a.p. 3.1.3. Above-ground biomass of weed populations The minimum biomass of D. sanguinalis occurred in CCP at both 60 (v2 = 17.778, df = 2, P = 0.000, Fig. 3a) and 90 (v2 = 17.667, df = 2, P = 0.000) d.a.p. Biomass of C. glaucum differed significantly between RGICF and CK at both 60 (F1, 16 = 12.316, P = 0.003) and 90 (F1, 16 = 11.095, P = 0.004) d.a.p. Biomass of P. nepalense in CK was 1.94-fold greater compared to RGICF (F1, 16 = 9.057, P = 0.008) at
Fig. 2. Effects of different treatments on plant height of the dominant weed species.
Fig. 3. Effect of different treatments on above-ground biomass of dominant weeds species.
60 d.a.p. but not significantly different at 90 d.a.p. (F1, 16 = 1.596, P = 0.225). When geese consumed weeds after 90 d.a.p. in RGICF, the biomass of D. sanguinalis and P. nepalense in RGICF decreased significantly, and was significantly different between RGICF and CK at both 120 (F1, 16 = 12.775, P = 0.003; F1, 16 = 58.819, P = 0.000, Fig. 3) and 150 (F1, 16 = 21.415, P = 0.000; v2 = 11.637, df = 1, P = 0.001) d.a.p. Biomass of G. parviflora in CK was significantly different from RGICF and CCP at both 120 (v2 = 9.615, df = 2, P = 0.008) and 150 (v2 = 14.059, df = 2, P = 0.001) d.a.p. 3.2. Changes of weed community under different treatments 3.2.1. Total density of the weed community Total density of weed communities varied significantly among treatments at 60 d.a.p. (v2 = 20.903, df = 2, P = 0.000, Fig. 4) and was significantly lower in RGICF than in CCP (F1, 16 = 88.156, P = 0.000) while there was no difference between RGICF and CK at 90 d.a.p. (F1, 16 = 3.379, P = 0.085). After geese grazed (120 d.a. p.), the total density of weeds in CK was 1.83-fold and 4.31-fold greater than in RGICF and CCP (F2, 24 = 31.942, P = 0.000), respectively. At 150 d.a.p., there were no significant differences among treatments (F2, 24 = 0.526, P = 0.598).
Fig. 1. Effect of different treatments on density of dominant weed species.
3.2.2. Total above-ground biomass of the weed community The lowest total above-ground biomass of the entire weed community was in CCP at both 60 (v2 = 18.210, df = 2, P = 0.000, Fig. 5)
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3.3. Economic benefit analysis
Fig. 4. Effect of treatments on overall weeds density.
The cost budget and profits for RGICF, CK, and CCP treatments are presented in Table 2. The largest cost in the RGICF production model was geese feed (30.59% of total), followed by land rent and labor which contributed 18.94% and 13.05%, respectively, to the total economic input. For CK, land rent (47.76%) was the largest cost, followed by chemical fertilizer (17.53%) and labor (10.61%). For CCP, land rent (45.01%) was the largest cost, followed by labor (16.53%) and chemical fertilizer (12.00%). RGICF produced the greatest economic output, which was respectively 2.40 and 2.11 times higher than in the CK and CCP, but it also required the largest economic net income which was 2.20 and 1.79 times higher than that of the CK and CCP, respectively. Due to the considerable economic investment which was 2.52 and 2.38 times higher than that of CK and CCP, the ratio of output/input was 0.07 and 0.19 lower than that of the CK and CCP.
4. Discussion
Fig. 5. Effect of different treatments on weeds overall above-ground.
and 90 (F2, 24 = 308.801, P = 0.000) d.a.p. Consumption of weeds by geese significantly affected above-ground biomass in RGICF, where the total above-ground biomass of RGICF was 280.72% (F1, 16 = 142.022, P = 0.000)and 275.63% (v2 = 10.965, df = 1, P = 0.001) lower than CK at 120 and 150 d.a.p. 3.2.3. Abundance distribution and diversity of the weed community More weed species and most of the weeds belonged to only a few species were observed in the weed communities of CK and RGICF by 60 and 90 d.a.p. because herbicide treatment in CCP significantly reduced weed species number and diversity (Fig. 6A, B). The Shannon-Weiner diversity index (H0 ) and richness (R) in CCP were significantly lower than in RGICF and CK at 60 (F2, 24 = 50.351, P = 0.000; v2 = 21.004, df = 2, P = 0.000) and 90 (F2, 24 = 16.670, P = 0.000; F2, 24 = 52.013, P = 0.000) d.a.p. (Table 1). Geese grazing greatly shaped equitability in the abundance distribution (Fig. 6C), decreased the abundance distribution proportion of dominant weed species such as D. sanguinalis, which was reduced 19.9% at 120 d.a.p. compared to 90 d.a.p. Abundance declined smoothly in RGICF, weed population abundance was distributed more evenly than in CK, and the evenness index (E) differed significantly between RGICF and CK (F1, 16 = 11.698, P = 0.004). Likewise, H0 (F2, 24 = 32.785, P = 0.000) and species number (F2, 24 = 47.181, P = 0.000) were higher in RGICF at 120 d.a.p. Species number continuously increased while R in CCP was lower compared to RGICF and CK at 150 d.a.p., and H0 varied significantly among treatments at 150 d.a.p (F2, 24 = 27.729, P = 0.000). For E, however, there were no significant differences among treatments. (Fig. 6D).
