Volume 4, Issue 1 (2017) Tropical Plant Research

December 2, 2017 | Author: TropPlRes | Category: Citrus, Seed, Plant Nursery, Weed, Germination
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01. Seed germination inhibitory effect of Caryota urens L. seed pericarp on rice and associated weeds. 02. Citrus varie...


ISSN (E): 2349 – 1183 ISSN (P): 2349 – 9265 4(1): 01–06, 2017 DOI: 10.22271/tpr.2017.v4.i1.001 Research article

Seed germination inhibitory effect of Caryota urens L. seed pericarp on rice and associated weeds S. I. Fonseka1, S. Adikari1, L. R. Jayasekera1*, P. Ranasinghe2 and G. A. S. Premakumara2 1

Department of Botany, University of Kelaniya, Sri Lanka 2 Industrial Technology Institute, Colombo, Sri Lanka

*Corresponding Author: [email protected]

[Accepted: 05 January 2017]

Abstract: Previous studies have shown that the Caryota urens seed pericarp possesses botanicals capable of inhibiting seed germination. Extracts made from C. urens pericarp were tested at different concentrations to find out its inhibitory activity. Three rice varieties (Bg 305, Bg 358, Bg 368) and the rice weeds (Echinochloa crus-galli, Ischaemum rugosum and Ipomoea aquatica) were tested. Percentage germination was measured for 7 days at 2 day intervals. The methanol extract of dried seed pericarp significantly reduced the seed germination, indicating that the concentration of the inhibitory substance/s in C. urens pericarp is higher in methanol extracts than the water extracts. Dried seed pericarp showed the highest inhibitory effect on the seed germination. The germination of all rice cultivars and weed species tested were completely inhibited by 100 and 200 mg.ml-1 concentrations, suggesting that the C. urens seed pericarp has a seed germination inhibitory effect on all weeds tested. In general, the germination inhibitory effects were maximal at high concentrations than at lower concentrations. Potential for using C. urens pericarp for weed control is highlighted. Keywords: Caryota urens - Seed pericarp - Germination - Rice - Weeds. [Cite as: Fonseka SI, Adikari S, Jayasekera LR, Ranasinghe P & Premakumara GAS (2017) Seed germination inhibitory effect of Caryota urens L. seed pericarp on rice and associated weeds. Tropical Plant Research 4(1): 1–6] INTRODUCTION The ever increasing demand for food with the exponential increase in human population demands maximal achievements in crop production. Development of effective and environmental friendly weed control measures is one area of importance in this respect. Weeds and weed control have become a major cost factor determining the economic profitability of crop production worldwide. Weeds, being the major biotic stress for most crops including rice, compete with crops for light, nutrients and moisture, resulting in significant decrease of yield and quality of crop harvest. In the light of the above, studies on locally available natural sources that have a potential to control weeds become relevant to develop appropriate, environmental friendly control measures that suit the agricultural background and economy of Sri Lanka. Allelopathy is defined as a mechanism by which plant, directly or indirectly affects, inhibits or stimulates growth of other plants by the production of chemical compounds or allelochemicals released to the environment (Ridenour & Callaway 2001). The use of allelochemicals by allelopathic plants/plant parts for weed management has received attention in recent times (Weston 1996) in view of their environmental friendly nature as opposed to synthetic chemicals. Hence the use of natural substances from plants is considered as a low input and sustainable approach to integrated weed management, a practice that helps reduce the increasing incidences of herbicide resistance in weeds as well (Mayer & Mayber 1989, Materechera & Hae 2008). Studies have shown the inhibitory effects of certain plants not only on weeds but also on growth and yields of crop species. The allelopathic and herbicidal effectiveness of different plant species have shown to depend on the plant part (Oudhia 2003). Therefore investigations are required to explore plants and respective plant parts with effective allelopathic activity, especially in the control of agricultural weeds (Materechera & Hae 2008). The inhibitory effects of extracts obtained from different seed pericarps and plant parts on seed germination of www.tropicalplantresearch.com


Received: 11 October 2016

Published online: 31 January 2017 https://doi.org/10.22271/tpr.2017.v4.i1.001

Fonseka et al. (2017) 4(1): 01–06 various other crops and weeds have been studied by Humaid & Warrag (1999), Kivi et al. (2010). The study reported here is based on the seed germination inhibitory effect of Caryota urens (Family: Arecaceae) seed pericarp. In C. urens the inflorescence is about 3 m in length and emerges at each leaf node from top to bottom. The plant produces pendent clusters of white, unisexual flowers resulting in about 35000 to 40000 seeds per inflorescence. Mesocarp is fleshy, filled with abundant irritant, needlelike crystals. When these fruits fall on the ground, it takes a long time to germinate. Therefore, it can be assumed that the seed pericarp of C. urens may contain seed germination inhibitory substances (Wijesinghe 1992). No study is known to have been conducted related to the seed germination inhibitory effects of C. urens seed pericarp on rice and associated weeds. As no information is available on the pattern of C. urens seed germination, Ranasinghe et al. (2008) conducted an investigation to study the C. urens seed germination pattern and to develop a method for induction of rapid germination. In their study, it was found that the complete removal of fleshy pericarps of ripe C. urens fruits is the most effective way to achieve a higher rate of germination. The prevention of C. urens seed germination may be due to the presence of inhibitory substances in their pericarps which may contain relatively high concentrations of growth inhibitors that can suppress germination of the embryo (Taiz & Zeiger 2010). This research was designed to study the inhibitory effect of C. urens seed pericarp on rice and on some associated weeds. Echinochloa crus-galli (L.) P.Beauv. is a grass weed that can germinate and grow for extended periods of time in an anaerobic environment (Kennedy et al. 1983) which is ecologically similar to rice. Being a highly competitive weed with rice, it can reduce rice yield up to 100% (Nyarko & Datta 1991). Ischaemum rugosum Salisb. is an annual grass weed that grows up to 120 cm in height. This aggressive, highly competitive weed is propagated by seeds. Five plants per square meter reduced 15% rice yield, while 80 plants per m2 reduced 82% rice yield in a study conducted by Nyarko & Datta (1991). Ipomoea aquatica Forssk. is a fast growing, perennial broad leaf vine propagated by seeds and stem cuttings, and can cause up to 30% rice yield loss (Nyarko & Datta 1991). The three lowland rice cultivars, Bg 305, Bg 358 and Bg 360 tested have been developed by the Rice Research and Development Institute in Bathalagoda (Bg) in Sri Lanka (Personal communication, 18 September 2013). MATERIALS AND METHODS Selection of seeds Three rice cultivars (Bg 305, Bg 358 and Bg 368) and the rice weeds (Echinochloa crus-galli (L.) P.Beauv., Ischaemum rugosum Salisb. and Ipomoea aquatica Forssk.) were selected for the experiment. The inhibitory effects of the aqueous extract of Caryota urens seed pericarp on seed germination Ripe fruits of Caryota urens were crushed and the pericarp was separated, homogenized and filtered. The extract was freeze dried. Lipolyzed seed pericarp extract (WESP) was used in germination inhibition experiments. A concentration series (0.5, 5, 50 and 500 mg.ml-1) was made with distilled water. 2000 µl of this concentration series was added to the Petri dishes containing 20 seeds of test species on filter papers separately. Three replicates were used for each concentration. Distilled water was used in control experiment. During the experimental period, seeds were treated with distilled water. Number of germinated seeds and lengths of roots, shoots of germinated seedlings were recorded. Results obtained on percentage germination, root length and shoot length of each treatment were statistically analyzed. The inhibitory effect of the methanol extract of Caryota urens seed pericarp on seed germination Seed pericarps were oven dried for one week at 50 ºC. Dried pericarps were immersed in methanol for a week and filtered through a muslin cloth. Methanol in filtrate was evaporated by rotator evaporation and the crude was freeze dried to remove excess water. The crude obtained from dried seed pericarps (DSPE) was stored in a freezer at -12ºC. Seed germination inhibitory effect was observed using this DSPE crude. In the bioassay on filter papers, a concentration series (0.5, 5, 50 and 500 mg.ml-1) from the DSPE was applied separately into Petri dishes containing 20 seeds of test species. 2000 µl of the extract was added per Petri dish. The dishes were kept wet by adding distilled water during the experimental period. Three replicates were used for each treatment. Distilled water was used for the control experiment. Germination of seeds, the length of shoots and roots of germinated seedlings were recorded. Results of each treatment were statistically analyzed. Bioassay on soil In the bioassay on soil, a higher concentration series (25, 50, 100 and 200 mg.ml-1) was applied separately into www.tropicalplantresearch.com 2

Fonseka et al. (2017) 4(1): 01–06 the Petri dishes with paddy soil containing 20 seeds of test species. 5000 µl of the extract was added per Petri dish. The soil was kept wet by adding distilled water, and the number of germinated seeds in each Petri dish was recorded daily. Three replicates were used for each treatment and distilled water was used for the control experiment. Percentage inhibition, shoot and root length of seedlings of each treatment were statistically analyzed. RESULTS Germination inhibitory effect of water-extracted seed pericarps on Echinochloa crus-galli seeds Reference to table 1, E. crus-galli seeds in the control experiment were started to germinate on the 2nd day, and reached a percentage germination of 84.6% after six days, whereas the germinability of seeds treated with 500 mg/ml of WESP was significantly lower (p0.05) in germination. In comparison with the control and other concentrations, a significantly (p0.05).