The germination, growth, and competition of weed populations impact the structure of the weed community and the succession process. Weed seed dormancy is broken by cultivation, after which a large number of weeds germinate and grow (Wei et al., 2005; Goldberg and Miller, 1990). Absence of weed management allowed more weed species to emerge and thrive at the beginning of the growing season (60 and 90 d.a.p.) in RGICF (Tang et al., 2014). The reproductive growth stage of corn (up to 120 d.a.p.)is a critical weed-free period (Safdar et al., 2016) and geese can manage weeds during this period by feeding and trampling. Weed consumption, especially the dominant species, may reduce interspecific competition (Wortman et al., 2010), and so it is possible that grazing promotes a slightly greater diversification of some weed species germination and growth opportunities (Plaza et al., 2015). Development of the weed community was not disturbed in CK. Some weeds such as D. sanguinalis, P. nepalense, and C. glaucum which have wide ecological amplitude and high density in the soil seed bank developed rapidly, and became dominant species (Grime, 2002). With weed growth and consumption of environmental resource such as solar radiation and soil nutrients, however, weed intra- and interspecific competition became increasingly intense. During the competition process, the advantages of being a dominant species increased and some rare and also common species were eliminated (Epperlein et al., 2014). The herbicide efficiently controlled density, plant height, and above-ground growth of weeds which are herbicide sensitive (Hume, 1987). Fewer weed species, lower weed density, and lower species diversity and richness occurred in CCP, which concurred with previous studies (Mavunganidze et al., 2014; Mézière et al., 2015). Following application, some weeds which were herbicide tolerant remained, competed with corn for environmental resources, and may have reduced the potential corn yield (Paul et al., 2009). Weeds are biomass producers in agricultural systems. The weed community could enhance the systematic utilization efficiency of solar energy, and increase the dry matter output. The RGICF treatment integrated the corn crop subsystem with the poultry raising subsystem. Weeds functioned as a link between the two subsystems and these weeds were not a significant risk to production as they are in conventional agricultural production. The weed community provided food for the geese, avoiding the input cost of herbicides (Sha et al., 2015). Geese fecal material provided nutrients that promoted the growth of both corn and weeds. The geese controlled the growth and spread of weeds and increased species richness, diversity, and evenness. However, the antagonism between
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Fig. 6. Rank-abundance plots corresponding to different treatments (A), (B), (C), and (D) indicate weeds population rank-abundance plot of July, August, September, and October respectively under different treatments.
Table 1 Diversity of weed community under different treatments. Item
Raising geese in corn field (RGICF)
Conventional corn plant (CCP)
Control (CK)
60 d
H0 E R
1.49 ± 0.16a 0.66 ± 0.07a 9.56 ± 1.01a
0.31 ± 0.31b 0.44 ± 0.35a 1.56 ± 0.53b
1.32 ± 0.22a 0.64 ± 0.08a 7.78 ± 1.20c
90 d
H0 E R
1.16 ± 0.23a 0.62 ± 0.08a 6.56 ± 1.24a
0.53 ± 0.28b 0.66 ± 0.31a 2.11 ± 0.60b
1.00 ± 0.17a 0.60 ± 0.12a 5.67 ± 1.32a
120 d
H0 E R
1.58 ± 0.22a 0.86 ± 0.07a 6.56 ± 1.42a
0.55 ± 0.31b 0.68 ± 0.31ab 2.11 ± 0.60b
1.19 ± 0.20c 0.76 ± 0.06b 4.78 ± 1.20c
150 d
H0 E R
1.77 ± 0.12a 0.81 ± 0.10a 6.67 ± 0.87a
0.76 ± 0.32b 0.75 ± 0.30a 2.67 ± 0.87b
1.35 ± 0.16c 0.83 ± 0.07a 5.33 ± 0.71a
Means (±SE) followed by same letters in rows are not significantly different (P > 0.05)
the weeds and crops can potentially lead to serious yield losses. Agriculture production in the future needs to focus on ecological benefits as well as economic benefits. The economic analysis indicated that in CK, where there was no weed management during the growing season, corn yield loss may have resulted from a reduction in soil fertility (Wortman, 2009). In RGICF, geese consumed both weeds and also some corn leaves, especially bottom leaves, which are a preferred food. Because of geese grazing, corn photosynthesis may have declined due to
Table 2 Economic input–output structure and economic benefits under different treatments (yuan/ha). Item
Raising geese in cornfields (RGICF)
Control (CK)
Conventional corn plant (CCP)
2.90 9691.67 87.89 1120.00 2202.67
— — — 1120.00 2202.67
— — — 1120.00 2202.67
1006.67
1006.67
1006.67
Input Water Fodder Fuel Film Chemical fertilizer Machinery depreciation Nylon net Heating device Herbicide Medicine Land rent Hydropower Labor Corn seeds Baby geese Total
1800.00 133.33 0.00 200.00 6000.00 400.00 4133.33 900.00 4000.00 31678.46
— — — — 6000.00 — 1333.33 900.00 — 12562.67
— — 500.00 — 6000.00 — 1600.00 900.00 — 13329.33
Output Geese Corn Total Output/Input Gross income Net income
26040.00 21458.62 47498.62 1.50 47498.62 15820.16
— 19764.58 19764.58 1.57 19764.58 7201.92
— 22508.89 22508.89 1.69 22508.89 9179.56
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reduced leaf area (Zhang et al., 2014), leading to a yield decrease. In addition, weed competition for resources such as sunlight and nutrients could also impact corn yield. However, due to geese grazing and trampling on weeds, above-ground weed growth was restrained, and competition for environmental resources was also be reduced. The reduction of corn yield is compensated by the economic gain from geese production, ultimately providing a greater economic benefit than the conventional herbicide (CCP) treatment. 5. Conclusion Geese serve as weed biological control agents and successfully control weed growth while maintaining a higher weed diversity. Geese also serve as economic output in the RGICF treatment produced a relatively large economic benefit. We predict that RGICF will eventually become an important production method for sustainable agriculture. Acknowledgment This project was financially supported by National Natural Science Foundation (31201594); Science and Technology Service Network Initiative of C.A.S. (KFJ-EW-STS-073); utilization of courtyard agricultural resource and development of Potentilla anserine L production in Nyingchi area. The support program for the top-notch young teachers of Agricultural and Animal Husbandry College, Tibet University (2015D0601). References Altieri, M.A., 1994. Biodiversity and Pest Management in Agroecosystems. Food Product Press, New York. Blubaugh, C.K., Kaplan, I., 2015. Tillage compromises weed seed predator activity across developmental stages. Biol. Control 81, 76–82. Derksen, D.A., Anderson, R.L., Blackshaw, R.E., et al., 2002. Weed dynamics and management strategies for cropping systems in the northern great plains. Agron. J. 94, 174–185. Deytieux, V., Nemecek, T., Knuchel, R.F., Gaillard, G., Munier-Jolain, N.M., 2012. Is Integrated Weed Management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. Eur. J Agron 36, 55–65. El Titi, A., 1995. Ecological aspects of integrated farming. In: Glen, D.M., Greaves, M. P., Anderson, H.M. (Eds.), Ecology and Integrated Farming Systems. John Wiley and Sons, New York, pp. 243–256. Epperlein, L.R.F., Prestele, J.W., Albrecht, H., Kollmann, J., 2014. Reintroduction of a rare arable weed: competition effects on weed fitness and crop yield. Agr. Ecosyst. Environ. 188, 57–62. Goldberg, D.E., Miller, T.E., 1990. Effects of the different resource additions on species diversity in an annual plant community. Ecology 71, 213–225. Guan, F.C., Wang, C., 2011. The theory and technology approach of agro-pastoral integration principle. J. Tibet Agri. Anim. Husbandry Coll. 1, 42–45 (in Chinese). Guan, F.C., Sha, Z.P., Wang, J.F., Tian, F.P., Cai, C.P., 2013a. Growth and development of goose under different spatiotemporal characteristics. Acta Agrestia Sinica 21, 1208–1213 (in Chinese). Guan, F.C., Tian, F.P., Sha, Z.P., Wang, J.F., Li, S.K., 2013b. Contribution of Goose Feeding Space and production effects on the different production modes. J. China Agric. Univ. 18, 129–133 (in Chinese).
Grime, J.P., 2002. Plant Strategies, Vegetation Processes, and Ecosystem Properties. John Wiley & Sons, Chichester, UK. Harker, K.N., O’Donovan, J.T., 2013. Recent weed control, weed management, and integrated weed management. Weed Technol. 27, 1–11. Hume, L., 1987. Long-term effects of 2, 4-D application on plants I. Effects on the weed community in a wheat crop. Can. J. Bot. 65, 2530–2536. Holland, J.M., Hutchison, M.A.S., Smith, B., Aebischer, N.J., 2006. A review of invertebrates and seed-bearing plants as food for farmland birds in Europe. Ann. Appl. Biol. 148, 49–71. Keller, M., Böhringer, N., Möhring, J., Rueda-Ayala, V., Gutjahr, C., Gerhards, R., 2014. Long-term changes in weed occurrence, yield and use of herbicides in maize in south-western Germany with implications for the determination of economic thresholds. Weed Res. 54, 457–466. Kropff, M.J., Spitters, C.J., 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds. Weed Res. 31, 97– 105. Magurran, A.E., 2004. Measuring Biological Diversity. Blackwell, Oxford, United Kingdom. Marshall, E.J.P., Brown, V.K., Boatman, N.D., Lutman, P.J.W., Squire, G.R., Ward, L.K., 2003. The role of weeds in supporting biological diversity within crop fields. Weed Res. 43, 77–89. Mavunganidze, Z., Madakadze, I.C., Nyamangara, J., Mafongoya, P., 2014. The impact of tillage system and herbicides on weed density, diversity and yield of cotton (Gossipium hirsutum L.) and maize (Zea mays L.) under the smallholder sector. Crop Prot. 58, 25–32. Mézière, D., Colbach, N., Dessaint, F., Granger, S., 2015. Which cropping systems to reconcile weed-related biodiversity and crop production in arable crops? An approach with simulation-based indicators. Eur. J. Agron. 68, 22–37. Mézière, D., Lucas, P., Granger, S., Colbach, N., 2013. Does Integrated Weed Management affect the risk of crop diseases? A simulation case study with blackgrass weed and take-all disease. Eur. J. Agron. 47, 33–43. Murphy, S.D., Clements, D.R., Belaoussoff, S., Swanton, C.J., 2006. Promotion of weed species diversity and reduction of weed seedbanks with conservation tillage and crop rotation. Weed Sci. 54, 69–77. Plaza, E.H., Navarrete, L., González-Andújar, J.L., 2015. Intensity of soil disturbance shapes response trait diversity of weed communities: the long-term effects of different tillage systems. Agri. Ecosyst. Environ. 