E. crus-galli seeds in the control started to germinate on the 2nd day. 25, 50, 100 and 200 mg.ml-1 treated seeds inhibited the germination with a significant difference (p0.05 indicate model terms are significant. In this case feed ratio is significant model terms. Values greater than 0.1 indicate the model terms are not significant. The "Lack of Fit F-value" of 1.54 implies it is not significant relative to the pure error. The values for the coefficient of determination 𝑅2=0.9352 and Adjusted 𝑅2 =0.8184 represents the proportion of variation in the yield or response in the model. Table 6. ANOVA for response surface quadratic model of ethanol recovery.



Model X1 X2 X3 X 12 X 22 X 32 X1*X2 X1*X3 X2*X3 Error Lack-of-fit Pure Error Total

9 1 1 1 1 1 1 1 1 1 5 3 2 14

Squares of sum 27566.7 3200 8450 7950 10016 539.1 1077.6 25 3025 2025 966.7 900 66.7 28533.3

Squares of mean 3063 3200 8450 7950 10016 539.1 1077.6 25 3025 2025 193.3 300 33.3



15.84 16.55 43.71 22.64 51.81 2.79 5.57 0.13 15.65 10.47

0.004 0.01 0.001 0.017 0.001 0.156 0.065 0.734 0.011 0.023



In ANOVA for response surface quadratic model of ethanol recovery of batch solvent extraction method, the model F-value of 1.18 implies the model is significant. There is only a 4.5% chance that a "model F-value" this large could occur due to noise. Values of "Prob0.01) respectively; Control (T1), Cowdung (T2), Annapurna organic fertilizer (T3), 75% Annapurna organic fertilizer + 25% Vermicompost (T4) and Vermicompost (T5). Table 2. Effect of different fertilizer on tuber yield of two potato varietiesX.

Tuber yield Treatments T1 T2 T3 T4 T5 LSD0.01 CV%


kg per plot V1 10.7b ± 0.39 11.3b ± 0.20 12.7ab ± 0.11 14.3a ± 0.37 12.8ab ± 0.27 2.2 6.6

V2 10.1b ± 0.34 11.2b ± 0.35 14.1a ± 0.37 13.7a ± 0.05 11.2b ± 0.29 1.7 5.0

V1 23.5cd ± 0.48 22.2d ± 0.15 26.0bc ± 0.20 28.8a ±0.41 26.2b ± 0.42 2.5 3.62

V2 21.0b ± 0.13 23.5b ± 0.66 28.3a ± 0.72 27.7a ± 0.13 22.9b ± 0.21 3.695 5.47

Note: Values are means of three replicates ± SE; values in a column with having similar and dissimilar superscript letter(s) are significantly similar and different (p>0.01) respectively; Control (T1), Cowdung (T2), Annapurna organic fertilizer (T3), 75% Annapurna organic fertilizer + 25% Vermicompost (T4) and Vermicompost (T5).

Yield: Yield of potato varieties varied significantly among the treatments. In case of V1, maximum yield was found from T4 (14.3 kg per plot and 28.8 t.ha-1) while minimum from T1 (10.7 kg per plot and 22.2 t.ha-1) whereas for the V2, maximum yield was found in T3 (14.1 kg per plot and 28.3 t.ha-1) and minimum was found www.tropicalplantresearch.com


Sikder et al. (2017) 4(1): 104–108 from T1 (10.1 kg per plot and 21.0 t.ha-1) (Table 2). V1 was found as more yielder variety than V2. Grade wise tuber yield: Yield of different graded tuber was not varied significantly among the treatments in both varieties (except V1: 28–55 mm). Maximum yield was found in T4 at 28–55 mm graded tuber (V1: 12.74 kg per plot and V2: 12.31 kg per plot) (Table 3). In this case V1 also found as the better performer than V2. Table 3. Effect of different fertilizer on grade wise tuber yield of two potato varietiesX.

Treatments T1 T2 T3 T4 T5 LSD0.01 CV%

Yield (kg per plot) according to different tuber grade 28–55 mm >55 mm V2 V1 V2 V1 V2 0.81a ± 0.09 9.30d ± 0.42 9.02a ±0.31 0.73a ± 0.06 0.49a ± 0.11 1.02a ± 0.06 9.92cd ± 0.25 10.12a ±0.79 0.66a ± 0.11 0.61a ± 0.10 a ab a a 0.74 ± 0.07 11.86 ± 0.45 12.31 ±0.53 0.57 ± 0.02 0.69a ± 0.06 0.95a ± 0.06 12.74a ± 0.66 11.48a ±0.41 0.41a ± 0.10 0.74a ± 0.09 a bc a a 0.57 ± 0.14 10.67 ± 0.72 10.43 ±0.59 0.44 ± 0.13 0.53a ± 0.13 0.68 1.34 3.92 0.83 0.74 10.45 14.42 11.42 13.96 12.94

0.01) respectively; Control (T1), Cowdung (T2), Annapurna organic fertilizer (T3), 75% Annapurna organic fertilizer + 25% Vermicompost (T4) and Vermicompost (T5).

DISCUSSION The results showed seed potato performed differently on growth and yield to different organic fertilizers. Nitrogen content increases in soil by the application of organic fertilizers may stimulate the faster plant growth that lead to more yield (Nogales et al. 2005). The stimulation of the plant growth in organic fertilizers arises by the presence of the phytohormones (Nogales et al. 2005, Smith et al. 2014). Our results showed that additional application of organic fertilizers with inorganic fertilizers increases the total tuber yield also different graded tuber. Integrated nutrient management by the application of both inorganic fertilizers and organic manures increases the different grades tuber production (Kumar et al. 2008, 2011, Das et al. 2009) and total tuber yield (Kumar et al. 2001, Raghav & Kamal 2008). Yield of tuber increases due to the availability of N, P and K contents in soil through the application of organic manures (Kumar et al. 2008, Baishya 2009, Zaman et al. 2011). The maximum advantages from applications of additional organic fertilizers with recommended doses of inorganic fertilizers might be found and i.e., to enhance uptake of fertilizer, to increased soil physical and chemical properties. Besides, by providing macro and micronutrient organic manure improve crop production. Potato yielded more tuber from manure application along with inorganic fertilizers (Johnston 1986, Nyiraneza & Snapp 2007, Bereez et al. 2005, Alam et al. 2007, Gruhn et al. 2000, Daniel et al. 2008). In our study potato tuber size 55 mm were considered as undersized, marketable and oversized as similar to Chilephake & Trautz (2014). In case of the 28–55 mm grade tuber, all the treatments had significant effect in asterix but it was not found any significant effect in diamant variety. Significant difference for grade wise tuber yield was found among different genotypes (Bhardwaj et al. 2008) and different treatments (Banjare et al. 2014, Chilephake & Trautz 2014) while non-significant difference was also found by Banjare et al. (2014). CONCLUSION Both the asterix and diamant variety were very popular to farmers in Bangladesh. It was found that asterix was better than diamant variety considering tuber yield. The asterix variety showed best performance in T4 (75% Annapurna organic fertilizer + 25% Vermicompost) with BARI recommended inorganic fertilizers among the treatments used in the study. Annapurna organic fertilizer (T3) was found as the best treatment for diamant. It is recommended to use BARI recommended inorganic fertilizers with T4 treatment for asterix and T3 for diamant. But further research is suggested using combination of organic and inorganic fertilizers in different areas of Bangladesh. From the results of the current study it can be concluded that use of the organic fertilizers with BARI recommended inorganic fertilizers can improve the tuber yield of potato. ACKNOWLEDGEMENT Authors are highly grateful to Bangladesh Agriculture Development Corporation (BADC) for providing the entire experimental facilities. www.tropicalplantresearch.com


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ISSN (E): 2349 – 1183 ISSN (P): 2349 – 9265 4(1): 109–114, 2017 DOI: 10.22271/tpr.2017.v4.i1.017 Research article

Colonization of stem borer damaged maize plants by aflatoxigenic Aspergillus species in Zimbabwe N. Nleya*, Y. O. Nyararai, M. Mawanza and F. N. Mlalazi National University of Science and Technology, Department of Applied Biology and Biochemistry, Box AC 939, Ascot, Bulawayo, Zimbabwe *Corresponding Author: [email protected]

[Accepted: 05 March 2017]