207, 101–108. Paul, N., Martin, V.A., Fabrice, R., 2009. Evolutionary-thinking in agricultural weed management. New. Phytol. 184, 783–793. Rask, A.M., Larsen, S.U., Andreasen, C., 2013. Determining treatment frequency for controlling weeds on traffic islands using chemical and non-chemical weed control. Weed Res. 53, 249–258. Rasmussen, I.A., 2004. The effect of sowing date, stale seedbed, row width and mechanical weed control on weeds and yields of organic winter wheat. Weed Res. 44, 12–20. Safdar, M.E., Tanveer, A., Khaliq, A., Maqbool, R., 2016. Critical competition period of parthenium weed (Parthenium hysterophorus L.) in maize. Crop Prot. 80, 101– 107. Sha, Z.P., Guan, F.C., Wang, J.F., Zhang, Y.Y., Liu, H.M., Wang, C., 2015. Evaluation of raising geese in cornfields based on emergy analysis: a case study in southeastern Tibet. China Ecol. Eng. 84, 485–491. Storkey, J., Westbury, D.B., 2007. Mini-Review-Managing arable weeds for biodiversity. Pest Manage. Sci. 63, 517–523. Tang, L., Cheng, C., Wan, K., Li, R.H., Wang, D.Z., Tao, Y., Pan, J.F., Xie, J., Chen, F., 2014. Impact of fertilizing pattern on the biodiversity of a weed community and wheat growth. PLoS ONE 9, e84370. Wei, S.H., Shen, Q., Bo, M., Wei, J., 2005. Effect of different crop rotation system on the characteristics of soil seedbank. Chin. J. Ecol. 24, 385–389 (in Chinese). Wortman, S., 2009. Long-term Organic and Conventional Crop Rotations: Yields, Soil Fertility and Weed Communities M.S. thesis. University of Nebraska-Lincoln. Wortman, S.E., Lindquist, J.L., Haar, M.J., 2010. Increased weed diversity, density and above-ground biomass in long-term organic crop rotations-Renew. Agr. Food Syst. 25, 281–295. Zhang, Y.Y., Wang, J.F., Sha, Z.P., Guan, F.C., Duan, J., 2014. Weeds biodiversity and maize growth in agro-pastoral integration system. J. Zhejiang Univ. (Agric. Life Sci.) 40, 638–646 (in Chinese).
Contents lists available at ScienceDirect
Biological Control journal homepage: www.elsevier.com/locate/ybcon
Impacts of geese on weed communities in corn production systems and associated economic benefits Yuyang Zhang a, Zhipeng Sha a, Fachun Guan a,b,⇑, Chao Wang a,c, Yajun Li d,⇑ a
Agriculture and Animal Husbandry College, Tibet University, Nyingchi, Tibet 860000, China Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China c Platform of Wild Characteristic Biological Resources in Tibet, Nyingchi, Tibet 860000, China d College of Mathematics, Jinlin University, Changchun 130012, China b
h i g h l i g h t s We evaluated three corn (Zea mays) production methods: raising geese in corn fields; conventional corn production with weed management; and corn
fields without weed management. The first of these (i.e. raising geese) minimized herbicide application, maintained higher weed diversity, and results in a greater overall economic benefit
compared to the other two methods. Raising geese in corn fields should be considered more widely for sustainable crop production.
a r t i c l e
i n f o
Article history: Received 13 October 2015 Revised 20 April 2016 Accepted 21 April 2016 Available online 22 April 2016 Keywords: Geese Corn fields Weed populations Community Biodiversity Economic benefit
a b s t r a c t Weed pests directly impact crop quality and yield. We compared three different treatments on weed diversity and structure, and assessed the economic benefits of each on corn (Zea mays) production. The treatments included: raising geese in corn fields (hereinafter referred to as RGICF), conventional corn production with weed management (CCP) and corn fields without weed management (CK). A ShannonWiener diversity index and richness indicated that fields with RGICF and CK had higher weed diversity than CCP fields at early growth stages (60 and 90 days after planting, hereinafter referred to as d.a.p.), but low evenness. In RGICF fields the dominance of the major weed species populations sharply decreased because of geese feeding and trampling activity. As a consequence, weed population abundances were more evenly distributed and the evenness index, richness, and Shannon-Wiener index differed from CK and CCP treatments at 120 d.a.p. The RGICF treatment resulted in a yield reduction of corn. This loss, however, was compensated by the economic gains obtained from geese production and RGICF production without herbicide application should be considered as a production approach for sustainable agriculture operations. Ó 2016 Elsevier Inc. All rights reserved.
1. Introduction Competition for sunlight, water, and soil nutrients between crops and weeds can reduce crop yield and quality (Kropff and Spitters, 1991). Chemical control of weeds is typically used in modern agriculture (Harker and O’Donovan, 2013). Hume (1987) reported that herbicides reduce populations of susceptible weeds while enabling resistant weed species to increase. Despite intensive use of herbicides in corn, abundances of certain weed species
⇑ Corresponding authors at: Agriculture and Animal Husbandry College, Tibet University, Nyingchi, Tibet 860000, China (F. Guan). E-mail address: [email protected] (F. Guan). http://dx.doi.org/10.1016/j.biocontrol.2016.04.011 1049-9644/Ó 2016 Elsevier Inc. All rights reserved.
and yield losses due to weed competition have increased (Keller et al., 2014). On the other hand, increased awareness of the vulnerability of arable weed populations is reflected in the UK Biodiversity Action Plan (BAP), which lists 20 arable plants that are endangered by destructively weeding, of which 12 arable plant species are under prioritized protection (Storkey and Westbury, 2007). Weeds can be viewed as primary producers within agricultural systems and they play important roles in arable system food webs. The weed community can provide food and habitats for higher trophic groups, supporting a diverse community of insects and birds (Marshall et al., 2003; Holland et al., 2006). Weeds can also add ecological value to arable systems by improving soil
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properties, facilitating soil nutrient cycling, and preventing soil erosion and nutrient leaching (El Titi, 1995; Altieri, 1994; Wortman et al., 2010). It is therefore useful to consider protecting the ecological functions of weeds if this can be done while maintaining acceptable crop yields. An alternative to herbicide weed control is Integrated Weed Management (IWM) which combines management techniques that decrease the density of weeds emerging in crops, reduce their relative competitive ability, and reduce the effects of weeds on crop yield to below the economic threshold (ET). The goal is to reduce the need for herbicide applications at the cropping system level (Deytieux et al., 2012; Mézière et al., 2013). Within the concept of IWM, many non-chemical weed control techniques have been developed. These include diversified crop rotations (Derksen et al., 2002; Murphy et al., 2006); notillage, minimal tillage; delayed autumn sowing, post-emergence harrowing (Rasmussen, 2004), competitive cultivars and competitive crop species (Blubaugh and Kaplan, 2015). These methods usually require more labor than chemical weed management, resulting in greater cost (Rask et al., 2013). It is therefore important to explore alternative methods to control weeds while maintaining some weeds for economic benefit. Raising geese in cornfields is a compound production model based on the principle of ‘‘Agro-pastoral Integration,” first proposed in 2011 (Guan and Wang, 2011). It is a production model that uses waste resources such as weeds, lower leaves of crops from the tillage system to raise poultry (Guan et al., 2013a,b). This study evaluated three treatments: Raising Geese in Cornfield model (RGICF), weed-unmanaged corn fields (CK), and the Conventional Corn Planting model (CCP). Our goal was to provide a comprehensive understanding of the effects of raising geese on the control of weed populations, changes of weed community structure (populations, diversity), and associated economic benefits.
2. Materials and methods 2.1. Location and study site The study was conducted in the Niyang River valley in Southwest China near the town of BaYi, Tibet (29°330 N, 94°210 E). The area topography is sloping fields at 2980–3100 m elevation above mean sea level. The climate is warm and sub-humid. The annual mean temperature at the study site was 8.6 °C, with 159.2 days on average exceeding a mean daily temperature of 10 °C. Frostfree period at the study site was 177 days. Assuming a base temperature of 10 °C, the site accumulates 2225.7 degree days. The mean annual sunshine is 1989 h and the 46% of the days have sunshine. The study was conducted in 2014. Three treatments were established. These were RGICF, CK, and CCP. Each treatment was set up in split-split plot design with three blocks, and each sub-plots covered an area of 100 m2. The corn rows were spaced 70 cm apart. A layer of plastic film was mulched and fertilizers were applied at planting (compound fertilizer, 240 kg/ha, which consisted of N-33%, P-17%, K-17%, and organic matter-20%). The sub-plots of the RGICF production model were enclosed by nylon net of 0.5 m height. No herbicide was applied nor manual weed removal conducted in the RGICF production model. On August 7, we conducted rotational grazing of geese (ten geese, 30 d old) in the three subplots of the RGICF production model. The geese were captured and confined in the evening to prevent loss from predation. Additional food was provided (mixed feed, 100 g/goose, consisting of ground corn grain-70%, wheat bran-20%, soybean meal-5%, fish meal-5%).
Post-emergence herbicide, ‘‘Yudiao” (Binnong Technology Company, Binzhou city, Shandong province, China), consisting of 90% atrazine, and 10% mesotrione, was applied to eradicate weeds in the CCP. A manual backpack sprayer with single fan nozzle (XF-16B, Xiefeng Machinery Company, Binzhou city, Shandong province, China) with a fluid capacity of 16 L and a 1.4 L/ min spray rate was used for apply herbicide application. The herbicide was applied 30 days after planting (corn was planted on April 28, 2014). No weed management was conducted in CK during the entire growing season. No geese were grazed in either CCP or CK. 2.2. Weed sampling Four weed evaluations were conducted during the 2014 corn growing season; these were on 4 July which was 60 days after planting (hereafter referred to as d.a.p), Aug. 4 (90 d.a.p), 4 September (120 d.a.p), and 4 October (150 d.a.p). On each sampling date, three 0.5 m2 (0.5 cm 100 cm) quadrats were established in the middle of each plot to minimize edge effects. The quadrats were positioned in the plot to avoid the re-sampling of previously sampled areas. All weeds present in the quadrat were collected and identified to species. The abundance of each weed species was counted to determine species density. Plant heights of the weeds were measured. Harvested weeds were oven dried at 80 °C for 48 h and weighed to determine the above-ground biomass of each species. Corn grain yield was determined by harvesting the entire plot, drying the corn kernals to 14% moisture, weighing, and extrapolating these data to kg ha1. 2.3. Statistical methods Weed species richness, the Shannon-Weiner diversity index (H0 ) and evenness index (E) were used to characterize species diversity. Weed species richness is the number of different weed species found in each plot (Magurran, 2004). H0 is the diversity represented by the proportional abundance of species. Higher values of H0 signify a greater diversity. E is the relationship between the observed number of species and the total number of species. E values can range 0 and 1.0 where a value of 0 corresponds to a community of only one species whereas a value of 1.0 indicates a community where all species are equally abundant (Tang et al., 2014). Rank-abundance plots were used to display the ranking distribution of the species relative abundance data. The X axis is species rank; the Y axis is relative abundance by base-10 logarithms. The relative abundance of a weed species population indicates its degree of dominance in the weed community. The greater the relative abundance of a weed population, the higher its dominance. Meanwhile, abundance distribution can comprehensively describe the community diversity and evenness (Tang et al., 2014). Data were summarized as mean values and standard errors of the mean. Differences in the mean density, height, above-ground biomass and diversity indices of each weed population and community among the treatments were compared using One-way analysis of variance (ANOVA) followed by the Tukey tests for post hoc multiple comparison at a 5% level of significance. Before the ANOVA, all data were transformed by log(x + 1) to satisfy the assumption of homogeneity of variance and normalize distributions. Other data were not normally distributed even after transformation, and thus analyzed using non-parametric Kruskal– wallis test with Dunn’s procedure for multiple comparison. Non-transformed data were presented in the paper. All statistical analyses were performed using SPSS version 21.0.