Abstract: The worldwide contamination of food and feed with mycotoxins is a significant problem. Aflatoxins are major mycotoxins of agro-economic importance produced by some Aspergillus species. Aflatoxigenic Aspergillus species were isolated from insect larvae damaged maize stalks and characterized. The fungi were isolated from the stems, ears and silks of the plant. Aflatoxin production by the isolates was determined using the ammonium hydroxide method as well as the ultra violet fluorescence method. The Lepidopteran species feeding on the crop was identified as Busseola fusca. The Aspergillus species isolated were identified as Aspergillus flavus and Aspergillus parasiticus. This was done using macro- and microscopic features. A total of 35 isolates were obtained of which only 17 were aflatoxigenic. From the aflatoxigenic species 88% were A. parasiticus whilst 12% were A. flavus. Insects were however negative for aflatoxigenic fungi implying that these insects do not act as direct vectors of aflatoxigenic fungi, but however predispose the crop to fungal infection by damaging the physical barrier. Keywords: Aflatoxins - Busseola fusca - Stalk borer - Vectors. [Cite as: Nleya N, Nyararai YO, Mawanza M & Mlalazi FN (2017) Colonization of stem borer damaged maize plants by aflatoxigenic Aspergillus species in Zimbabwe. Tropical Plant Research 4(1): 109–114] INTRODUCTION Corn (Zea mays) is the most important cereal crop in the world after wheat and rice and belongs to the grass family Gramineae (Karthikeyan et al. 2013). Cereal grains and their products are the main foods for human consumption throughout the world. In Zimbabwe maize is of great importance as it the primary staple food and occupies about half of the agricultural land (Sithole 1989). It is consumed in a wide variety of ways. Green maize fresh on the cob is eaten roasted or boiled; grain is used in traditional dishes (such as umxhanxa and inkobe) or ground to mealie-meal to make porridge and paste (sadza/ isitshwala). Maize is also an important livestock feed both as silage and as crop residue. The maize grain is vulnerable to contamination by mycotoxigenic fungi if damaged by insect pests. These moulds include Aspergillus, Penicillium and Fusarium species. Aflatoxins are a group of mycotoxins produced by some Aspergillus species namely, A. flavus, A. parasiticus and rarely A. nomius (Dvorackova 2000). These moulds are able to colonize a wide range of crops both in the field as non-destructive pathogens and in storage. Damage from insects feeding provides preferential sites for penetration by fungi, with some insects acting as vectors of fungi (Samapundo 2006). The main insects implicated in maize production is the maize stem borer complex which comprise of the maize stalk borer; Busseola fusca, the pink stalkborer; Sesamia calamistis, the spotted stem borer; Chilo partellus and the sugarcane stem borer; Eldana saccharina (Capinera 2008). Stem borer infestations range from 30–70% in fields of poor resourced farmers and less than 30% in commercial farms where insecticides are used for control (Sithole 1989). Insect larvae that feed on developing corn kernels are implicated in the establishment of Aspergilli infection and subsequent contamination of the seed with aflatoxins before harvest. These insects facilitate the entrance of Aspergilli from outside the plant into the ears of the crop (Lawley et al. 2012). Insects infect kernels through the following ways; (i) they transport primary inoculum to the ears, (ii) they move inoculum from the silks into the ear, (iii) they disseminate inoculum within the ear and (iv) they wound intact tissue providing more infection www.tropicalplantresearch.com


Received: 16 November 2016

Published online: 31 March 2017 https://doi.org/10.22271/tpr.2017.v4.i1.017

Nleya et al. (2017) 4(1): 109–114 sites. Wounding may also allow kernels to dry down to moisture levels that support the growth of Aspergillus and subsequent aflatoxin production (Giorni 2007, Samapundo 2006). Aflatoxins are one of the major etiological factors in the development of hepatocellular carcinoma (IARC 2002). There are four naturally occurring aflatoxins aflatoxin (AF) B1, B2, G1 and G2. Mammals that ingest AFB1 and AFB2 contaminated diets eliminate into milk amounts of the principal 4-hydroxylated metabolite known as “milk toxin” or AFM1 and AFM2. These hydroxylated metabolites are potential contaminants in dairy foods. As such, aflatoxins are not only of importance in humans alone but in animals as well (Samapundo 2006). This results in corn, when consumed as a staple food, being a major dietary source of mycotoxins to both public and animal health (Samapundo 2006). The aim of the study was to isolate and characterize aflatoxigenic Aspergillus that are associated with insect larvae damaged maize stalks in order to assess the impact of damage to aflatoxin contamination in maize. METHODS AND MATERIALS Sampling A total of 20 maize plants damaged by the maize stalk borer were obtained from a small scale field in Manningdale, Bulawayo in Zimbabwe. The maize plants were taken to the laboratory where they were sundried to reduce the moisture content. Nodes of the stem showing insect damage were cut and the surface swabbed using 70% ethanol. Using a sterile blade, each node was cut vertically so as to expose the damaged tissue. Corn ears showing insect damage were removed from the stalk, and the leaves were removed to expose the damaged tissue. Scrapings of the damaged grain were aseptically placed on malt extract agar and the plates incubated at 25°C for 5 days. From each of the 20 ears, 2 mm portions of the silks were excised and placed on MEA and incubated at 25°C for 5 days. The insect larvae that were found feeding on the stems and ears were surface sterilized using 2% sodium hypochlorite, followed by two washes in sterile water. This was done so as to remove any microorganism on the surface of the insect, and to allow isolation of fungi present within the insect. Each insect was then placed on MEA, and the plates incubated at 25°C for 5 days. Insect pupae obtained from both stems and ears were placed in jars, where they were allowed to complete their life cycles so as to enable easy identification of the species.

Figure 1. A, Maize plant showing signs of stalk borer attack; B, Maize stalk showing points through which the stalk borer penetrated the plant; C, Pupae of the stalk borer inside the maize stalk; D, Ears showing damage by the stalk borer; E, Busseola fusca larvae isolated from maize plants.



Nleya et al. (2017) 4(1): 109–114 Identification of isolates Identification of the fungal isolates was done using colony morphology and microscopy. Microscopy was done using Motic digital microscope and the captured images were identified by comparing images from (Baquaiao et al. 2013, Ellis et al. 2007, Watanabe 2002). Detection of aflatoxins The isolates’ ability to produce aflatoxins was carried out using the ammonium hydroxide method by Saito & Machida (1999) after growing the isolates on yeast extract sucrose (YES) agar. To determine the type of aflatoxin produced by positive isolates from the ammonium hydroxide test, the UV fluorescence determination according to Davies et al. (1987) using coconut agar media was carried out. RESULTS

Figure 2. The distribution of Aspergillus in the maize plants.

The insects that were isolated from the maize stalks belonged to one species i.e. Busseola fusca as shown in figure 1. From the 20 maize plants that were sampled Aspergillus was isolated from 12 maize plants. Figure 2 shows the distribution of Aspergillus species in maize plants. Aspergillus was isolated from 63% of the plants sampled whereas no Aspergillus was present in the remaining 37%. Table 1. Macroscopic features used to identify the Aspergillus isolates.

Colony morphology (Macroscopic) (i)

Colony colour


Yellowish green

Aspergillus flavus

Dark green

Aspergillus parasiticus




Nleya et al. (2017) 4(1): 109–114 Table 2. Microscopic features used in the identification of Aspergillus isolates.

Microscopic image (i)



Large globose sclerotia Aspergillus flavus Biseriate metualae


Large globose sclerotia Uniseriate metualae

Aspergillus parasiticus

Septate conidiophore

A total of 35 isolates was obtained. Isolates were obtained from the ears, silks and stems. No Aspergillus was isolated from the insects. The 35 isolates belonged to two species namely Aspergillus flavus and Aspergillus parasiticus. The predominating species was A. parasiticus. Macroscopic and microscopic features were used in the identification of the species as shown in table 1 and 2 respectively. The isolates’ ability to produce aflatoxins was done using the ammonium hydroxide test were aflatoxin producers were identified by a change in colour as shown in figure 3.

Figure 3. Photograph of an aflatoxigenic isolate before (A) and after (B) screening. Before screening the underside of the plate was cream in colour, and after screening the plate had developed an orange-pinkish colour due to reaction of aflatoxin biosynthetic intermediates and ammonium hydroxide.