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3. Results 3.1. Weed populations from the three treatments 3.1.1. Densities of weed population Digitaria sanguinalis, Polygonum nepalense, Chenopodium glaucum, Equisetum diffusum, Gnaphalium affine, and Galinsoga parviflora were the most dominant species in the weed community comprising >85% of the total weed density in each treatment. The mean density for D. sanguinalis was extremely low in CCP compared to RGICF and CK at both 60 (v2 = 19.484, df = 2, P = 0.000, Fig. 1) and 90 d.a.p. (v2 = 17.838, df = 2, P = 0.000), and the number of D. sanguinalis differed significantly between RGICF and CK at 60 d.a.p. (F1, 16 = 6.863, P = 0.019). The density of C. glaucum was higher in RGICF either 60 d.a.p. (F1, 16 = 12.815, P = 0.003) or 90 d. a.p. (F1, 16 = 9.001, P = 0.008) and was absent from CK. At 120 d.a. p. in RGICF the density of D. sanguinalis was 2.38-fold lower than in CK (F1, 16 = 10.174, P = 0.006, Fig. 1), and still showed a significant difference at 150 d.a.p. (F1, 16 = 5.668, P = 0.030). Densities of P. nepalense varied significantly between RGICF and CK at both 120 (F1, 16 = 46.559, P = 0.001) and 150 d.a.p. (v2 = 8.302, df = 1, P = 0.004), 3.1.2. Plant heights of weed species As with density, plant heights of D. sanguinalis in CCP differed significantly from RGICF and CK at 60 (v2 = 18.671, df = 2, P = 0.000, Fig. 2) and 90 (v2 = 17.760, df = 2, P = 0.000) d.a.p. The height of G. affine in CCP was 4.14 times higher than in CK at 90 d.a.p (v2 = 6.701, df = 1, P = 0.010). Plant heights of other dominant weed species were not significantly different at 60 and 90 d.a.p. After 90 d.a.p., consumption of all dominant weeds by geese had significantly affected plant heights, with D. sanguinalis (F2, 24 = 12.101, P = 0.000, Fig. 2) and G. parviflora (v2 = 15.483, df = 2, P = 0.000) in CK significantly higher than in RGICF and CCP. At 120 d.a.p., plant height of P. nepalense in CK was 2.60-fold greater than in RGICF (F1, 16 = 32.267, P = 0.000). Plant heights of G. parviflora and P. nepalense in CK were 3.41-fold (F1, 16 = 5.682 P = 0.030) and (v2 = 12.601, df = 1, P = 0.000) 13.32-fold higher compared with RGICF at 150 d.a.p. 3.1.3. Above-ground biomass of weed populations The minimum biomass of D. sanguinalis occurred in CCP at both 60 (v2 = 17.778, df = 2, P = 0.000, Fig. 3a) and 90 (v2 = 17.667, df = 2, P = 0.000) d.a.p. Biomass of C. glaucum differed significantly between RGICF and CK at both 60 (F1, 16 = 12.316, P = 0.003) and 90 (F1, 16 = 11.095, P = 0.004) d.a.p. Biomass of P. nepalense in CK was 1.94-fold greater compared to RGICF (F1, 16 = 9.057, P = 0.008) at
Fig. 2. Effects of different treatments on plant height of the dominant weed species.
Fig. 3. Effect of different treatments on above-ground biomass of dominant weeds species.
60 d.a.p. but not significantly different at 90 d.a.p. (F1, 16 = 1.596, P = 0.225). When geese consumed weeds after 90 d.a.p. in RGICF, the biomass of D. sanguinalis and P. nepalense in RGICF decreased significantly, and was significantly different between RGICF and CK at both 120 (F1, 16 = 12.775, P = 0.003; F1, 16 = 58.819, P = 0.000, Fig. 3) and 150 (F1, 16 = 21.415, P = 0.000; v2 = 11.637, df = 1, P = 0.001) d.a.p. Biomass of G. parviflora in CK was significantly different from RGICF and CCP at both 120 (v2 = 9.615, df = 2, P = 0.008) and 150 (v2 = 14.059, df = 2, P = 0.001) d.a.p. 3.2. Changes of weed community under different treatments 3.2.1. Total density of the weed community Total density of weed communities varied significantly among treatments at 60 d.a.p. (v2 = 20.903, df = 2, P = 0.000, Fig. 4) and was significantly lower in RGICF than in CCP (F1, 16 = 88.156, P = 0.000) while there was no difference between RGICF and CK at 90 d.a.p. (F1, 16 = 3.379, P = 0.085). After geese grazed (120 d.a. p.), the total density of weeds in CK was 1.83-fold and 4.31-fold greater than in RGICF and CCP (F2, 24 = 31.942, P = 0.000), respectively. At 150 d.a.p., there were no significant differences among treatments (F2, 24 = 0.526, P = 0.598).
Fig. 1. Effect of different treatments on density of dominant weed species.
3.2.2. Total above-ground biomass of the weed community The lowest total above-ground biomass of the entire weed community was in CCP at both 60 (v2 = 18.210, df = 2, P = 0.000, Fig. 5)
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3.3. Economic benefit analysis
Fig. 4. Effect of treatments on overall weeds density.