DISCUSSION Stalk tunnelling was evident in all the maize plants that had been attacked by the stalk borer (Fig. 1C) due to the feeding of the insect larvae feeding on the plant stem tissue. The insect species was identified as Busseola fusca (Fig. 1D) which is also known as the African maize stalk borer or the Fuller, and it is the most common maize pest in Zimbabwe and Sub-Saharan Africa (Sithole 1989). The tunnelling action of B. fusca predisposes the plant to penetration by fungi, some of which are aflatoxigenic (Samapundo 2006). Members of the Aspergillus species section Flavi where isolated from the tunnel scrapings and these were A. flavus and A parasiticus. These were identified by their characteristic conidial heads which occur in shades of yellow-green www.tropicalplantresearch.com


Nleya et al. (2017) 4(1): 109–114 to brown (Gupta 2012) as shown in table 1. Aspergillus species were isolated from 63% of the maize plants. A. parasiticus and A. flavus are the major aflatoxigenic species in the world (Samapundo 2006). Both species produce the most potent aflatoxin, AFB1. This ability to produce AFB1 in maize makes them a significant threat to both humans and livestock who may consume this contaminated maize and maize stalks respectively. In addition to AFB1, A. flavus and A. parasiticus produce AFB2. Together AFB1 and AFB2 when consumed by mammals are metabolically biotransformed to AFM1 and AFM2 and are excreted in milk. This has a negative impact on nursing animals which may suffer from aflatoxicosis (Gupta 2012). Also, in the commercial set up, contamination of feed with A. flavus and A. parasiticus may result in contamination of the milk designated for consumer use. If regulatory laws are thorough, such milk may not be released for consumer use, thus entailing economic losses to dairies. In order to determine the role of insects as vectors of fungi, insect larvae were surface sterilized and cultured on MEA. No aflatoxigenic fungi or Aspergillus species was isolated from them. This means that insects may not directly act as hosts of fungi. The stalk borer breaks open the physical barrier allowing the fungi to access the plant tissue. According to Giorni (2007) wounding of the kernels and stems by insects also reduces the moisture content and this favours the proliferation of A. flavus with subsequent aflatoxin production. It was observed that the stems were damaged at several sites per stem node as compared to the damage per ear (Figs. 1B &1D). This trend is greatly explained by the lifecycle of B fusca on maize plants. The female B. fusca moth oviposit its eggs behind the vertical edges of leaf sheaths. After hatching the first instars migrate to the whorl where they feed on young and tender leaves deep inside the whorl. From the third instar onwards, larvae migrate to the lower parts of the plant where they penetrate into the stem. These larvae begin to feed on the stem, going up the plant to the ear. Thus the ear is usually infested by the pests at much later stages (Calatayad et al. 2014, Frérot et al. 2006). Results show that the stems had the highest prevalence of aflatoxigenic fungi of 65%, followed by the silks (42%) and the ears had the least prevalence of 33% (Fig. 2). The observed trend can be explained by the nutritional content, moisture content and environmental differences between these plant tissues. Stems have a high nutritional content packed with sugars and complex carbohydrates (Plessis 2003). This is favourable for both insect and fungal growth. Thus most fungi were able to proliferate within the stems as compared to the ears and silks. The silks had a moderate aflatoxigenic fungi prevalence of 42%. Silks are exposed to the atmosphere as such these toxigenic fungi isolated may not be a result of insect activity, but a result of aflatoxigenic fungal spores flying in the atmosphere which adhere to silk surfaces. In Zimbabwe, maize is the staple food consumed by the vast majority of the population. In the rural areas, households grow the crop themselves and use it to make the mealie-meal, which is consumed on a daily basis all year round. These communities often do not make use of pesticides due to financial constraints. As a result, damage of the crop by insects such as B. fusca as demonstrated in the study, allows growth of A. flavus and A. parasiticus. This means that these communities are at a risk of aflatoxin poisoning. Furthermore, crop residues that remain after a season’s harvest are given to cattle as feed during the dry season exposing the livestock to aflatoxin poisoning as the dry stalks and cobs offer the desirable conditions for Aspergillus proliferation, as demonstrated in the study. Therefore there is a need to develop strategies of controlling or eliminate stem borers in the communal areas such as cultural control. Cultural control is an economical method of stem borer control and has been adopted in West Africa. It includes methods such as removal and destruction of crop residues, intercropping, crop rotation and manipulation of planting dates (Ogah & Ogbodo 2012). Biological methods can also be used to control the maize stalk borer for example the exchange of species and strains of natural enemies between regions, and the use of non-co-evolved natural enemies, as well as habitat management solutions, namely the use of trap plants such as wild grasses on which larval mortality can be very high (Cherry et al. 1999). ACKNOWLEDGEMENTS The authors would like to thank the support provided by the National University of Science and Technology and the Technical staff of the Department of Applied Biology and Biochemistry for their support. REFERENCES Baquaiao AC, de Oliveira MMM, Reis TA, Zorzete P, Atayde DD & Correa B (2013) Polyphasic Approach to the identification of Aspergillus section Flavi isolated from Brazil nuts. Food Chemistry Journal 139: 1127–1132.



Nleya et al. (2017) 4(1): 109–114 Calatayad PA, LeRu BP, van den Berg J & Schulthess F (2014) Ecology of the African Maize Stalk Borer, Busseola fusca (Lepidoptera: Noctuidae) with Special Reference to Insect-Plant Interactions. Insects 5(3) 539–563. Capinera JL (2008) Encyclopedia of Entomology. Springer, USA, pp. 270–273. Cherry AJ, Lomer CJ, Djegui D & Schulthess (1999) Pathogen incidence and their potential as microbial control agents in IPM of maize stem borers in West Africa. BioControl 44: 301–327. Davies ND, Iyer SK & Diener UL (1987) Improved method of screening for aflatoxin with a coconut agar medium. Applid Environmental Microbiology 53: 1593. Dvorackova I (2000) Aflatoxins and human health, CRC Press. USA, pp. 3–10. Ellis D, Davis S, Alexiou H, Handke R & Bartley R (2007) Descriptions of Medical fungi, 2nd Edition. School of Molecular and Biomedical Science, University of Adelaide, Australia. Frérot B, Félix AE, Sarapuu E, Calatayud PA, LeRü B & Guenego H (2006) Courtship behaviour of the African Maize Stem Borer: Busseola fusca (Fuller) (Lepidoptera: Noctuidae) under laboratory conditions. International Journal of Entomology 42(3–4): 413–416. Giorni, P (2007) Impact on Environmental and Plant actors on Aspergillus section Flavi isolated from maize in Italy, Ph.D. Thesis. Faculty of Medicine and Biosciences, Cranfield Univesity, Italy. Gupta RC (2012) Verterinary Toxicology. Basic and clinical Principles, 2nd Edition. Elsevier, Inc. USA, pp. 1181–1190. IARC (International Agency for Research on Cancer) (2002) Traditional herbal medicines, some mycotoxins, napthalene, and styrene. Monographs on the evaluation of carcinogenic risks to humans 82: 171. Karthikeyan M, Karthikeyan A, Velazhahan R, Madhavan S & Jayaraj T (2013) Occurrence of aflatoxin in maize kernels and molecular characterisation of the producing organism, Aspergillus. African Journal of Biotechnology 12: 583–5840. Lawley R, Curtis L & Davis J (2012) The foods Safety hazard guide book. Royal Society of Chemistry, USA, pp. 207–211. Ogah ED & Ogbodo EN (2012) Assessing the Impact of Biodiversity Conservation in the Management of Maize Stalk Borer (Busseola fusca, F.) in Nigeria. Current Trends in Technology and Science 2(2): 234–238. Plessis J (2003) Maize production. ARC-Grain Crops Institute, South Africa, pp. 1– 9. Saito M & Machida S (1999) A rapid identification method for aflatoxin producing strains of Aspergillus flavus and A parasiticus by ammonia vapour. Mycoscience 40: 205–208. Samapundo S (2006) Post-harvest strategies for the prevention of fungal growth and mycotoxin production in corn, Ph.D. Thesis. University of Ghent, Belgium. Sithole ZS (1989) Towards insect resistance maize for the third world proceedings- Symposium on methodologies for developing host plant resistance to maize insects. International maize and wheat improvement centre, pp. 286–288. Watanabe T (2002) Pictorial Atlas of soil and seed fungi- Morphologies of cultured fungi and key to species, 2nd Edition. CRC Press, USA.



ISSN (E): 2349 – 1183 ISSN (P): 2349 – 9265 4(1): 115–125, 2017 DOI: 10.22271/tpr.2017.v4.i1.018 Research article

Evaluation of land-use land-cover change with changing climatic parameters of a watershed of Madhya Pradesh, India Sandeep Soni Remote Sensing and GIS Lab, Mahatma Gandhi Chitrakoot Gramoday Vishwavidyalaya Chitrakoot, Satna, Madhya Pradesh, India *Corresponding Author: [email protected]

[Accepted: 09 March 2017]

Abstract: In this present study an attempt has been made to determine trend of climatic parameters (precipitation and temperature) as well as LULCC for the watershed located near the Achanakmar-Amarkantak biosphere reserve of Central India. For analyzing trends of LULCC, Remote Sensing technique is used and Landsat satellite data of five different years (1990, 2000, 2005, 2011, and 2013) are procured and eight LULC classes such as settlement, river, water bodies, high dense vegetation, low dense vegetation, fallow land, open land and agriculture are identified. The trend analysis carried out over the LULCC data which shows that the high dense vegetation and agricultural lands are decreasing while settlement, fallow land and low dense vegetation lands are increasing. Simultaneously, when both parametric and nonparametric methods of trend analysis are applied over the annual precipitation and temperature (maximum, mean and minimum) data for the period of 1981 to 2011, significant (p-value < 0.05) decreasing and increasing trends are observed, respectively. Although, the present study does not include an establishment of an empirical relationship of LULCC and climate change, result of this study is a strong indicator of decreasing high dense vegetation having local impacts of decreasing rainfall and vice-versa. Keywords: Land-use land-cover - Remote Sensing - GIS - Precipitation & Temperature - Trend Analysis. [Cite as: Soni S (2017) Evaluation of land-use land-cover change with changing climatic parameters of a watershed of Madhya Pradesh, India. Tropical Plant Research 4(1): 115–125] INTRODUCTION The anthropogenic activities of post Industrial era, have caused significant emission enhancement of greenhouse gases resulting the global warming. Subsequently, climate change has become the most important environmental plight of the present time (IPCC 2007 a,b,c,d). However, along with the climate change issue of present time, rapid land use and land cover change (LULCC) has also taken place throughout the world due to urbanization, deforestation and population growth (Duncan et al. 1993, Parker & Alexander 2002, Delang 2002, Duram et al. 2004, Fromard & Vega 2004). It is now presumed that LULCC and climate change are interlinked with each other (Turner et al. 2007). Since the global and hemispheric climate change can have direct impacts on temperature and precipitation (rainfall, snow etc.) distribution, enhancement of flood and avalanche frequency along with changes in the agricultural productivity of a country or region, impact assessments of climate change for a particular ecosystem or targeted land practices such as national parks, forests or river basins are necessary. Simultaneously, LULCC is an important parameter contributing to local and regional climate change (Chase 1999, Pielke et al. 2002). A recent study estimated that 40% of the global temperature rise is due to world-wide change in land use (Kalne & Kai 2003, Munoz-Villers & Lopez-Blandco 2008). The LULCC of a region is so pervasive that, when aggregated globally, the LULCC significantly affects the key aspects of Earth system functioning (Muttitanon & Tripathi 2005). The LULCC is the primary source of soil and forest degradation (Tolba & El-Kholy 1992) which alters the ecosystem services of a particular land type and affect the ability of biological systems to support human needs (Sala et al. 2000, Vitousek et al. 1997). However, enough attention www.tropicalplantresearch.com


Received: 20 November 2016

Published online: 31 March 2017 https://doi.org/10.22271/tpr.2017.v4.i1.018

Soni (2017) 4(1): 115–125 was not provided to the LULCC during several climate studies which involve detection of climate controlling factors (Reddy & Gebreselassie 2011). According to the third assessment report of Intergovernmental Panel on Climate Change (IPCC), climate studies involving detection and attribution techniques of climate controlling factors have not taken into account the anthropogenic forcing such as changes in the Land use and Land cover (LULC) (IPCC 2001). Several national and international research communities have studied the reason, trend and impacts of climate change at global, hemispherical and regional scale (Chase 1999, Joeri 2011, Paeth 2009). Temperature and precipitation are assumed to be one of the important climatic parameters that represent climate change on a long-term basis. Therefore, several explorations on climatic trends have been conducted by analyzing precipitation and temperature data at different periods of records throughout the world (Dessens & Bucher 1995, Serra et al. 2001, Marengo 2004, Longabardi & Villani 2009). Most of these studies have shown that the trend of temperature or precipitation distribution of a particular region is either decreasing or increasing. A recent study by Karl et al. (1993) have shown that the monthly minimum temperature, from countries comprising 37% of the global landmass, is increasing by 0.84oC compared to only 0.28oC increase in maximum temperature for the period 1951–1990. The IPCC (2007e) report has also demonstrated that the global surface warming is occurring at a rate of 0.74±0.18 °C during 1906–2005. The maximum and minimum temperature datasets are important because minimum temperature alone is almost certainly not a good parameter to detect heat accumulation in the atmosphere associated with climate changes (Pielke & Matsui 2005). In addition, minimum temperature is much more sensitive to land use change than maximum temperature (Hale & Gallo 2008, Runnalls & Oke 2006, Walters et al. 2007). In India, the climate change is expected to adversely affect forestry, agriculture, temperature, and rainfall distribution. Climate change is also expected to change the monsoon onset (Lal et al. 1994, Panigrahy et al. 2009) and increase extreme events such as floods (Booij 2005), droughts (Loukas et al. 2008) and devastating cyclonic storms (Knutson et al. 2010) which have direct consequences on the population and the economy of the country (Fulekar & Kale 2010). Therefore, climate change studies are of paramount importance and efforts are continuing to understand the trend of climatic regimes over the Indian subcontinent (Aggarwal et al. 2004, Mall et al. 2006, Rupa-Kumar et al. 2006, Joshi & Rajeevan, 2006, Samui & Kamble 2009). A study by Kothawale & Rupa-Kumar (2005) has indicated that the mean annual temperature of the Indian subcontinent has increased at a rate of 0.05°C per decade during 1901–2003 mostly due to the rise of maximum temperature (0.07°C per decade) rather than the increase of minimum temperature of 0.02°C per decade. However, the LULCC has an inter-relationship with the temperature and precipitation distribution of an area, and depending on the feedback processes within land and atmosphere, impact of the climate change can be assessed. A study by Douglas et al. (2006) on the changes in moisture and energy fluxes due to agricultural land use and irrigation in the central Indian region has shown that the increasing agricultural land use has contributed significantly in increasing the vapor flux of this region which could modulate the local to regional scale cloud formation and subsequently, modulate the precipitation distribution. Therefore, it is important that we understand the LULCC of a particular river basin area or a targeted land practice area, so that the interrelationships between the climate parameters and the LULCC can be explored (Turner et al. 1994, Knorr et al. 2011). Therefore, the present study aims to explore the long-term land use land cover changes of a watershed with particular focus on investigating changes in the meteorological parameters of the area and their relationships with LULCC. The Chakrar watershed of Madhya Pradesh is chosen for this purpose. The Chakrar watershed is an important sub-tributary of the Narmada basin and constitutes part of the Achanakmar-Amarkantak Biosphere Reserve. The watershed has dominant Sal (Shorea robusta Gaertn.) forest and due to increasing human population in the last few decades, it is hypothesized that the forest area within this watershed is degrading. An in-depth analysis of land-use land-cover change of the watershed and subsequent analysis of meteorological parameters over the study area is, therefore, envisaged to provide (i) whether the underlying hypothesis of degrading forest cover over the watershed is true and (ii) enhanced understanding of the inter-relationships between climatic and LULCC drivers. MATERIAL AND METHODS Study Area The study area is Chakrar Watershed of Dindori District, Madhya Pradesh, India (Fig. 1). The watershed has www.tropicalplantresearch.com 116

Soni (2017) 4(1): 115–125 rich Sal (Shorea robusta Geartn.) dominant forest with altitudinal range 700–980 m. The watershed extends from 22o31' 12.24" to 22o52' 44.93" N latitude and 81o14' 41.23" to 81o28' 29.42" E longitude. Total catchment area of the Chakrar watershed is 415 km2. The area falls under sub-tropical monsoon climatic region. Average annual precipitation of this watershed varies between 1200–1306 mm and annual temperature varies between 18–43 ºC. Maximum amount of rainfall occurs during monsoon season i.e. June to September.

Figure 1. Location map of the study area.

Data Used In order to investigate the LULCC of the study area, Landsat 5 TM (path 143, row 44) satellite data (Ioannis & Meliadis 2011) are used for the year of 1990 and Landsat 7 TM (path 143, row 44) satellite data are used for 2000, 2005, 2011 and 2013. Satellite data was procured from USGS websites (www.glovis.usgs.gov). Yearly continuous data of LULCC was unavailable from the Landsat products over the experimental area. Hence, only five years of LULCC of the study area was investigated. However, the Meteorological time series data of annual precipitation, mean temperature, maximum temperature and minimum temperature for 30 years (1981–2011) are acquired from India Water Portal (www.indiawaterportal.org/met_data/) for the study area. Land-Use Land-Cover Change Analysis Satellite data were processed using ERDAS IMAGINE 9.2 software for geometric corrections (image to image georectification) with the coordinate system UTM WGS 84, Zone 44 North to make these images compatible (Lillesand & Kiefer 1994). The images of 1990, 2000, 2005, 2011 and 2013, resampled to 30m x 30m pixel size using the nearest neighbor resampling technique (Serra et al. 2003, Jensen 2005). Pixel based supervised image classification with maximum likelihood classification algorithm was used to map the land-use land-cover classes (Lillesand & Kiefer 1994, Shalaby & Ryutaro 2007). Eight LULC classes viz., Settlement, River, Water bodies, High Dense Vegetation, Low Dense vegetation, Fallow land, Open land and Agriculture were identified for image classification. The pre and post field visit was done with a GPS receiver and using a set of questionnaire designed for the purpose. GPS points were selected for ground truth validation and verification of location (latitude and longitude) and elevation. The accuracy assessments were performed for classified images of 1990, 2000, 2005, 2011 and 2013. A minimum of about 40 random points were generated per class using stratified random sampling approach for efficient accuracy assessment (Congalton & Green 2009). The corresponding reference class for each LULC type was collected from different data sources, including data from field visits, topographic maps, and raw images. Raw images were used for those visually visible classes, e.g., forests and water bodies www.tropicalplantresearch.com