The cost budget and profits for RGICF, CK, and CCP treatments are presented in Table 2. The largest cost in the RGICF production model was geese feed (30.59% of total), followed by land rent and labor which contributed 18.94% and 13.05%, respectively, to the total economic input. For CK, land rent (47.76%) was the largest cost, followed by chemical fertilizer (17.53%) and labor (10.61%). For CCP, land rent (45.01%) was the largest cost, followed by labor (16.53%) and chemical fertilizer (12.00%). RGICF produced the greatest economic output, which was respectively 2.40 and 2.11 times higher than in the CK and CCP, but it also required the largest economic net income which was 2.20 and 1.79 times higher than that of the CK and CCP, respectively. Due to the considerable economic investment which was 2.52 and 2.38 times higher than that of CK and CCP, the ratio of output/input was 0.07 and 0.19 lower than that of the CK and CCP.
4. Discussion
Fig. 5. Effect of different treatments on weeds overall above-ground.
and 90 (F2, 24 = 308.801, P = 0.000) d.a.p. Consumption of weeds by geese significantly affected above-ground biomass in RGICF, where the total above-ground biomass of RGICF was 280.72% (F1, 16 = 142.022, P = 0.000)and 275.63% (v2 = 10.965, df = 1, P = 0.001) lower than CK at 120 and 150 d.a.p. 3.2.3. Abundance distribution and diversity of the weed community More weed species and most of the weeds belonged to only a few species were observed in the weed communities of CK and RGICF by 60 and 90 d.a.p. because herbicide treatment in CCP significantly reduced weed species number and diversity (Fig. 6A, B). The Shannon-Weiner diversity index (H0 ) and richness (R) in CCP were significantly lower than in RGICF and CK at 60 (F2, 24 = 50.351, P = 0.000; v2 = 21.004, df = 2, P = 0.000) and 90 (F2, 24 = 16.670, P = 0.000; F2, 24 = 52.013, P = 0.000) d.a.p. (Table 1). Geese grazing greatly shaped equitability in the abundance distribution (Fig. 6C), decreased the abundance distribution proportion of dominant weed species such as D. sanguinalis, which was reduced 19.9% at 120 d.a.p. compared to 90 d.a.p. Abundance declined smoothly in RGICF, weed population abundance was distributed more evenly than in CK, and the evenness index (E) differed significantly between RGICF and CK (F1, 16 = 11.698, P = 0.004). Likewise, H0 (F2, 24 = 32.785, P = 0.000) and species number (F2, 24 = 47.181, P = 0.000) were higher in RGICF at 120 d.a.p. Species number continuously increased while R in CCP was lower compared to RGICF and CK at 150 d.a.p., and H0 varied significantly among treatments at 150 d.a.p (F2, 24 = 27.729, P = 0.000). For E, however, there were no significant differences among treatments. (Fig. 6D).
The germination, growth, and competition of weed populations impact the structure of the weed community and the succession process. Weed seed dormancy is broken by cultivation, after which a large number of weeds germinate and grow (Wei et al., 2005; Goldberg and Miller, 1990). Absence of weed management allowed more weed species to emerge and thrive at the beginning of the growing season (60 and 90 d.a.p.) in RGICF (Tang et al., 2014). The reproductive growth stage of corn (up to 120 d.a.p.)is a critical weed-free period (Safdar et al., 2016) and geese can manage weeds during this period by feeding and trampling. Weed consumption, especially the dominant species, may reduce interspecific competition (Wortman et al., 2010), and so it is possible that grazing promotes a slightly greater diversification of some weed species germination and growth opportunities (Plaza et al., 2015). Development of the weed community was not disturbed in CK. Some weeds such as D. sanguinalis, P. nepalense, and C. glaucum which have wide ecological amplitude and high density in the soil seed bank developed rapidly, and became dominant species (Grime, 2002). With weed growth and consumption of environmental resource such as solar radiation and soil nutrients, however, weed intra- and interspecific competition became increasingly intense. During the competition process, the advantages of being a dominant species increased and some rare and also common species were eliminated (Epperlein et al., 2014). The herbicide efficiently controlled density, plant height, and above-ground growth of weeds which are herbicide sensitive (Hume, 1987). Fewer weed species, lower weed density, and lower species diversity and richness occurred in CCP, which concurred with previous studies (Mavunganidze et al., 2014; Mézière et al., 2015). Following application, some weeds which were herbicide tolerant remained, competed with corn for environmental resources, and may have reduced the potential corn yield (Paul et al., 2009). Weeds are biomass producers in agricultural systems. The weed community could enhance the systematic utilization efficiency of solar energy, and increase the dry matter output. The RGICF treatment integrated the corn crop subsystem with the poultry raising subsystem. Weeds functioned as a link between the two subsystems and these weeds were not a significant risk to production as they are in conventional agricultural production. The weed community provided food for the geese, avoiding the input cost of herbicides (Sha et al., 2015). Geese fecal material provided nutrients that promoted the growth of both corn and weeds. The geese controlled the growth and spread of weeds and increased species richness, diversity, and evenness. However, the antagonism between
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Fig. 6. Rank-abundance plots corresponding to different treatments (A), (B), (C), and (D) indicate weeds population rank-abundance plot of July, August, September, and October respectively under different treatments.