Soni (2017) 4(1): 115–125 (Congalton & Green 2009). Topographic maps were utilized to collect reference samples for 1990 classified images while field visits data were mainly used for the 2013 classified image. Reference points for the 2000, 2005 and 2011 classified image were collected through visual interpretation of the raw Landsat TM 2000, 2005 and 2011 image. This was supplemented by field visits and discussion with elders in the study landscape that made it possible to establish reference points of different classes. Trend Analysis The trend is a significant change over time exhibited by a random variable, detectable by statistical nonparametric and parametric procedures. According to Önöz & Bayazıt (2003), parametric t-test has less power than the non-parametric Man-Kendall test when the probability distribution is skewed. With due trend detection and cross verification, both parametric and non-parametric statistical procedures are applied to the precipitation and temperature time series. Mann-Kendall Test Mann-Kendall test is a non-parametric statistical test used to assess the significance of trends in climatic time series data such as precipitation and temperature (Mavromatis & Stathis 2011). Non-parametric tests are thought to be more suitable for non-normally distributed data which are encountered in climatic time series (Yue et al. 2002). Man-Kendall test was suggested by Mann (1945) for randomness against time, which constitutes a particular application of Kendall’s test for correlation commonly known as the ‘Mann-Kendall’ or the ‘Kendall t test’ (Kendall 1962). The test has been extensively used with environmental time series (Hipel & McLeod 1994, McLeod et al. 1990). Let X1, X2,……………Xn represents data points over time, in the test null hypothesis H0 is tested where the random variables are independent and identically distributed. The alternative hypothesis H A, is that the data are not identical. Under H0, the Mann-Kendall test is calculated by using following equations (Lazaro et al. 2001, Önöz & Bayazit 2003, Kahya & Kalayci 2004).


Under the hypothesis of independent and randomly distributed random variables, when n ≥ 8, the S statistics is approximately normally distributed with the mean. The variance statistic is given as

Where, p is the number of tied groups in the data and tj is the number of data values in the jth tied group. As a consequence, the standard test statistic Z is computed as follows

The test statistic Z is used a measure of significance of trend and used to test the null hypothesis, H0. The test statistic Z follows a standard normal distribution. A positive (negative) values of Z signifies an upward (downward) trend. Spearman’s Rho Spearman’s Rho is a rank based test to determine the significance of correlation between two variables that can be used to test for a correlation between time and the data series (Siegel & Castellan 1988). This test evaluates the degree to which individuals or cases with high rankings on one variable were observed to have similar ranking on another variable (Sprent 1989). www.tropicalplantresearch.com


Soni (2017) 4(1): 115–125 The test statistic ρs is the correlation coefficient, which is obtained in the same way as the usual sample correlation coefficient, but using ranks:


and xi (time), yi (variable of interest), trend analysis). For time series, the quantity


refer to the ranks ( ,

, Sx and Sy have the same value in a

is normally distributed mean of 0 and variance of 1.

Linear Regression Linear regression is a parametric test which is one of the most common trend test and in its basic form assumes that data is normally distributed. The test statistic for linear regression is the regression gradient. The test is used to test for linear trend by the linear relationship between time and the variables of interest. The linear regression gradient is calculated by

And the intercept is estimated as The test statistic S is Where,

The application of this test assumes that the errors (deviations from the trend) are independent and follow the same normal distribution with 0 mean. RESULTS AND DISCUSSION Accuracy assessment of the supervised classification of the satellite imagery was derived by using a reference template from the margining data with 40 randomly selected samples on the latest imagery, from which overall accuracy and Kappa statistics were derived. The Kappa statistics incorporated the diagonal elements of the error matrices (Yuan et al. 2005). Satellite imageries of 1990, 2000, 2005, 2011 and 2013 were classified (Fig. 2) and validated using error matrix and Kappa statistics. The overall accuracy was found to be 91 percent whereas overall Kappa statistics was 0.8898. The statistics shows that the result was overall good. Land use land cover maps from Landsat imageries of 1990, 2000, 2005, 2011 and 2013 were produced and trend analysis of LULCC were carried out. Regardless of proportion of changes in the size and type of land cover, significant changing trends were observed between 1990 and 2013. The major land cover classes such as: settlement, fallow land, and low dense vegetation show increasing trend in land cover areas (Fig. 3A,B,C) having r2 of 0.90, 0.89 and 0.72, respectively. Settlement area was found to increase by 1.87%, whereas the fallow land and low dense vegetation were found to increase by 8.99% and 4.78%, respectively. Significant decreasing trends were observed for high dense vegetation and agriculture classes. High dense vegetation, which was spread over 139.83 km2 area in 1990, degraded to only 95.85 km2 in 2013 (Table 1) and remaining proportion of lands were found to transform to low dense vegetation and fallow land (Fig. 2A–E). A significant www.tropicalplantresearch.com


Soni (2017) 4(1): 115–125 decrement of 10.60% is observed in high dense vegetation with r2 of 0.84 (Fig. 3A–F). Agriculture land which were spread over 53.38 km2 during 1990 reduced to 31.29 km2 in 2013 showing a decreasing trend with r2 = 0.54 and loss of 5.32%. A trend relationship between high dense vegetation with rainfall and agriculture with rainfall shows a decreasing trend (Fig. 4A, B). Table 1. Land use land cover changes in Chakrar Watershed during 1990–2013. [Reproduces from Soni et al. 2015]


Settlement River Waterbodies High Dense Vegetation Low Dense Vegetation Fallow Land Open Land Agriculture Total

1990 Area Area (km2) (%) 4.91 1.18 2.67 0.64 0.24 0.06 139.83 33.69

2000 Area Area (km2) (%) 6.41 1.54 2.71 0.65 0.26 0.06 132.15 31.84

2005 Area Area (km2) (%) 10.96 2.64 2.73 0.66 0.34 0.08 131.73 31.74

Area (km2) 12.29 2.73 0.43 96.25

2011 Area (%) 2.96 0.66 0.10 23.19









2013 Area Area (km2) (%) 12.69 3.06 2.72 0.66 0.48 0.11 95.85 23.10 74.37


143.04 34.46 16.40 3.95 53.39 12.86

141.15 34.01 11.83 2.85 62.07 14.95

159.25 38.37 17.74 4.28 37.35 9.00

162.56 39.17 16.63 4.01 48.60 11.71

180.33 43.45 17.29 4.17 31.29 7.54

415.03 100.00

415.03 100.00

415.03 100.00

415.03 100.00

415.03 100.00

Figure 2. Land use land cover map of Chakrar Watershed during: A, 1990; B, 2000; C, 2005; D, 2011; E, 2013. [Reproduces from Soni et al. 2015]



Soni (2017) 4(1): 115–125

Figure 3. Graph of trend analysis of land use/cover classes: A, Settlement; B, High dense vegetation; C, Low dense vegetation; D, Fallow land; E, Open land; F, Agriculture land. [Reproduces from Soni et al. 2015]

Figure 4. Trend relationship between: A, High dense vegetation and rainfall; B, Agriculture and rainfall with respective years.



Soni (2017) 4(1): 115–125 Since, it is hypothesized that a major proportion of this LULCC is associated with changing climatology of the area; trends of two major climatic indicators (precipitation and temperature) were also computed. Along with the non-parametric Mann-Kendall test for identifying trend of rainfall and temperature for 30 years over the study area, parametric Linear Regression test for trend analysis and Spearman’s Rho test for correlation analysis with time were also carried out for the climatic indicators. In the Mann-Kendall test, Z statistics and S score for annual rainfall revealed negative trend at p-value < 0.05 (Table 2A). For annual maximum temperature, mean temperature and minimum temperature positive trend at p-value < 0.1 were observed (Table 2B,C,D). In Spearman’s Rho test, correlation coefficient value for total annual rainfall was found to be negative (-0.42) at a p-value < 0.05. However, for annual maximum temperature, mean temperature and minimum temperature correlation coefficient values were found to be 0.16, 0.19 and 0.26, respectively, with p-values < 0.1. In order to compare the trend analysis results from the nonparametric methods with parametric method, linear regression tests were also performed over the climatic parameters. Sign of slopes of the linear regression tests for all the climatic parameters were found to be comparable with the Mann-Kendal method. However, the magnitude of slopes was found to vary marginally within an error range of ±10%. Table 2. Trend analysis: A, Total annual rainfall; B, Minimum temperature; C, Mean temperature; D, Maximum temperature.

A. Total annual rainfall MannTotal S Kendall score = -135 Spearman's Rho = Rho -0.416 Linear Sigma = regression 4.439 B. Minimum temperature MannTotal S Kendall score = 68 Spearman's Rho = Rho 0.258 Linear Sigma = regression 0.005 C. Mean temperature MannTotal S Kendall score = 52 Spearman's Rho = Rho 0.187 Linear Sigma = regression 0.005 D. Maximum temperature MannKendall Spearman's Rho Linear regression

Total S score = 42 Rho = 0.159 Sigma = 0.006

Test statistic (Statistical table) (Resampling) Result Z statistics a=0.1 a=0.05 a=0.01 a=0.1 a=0.05 a=0.01 -2.278 1.645 1.96 2.576 1.615 1.819 2.583 Statistically significant trend (at a < 0.05). Decreasing trend. -2.279 1.645 1.96 2.576 1.645 1.997 2.474 Statistically significant trend (at a < 0.05). Decreasing trend. -2.345 1.699 2.045 2.756 1.687 1.968 2.708 Statistically significant trend (at a < 0.05). Decreasing trend. Test statistic (Statistical table) (Resampling) Result Z statistic a=0.1 a=0.05 a=0.01 a=0.1 a=0.05 a=0.01 1.139 1.645 1.96 2.576 1.7 2.057 2.549 No statistically significant trend (at a = 0.10). Decreasing trend. 1.411 1.645 1.96 2.576 1.743 2.061 2.754 No statistically significant trend (at a = 0.10). Decreasing trend. 1.558 1.699 2.045 2.756 1.682 2 2.974 No statistically significant trend (at a = 0.10). Decreasing trend. Test statistic (Statistical table) (Resampling) Result Z statistic a=0.1 a=0.05 a=0.01 a=0.1 a=0.05 a=0.01 0.867 1.645 1.96 2.576 1.666 1.989 2.515 No statistically significant trend (at a = 0.10). 1.025 1.645 1.96 2.576 1.756 2.085 2.516 No statistically significant trend (at a = 0.10). 1.247 1.699 2.045 2.756 1.754 2.091 2.99 No statistically significant trend (at a = 0.10). Test statistic (Statistical table) (Resampling) Result Z statistic a=0.1 a=0.05 a=0.01 a=0.1 a=0.05 a=0.01 0.697 1.645 1.96 2.576 1.649 1.904 2.481 No statistically significant trend (at a = 0.10). 0.872 1.645 1.96 2.576 1.778 2.116 2.783 No statistically significant trend (at a = 0.10). 0.966 1.699 2.045 2.756 1.756 2.096 2.779 No statistically significant trend (at a = 0.10).

CONCLUSION The primary aim of this present study was to evaluate the changing land use and land cover and climatic parameter of a watershed area near a biosphere reserve of the central India. It was hypothesized that the land use and land cover change of an area is interlinked with the local climate. Therefore, trend analysis of LULCC and major climatic parameters were carried out. It was observed that the high dense vegetation and agricultural land are decreasing for the study area since 1990, while low dense vegetation, settlement and fallow land are www.tropicalplantresearch.com


Soni (2017) 4(1): 115–125 increasing. Simultaneously, trend analysis of the climatic variables for the period of 1980-2011 over the study area are revealed that the annual rainfall trend is decreasing whereas, trend of annual maximum temperature, mean temperature and minimum temperature is increasing. It should be noted that the present study does not include establishment of a direct relationship between LULCC and climatic control, albeit, an indication of decreasing high dense vegetation with decreasing rainfall is noted. Therefore, this present study lays the foundation of a future land use land cover – climate model scenario for testing sensitivity of both climate and LULC to each other. ACKNOWLEDGEMENTS Author is thankful to the Vice Chancellor of MGCGV University M.P. India to provide Remote Sensing and GIS laboratory. REFERENCES Aggarwal PK, Joshib PK, Ingramc JSI & Guptad RK (2004) Adapting food systems of the Indo-Gangetic plains to global environmental change: key information needs to improve policy formulation. Environmental Science & Policy 7: 487–498. Booij MJ (2005) Impact of climate change on river flooding assessed with different spatial model resolutions. Journal of Hydrology 303: 176–198. Chase TN, Pielke Sr. RA, Kittel TGF, Nemani RR & Running SW (1999) Simulated impacts of historical land cover changes on global climate in northern winter. Climate Dynamics 16: 93–105. Congalton RG & Green K (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd ed. CRC Press/Taylor & Francis: Boca Rato. FL. USA. Delang CO (2002) Deforestation in northern Thailand: The result of Hmong farming practices of Thai development strategies. Society and Natural Resources 15: 483–501. Dessens J & Bucher A (1995) Changes in minimum and maximum temperatures at the Pic du Midi relation with humidity and cloudiness, 1882–1984. Atmospheric Research 37: 147–162. Douglas EM, Niyogi D, Frolking S, Yeluripati JB, Pielke Sr. RA, Niyogi N, Vörösmarty CJ & Mohanty UC (2006) Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian monsoon belt. Geophysical Research Letters 33(14): L14403. Duncan J, Stow D, Franklin J & Hope A (1993) Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico. International Journal Remote Sensing 14(18): 3395– 3416. Duram LA, Bathgate J & Ray C (2004) A local example of land-use change: Southern Illois-1807, 1938, and 1993. The Professional Geographer 56(1): 127–140. Fromard FC & Vega PC (2004) Half a century of dynamic coastal affecting mangrove shoreline of French Guiana: A case study based on remote sensing data analyses and field surveys. Marine Geology 208: 265–280. Fulekar MH & Kale RK (2010) Impact of Climate Change: Indian Scenario. University News 48(24): 15–23. Hale RC & Gallo KP (2008) Influences of specific land use/land cover conversions on climatological normals of near-surface temperature. Journal of Geophysical Research 113: D14113. Hipel KW & McLeod AI (1994) Time Series Modelling of Water Resources and Environmental Systems. Elsevier. Ioannis M & Meliadis M (2011) Multi-temporal Landsat image classification and change analysis of landcover/use in the Prefecture of Thessaloiniki, Greece. Proceedings of the International Academy of Ecology and Environmental Sciences 1(1): 15–26. IPCC (2001) Climate Change 2001: Synthesis Report: Contribution of working groups I, II and III to the Third Assessment Report. Cambridge Press. IPCC (2007a) Climate Change 2007a: Synthesis Report. In: Pachauri RK & Reisinger A (eds) Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge. IPCC (2007b) Climate Change 2007b: The Physical Science Basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M & Miller HL (eds) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge. www.tropicalplantresearch.com


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ISSN (E): 2349 – 1183 ISSN (P): 2349 – 9265 4(1): 126–133, 2017 DOI: 10.22271/tpr.2017.v4.i1.019 Research article

Effects of genotype and agro-ecological conditions on storability of soybean [Glycine max (L.) Merr.] seed Grace J. Chirchir1*, Maina Mwangi1, Desterio O. Nyamongo2 and Joseph P. Gweyi-Onyango1 1

Kenyatta University, School of Agriculture and Enterprise Development, Department of Agricultural Science and Technology, Nairobi, Kenya 2 Genetic Resource Research Institute, Kenya Agriculture and Livestock Research Organization, Kenya *Corresponding Author: [email protected]

[Accepted: 11 March 2017]

Abstract: Soybean seed storability in the tropics is especially important due to rapid deterioration that varies with the environment, genotype and management practices. The objective of this study was to investigate soybean seed quality during storage as influenced by genotype and agroecological conditions in Meru South Sub-County, Kenya. Seeds of two commonly grown soybean genotypes- Gazelle and TGx 1740-2F (SB19) harvested in February 2013 were used to investigate seed storability. Seed samples (2.5 kg) were stored in gunny bags in a completely randomized design with three replications, giving a total of 6 experimental units per site. The storage treatments were set on farm under ambient conditions in two contrasting agro-ecologies: Upper Midlands II (altitude- 1529 m above sea level; temp.- 18.2–20.6 ºC) at Kirege and Lower Midlands IV (altitude- 1129 m above sea level; temp.- 21.0–23.5 ºC) at Igambatuntu. Changes in seed quality were monitored at 0, 123 and 246 days of storage by subjecting them to germination, electrical conductivity and accelerated aging tests. Data were analyzed using SAS 2009. Results revealed that smaller-seeded soybean genotype TGX 1740-2F was a better storer seed than larger seeded Gazelle as shown by a higher vigor at end 246 days (8 months) of storage. In addition, seed storage environmental conditions significantly influenced the degree of seed deterioration. Soybean seed stored at cooler Upper Midlands II had greater vigor than that stored at the warmer Lower Midlands IV agro-ecology. This study concludes that soybean seed deteriorates in storage and that genotype by environment interaction plays an important role in expression of seed storability. It is recommended that soybean be stored in the cooler higher agro-ecologies of the tropics for improved storability. Keywords: Soybean - Seed storability - Genotype - Agro-ecology. [Cite as: Chirchir GJ, Mwangi M, Nyamongo DO & Gweyi-Onyango JP (2017) Effects of genotype and agroecological conditions on storability of soybean [Glycine max (L.) Merr.] seed. Tropical Plant Research 4(1): 126–133] INTRODUCTION Availability of high quality seed that ensure adequate plant stand is necessary for the production and expansion of soybean. Between harvest and the next planting season farm- saved seed is usually stored under ambient tropical storage conditions. The preservation of seed viability and quality in storage is an important trait both for food usage and for seed use (Bentsink et al. 2000). Generally, viability and quality of seeds gradually deteriorate after harvest (Coolbear 1995, McDonald 1999), but the deterioration in long-term storage depends on environment, biochemical, biological, and genetic factors. Loss of seed viability and vigor under high temperature and RH conditions is a common phenomenon in many crop seeds (Balešević-Tubić et al. 2010) but it is well marked in soybean (Burris 1980, Tatipata 2009). Field seed storage conditions of high humidity and temperature synergistically accelerate physiological deterioration and pathological damage of seed. In addition, significant genotypic differences in soybean seed storability have been found by several researchers (Kurdikeri et al. 1996, Shelar 2002, El-Abady et al. 2012, Wien & Kueneman 1981). Such differences have been attributed www.tropicalplantresearch.com