Table 1 Diversity of weed community under different treatments. Item
Raising geese in corn field (RGICF)
Conventional corn plant (CCP)
Control (CK)
60 d
H0 E R
1.49 ± 0.16a 0.66 ± 0.07a 9.56 ± 1.01a
0.31 ± 0.31b 0.44 ± 0.35a 1.56 ± 0.53b
1.32 ± 0.22a 0.64 ± 0.08a 7.78 ± 1.20c
90 d
H0 E R
1.16 ± 0.23a 0.62 ± 0.08a 6.56 ± 1.24a
0.53 ± 0.28b 0.66 ± 0.31a 2.11 ± 0.60b
1.00 ± 0.17a 0.60 ± 0.12a 5.67 ± 1.32a
120 d
H0 E R
1.58 ± 0.22a 0.86 ± 0.07a 6.56 ± 1.42a
0.55 ± 0.31b 0.68 ± 0.31ab 2.11 ± 0.60b
1.19 ± 0.20c 0.76 ± 0.06b 4.78 ± 1.20c
150 d
H0 E R
1.77 ± 0.12a 0.81 ± 0.10a 6.67 ± 0.87a
0.76 ± 0.32b 0.75 ± 0.30a 2.67 ± 0.87b
1.35 ± 0.16c 0.83 ± 0.07a 5.33 ± 0.71a
Means (±SE) followed by same letters in rows are not significantly different (P > 0.05)
the weeds and crops can potentially lead to serious yield losses. Agriculture production in the future needs to focus on ecological benefits as well as economic benefits. The economic analysis indicated that in CK, where there was no weed management during the growing season, corn yield loss may have resulted from a reduction in soil fertility (Wortman, 2009). In RGICF, geese consumed both weeds and also some corn leaves, especially bottom leaves, which are a preferred food. Because of geese grazing, corn photosynthesis may have declined due to
Table 2 Economic input–output structure and economic benefits under different treatments (yuan/ha). Item
Raising geese in cornfields (RGICF)
Control (CK)
Conventional corn plant (CCP)
2.90 9691.67 87.89 1120.00 2202.67
— — — 1120.00 2202.67
— — — 1120.00 2202.67
1006.67
1006.67
1006.67
Input Water Fodder Fuel Film Chemical fertilizer Machinery depreciation Nylon net Heating device Herbicide Medicine Land rent Hydropower Labor Corn seeds Baby geese Total
1800.00 133.33 0.00 200.00 6000.00 400.00 4133.33 900.00 4000.00 31678.46
— — — — 6000.00 — 1333.33 900.00 — 12562.67
— — 500.00 — 6000.00 — 1600.00 900.00 — 13329.33
Output Geese Corn Total Output/Input Gross income Net income
26040.00 21458.62 47498.62 1.50 47498.62 15820.16
— 19764.58 19764.58 1.57 19764.58 7201.92
— 22508.89 22508.89 1.69 22508.89 9179.56
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Y. Zhang et al. / Biological Control 99 (2016) 47–52
reduced leaf area (Zhang et al., 2014), leading to a yield decrease. In addition, weed competition for resources such as sunlight and nutrients could also impact corn yield. However, due to geese grazing and trampling on weeds, above-ground weed growth was restrained, and competition for environmental resources was also be reduced. The reduction of corn yield is compensated by the economic gain from geese production, ultimately providing a greater economic benefit than the conventional herbicide (CCP) treatment. 5. Conclusion Geese serve as weed biological control agents and successfully control weed growth while maintaining a higher weed diversity. Geese also serve as economic output in the RGICF treatment produced a relatively large economic benefit. We predict that RGICF will eventually become an important production method for sustainable agriculture. Acknowledgment This project was financially supported by National Natural Science Foundation (31201594); Science and Technology Service Network Initiative of C.A.S. (KFJ-EW-STS-073); utilization of courtyard agricultural resource and development of Potentilla anserine L production in Nyingchi area. The support program for the top-notch young teachers of Agricultural and Animal Husbandry College, Tibet University (2015D0601). References Altieri, M.A., 1994. Biodiversity and Pest Management in Agroecosystems. Food Product Press, New York. Blubaugh, C.K., Kaplan, I., 2015. Tillage compromises weed seed predator activity across developmental stages. Biol. Control 81, 76–82. Derksen, D.A., Anderson, R.L., Blackshaw, R.E., et al., 2002. Weed dynamics and management strategies for cropping systems in the northern great plains. Agron. J. 94, 174–185. Deytieux, V., Nemecek, T., Knuchel, R.F., Gaillard, G., Munier-Jolain, N.M., 2012. Is Integrated Weed Management efficient for reducing environmental impacts of cropping systems? A case study based on life cycle assessment. Eur. J Agron 36, 55–65. El Titi, A., 1995. Ecological aspects of integrated farming. In: Glen, D.M., Greaves, M. P., Anderson, H.M. (Eds.), Ecology and Integrated Farming Systems. John Wiley and Sons, New York, pp. 243–256. Epperlein, L.R.F., Prestele, J.W., Albrecht, H., Kollmann, J., 2014. Reintroduction of a rare arable weed: competition effects on weed fitness and crop yield. Agr. Ecosyst. Environ. 188, 57–62. Goldberg, D.E., Miller, T.E., 1990. Effects of the different resource additions on species diversity in an annual plant community. Ecology 71, 213–225. Guan, F.C., Wang, C., 2011. The theory and technology approach of agro-pastoral integration principle. J. Tibet Agri. Anim. Husbandry Coll. 1, 42–45 (in Chinese). Guan, F.C., Sha, Z.P., Wang, J.F., Tian, F.P., Cai, C.P., 2013a. Growth and development of goose under different spatiotemporal characteristics. Acta Agrestia Sinica 21, 1208–1213 (in Chinese). Guan, F.C., Tian, F.P., Sha, Z.P., Wang, J.F., Li, S.K., 2013b. Contribution of Goose Feeding Space and production effects on the different production modes. J. China Agric. Univ. 18, 129–133 (in Chinese).
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