Received: 22 November 2016

Published online: 31 March 2017 https://doi.org/10.22271/tpr.2017.v4.i1.019

Chirchir et al. (2017) 4(1): 126–133 to biochemical characteristics of soybean genotypes which affect the degree of seed damage and the ability of seed to resist the negative consequences of aging (Balešević-Tubić et al. 2011). Genetic factors such as hardseediness, seed size, seed coat color, resistance to diseases and seed chemical composition influence the expression of seed vigor (AOSA 2009). Soybean seed quality deterioration has been associated with large seed size and permeable seed coats (Horlings et al. 1991). Soybean seed size was found to have a direct effect on seed germination and vigor (Sung 1992). In addition, a strong negative correlation was found between germination and seed weight among soybean genotypes (Singh et al. 1978). However, the physiological and biochemical mechanisms by which this variability is expressed are still not fully understood, although it has been found to be significantly influenced by genotype, environment, management practices and their interactions (Bellaloui et al. 2011). Hence, in the present investigation, efforts have been made to study the storability of soybean as influenced by genotype and contrasting storage agro-ecological conditions of Meru South sub-county of Kenya. MATERIALS AND METHODS Site description: The storage experiment was conducted in Meru South Sub-county of Kenya, in two soybean growing areas, representative of the environmental conditions soybean seed would be subjected to in a typical farmer’s storage facility. Site 1 was at Kirege village located at 00º 20.580’’S, 037º 37.189’ E, a higher altitude (1529 m above sea level ) site within cooler humid agro-ecological zone - Upper Midlands II (UM2) with an annual mean temperature of 18.2 to 20.6 ºC (Jaetzold et al. 2006). Site 2 was at Igambatuntu Village located at 00º 06’19.4”N, 037º 54’ 49.7’’E; altitude 1129 m above sea level, which represented the warmer semi-humid agroecological zone - Lower Midlands IV (LM4) with annual mean temperature of 21.0 to 23.5 ºC (Jaetzold et al. 2006 ). Experimental layout: Farm saved seed of two commonly grown soybean genotypes Gazelle and TGx 1740-2F (SB19), harvested in February 2013 were used in the experiment. The seed was obtained from two farmers from areas representative of Upper Midlands III agro-ecology in Meru South and Maara Sub-counties in February 2013. Seed samples (2.5 kg) from each seed lot was stored in three replicates in synthetic gunny bags (Biryani Pakistani Rice bags, M/S.H.M Traders) and tied with a sisal twine giving a total of 6 experimental units per site. The storage treatments were set in continuous non-climate controlled farmers’ in-house stores, in a Completely Randomized Design (CRD) with three replications and stored for eight months from 14th March 2013 to 14th November 2013. The seeds were sampled after 0, 123 and 246 days (0, 4 and 8 months respectively) of storage for quality tests at the Genetic Resource Research Institute Laboratories (S 01º 12.955’; E 036º 37.859’, altitude 2100 m above sea level, AEZ LH3). The seed moisture, standard germination, electrical conductivity and the accelerated aging tests (AOSA 2009) were performed to monitor any changes in seed quality during storage. 1000 seed weight: Weight of 1000 seeds was evaluated by randomly sampling seeds from each seed lot, counting and weighing them. The values were then corrected at 13% moisture content. Determination of seed moisture: Seed moisture contents were determined using a Grain moisture meter (GMK-303RS, G-Won Hitech Co. Ltd) which measures the electric properties of seed moisture either by conductivity or capacitance within the range of 6–25% range. Four replicates per seed lot were sampled, placed inside the moisture meter, ground and readings taken. Germination tests: Seeds of soybean [Glycine max (L.) Merr.] were treated for 40 seconds with sodium hypochlorite solution (3.85% active ingredient) diluted with water in 1:2 ratio for 40 seconds and then surface washed with distilled water three times, to retard saprophytic fungal growth. Seeds were then germinated by placing 50 seeds per replication in four replicates in germination boxes using 500 ml plastic containers with lids, containing 1% water agar under laboratory conditions (ISTA 2007). The germination boxes were arranged in a completely randomized design (CRD) in a walk-in germination chamber with alternating 12 h fluorescent light at 30ºC and www.tropicalplantresearch.com


Chirchir et al. (2017) 4(1): 126–133 12 hours darkness at 20ºC. Counts of germinating seeds were made daily, starting on the first day of imbibitions and terminated 11 days after sowing, when maximum germination was obtained. Seeds were identified as germinated when 2mm of the radicals protruded (ISTA 2007). Normal seedlings were recorded for calculating germination percentage (GP) at last count. Germination percent (GP) was calculated as follows:

Electrical Conductivity test: Electrical conductivity tests were determined by weighing four replicate samples of 50 seeds per treatment and placing them in 250 ml plastic cups containing 200 ml of distilled water. The seeds were gently stirred to remove air bubbles. Any floating seeds were removed and the cups covered with aluminum foil. The seeds were then left to soak in the water for 24 hours at room temperature 20±2 ºC. Conductivity of seed leachates (electrical conductivity) was measured using a Jenway 4020 conductivity meter and CRT-CAA- 515B electrode dip type cell (Fisons Scientific Equipment) The conductivity meter was standardized with 0.01N potassium Chloride. The solution was prepared afresh by dissolving 0.7456 g KCl in 1,000 ml of distilled water at room temperature. The cell constant (K) value at 1.000, and temperature coefficient per ºC of 2% rise at 25ºC was used to take the readings. The electrical conductivity of a control sample of an equivalent quantity of distilled water was also determined. Conductivity was expressed on a weight basis in micro Siemens per cm per gram (µs.cm-1.g-1) of seed (ISTA 2007). Accelerated ageing test: For each seed lot, seeds were first preconditioned by exposing them to humid environment, created by placing seeds in open trays, above a water pan at room temperature for 48 hours in order to raise the seed moisture content to between 10% and 14%. After allowing the seed moisture to equilibrate, 100 seed weight, adjusted to 13% moisture content was taken. Accelerated aging tests were then conducted by placing four replicates of 100 seeds per seed lot on a screen inside a 38×28×10 cm accelerated aging boxes (Hoffman Manufacturing Company, Albany, OR) containing 500 ml of distilled water. The boxes were tightly sealed and placed in an ageing chamber maintained at 41°C and 100 per cent relative humidity for 72 hours (Delouche 1965). At the end of ageing, germination tests were conducted in four replicates of 50 seeds per seed lot on 1% water agar in a germination chamber with alternating 12 h darkness (20ºC) and 12h of fluorescent light (30ºC). The ageing response was measured based on accelerated ageing germination percent of normal seedlings (ISTA 2007). Statistical analysis: Data were analyzed using PROC GLM (GLM Procedure) model of the Statistical Analysis Systems software. Parameters were subjected to Analysis of Variance (ANOVA) and means separated using Least Significance Difference (LSD) at p < 0.05 based on Tukey’s Studentized Range (HSD) test. RESULTS The means of seed viability and vigor for tested genotypes of soybean - TGx1740- 2F (SB 19) and Gazelle before and after storage in both storage agro-ecological conditions of Upper Midlands II (UM2) and Lower Midlands IV (LM4) was investigated. Results revealed that seed quality traits during storage varied amongst tested genotypes and storage environments, with statistically highly significant differences (p < 0.05). Effects of genotype on storability of soybean seed The genotypic differences in seed viability and vigor of soybean genotypes SB 19 (TGx1740-2F) and Gazelle in storage under ambient conditions was significant (p < 0.05). There were significant differences in 1000-seed weight between the soybean genotypes (Table 1). Table 1. Seed weight of soybean genotype Gazelle and TGx 1740-2F.

Genotype Gazelle TGx1740 2F (SB19) LSD0.05

1000 Seed weight 159.20a 119.60b 7.52

Seed coat color Cream Cream -

Note: Values followed by the same letter(s) in each column are not significantly different (p
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