Manuscript accepted on : 30-05-2024
Published online on: 26-06-2024
Plagiarism Check: Yes
Reviewed by: Dr. Arshad Yaseen
Second Review by: Dr. Rishee K Kalaria
Final Approval by: Dr. Eugene A. Silow
Tanvi Taneja1, Indu Sharma2, Bikram Jit Singh3 , Amarjeet Singh4 , Mukesh Kumar1 , Raj Singh1*
1Department of Bio-Sciences and Technology, Maharishi Markandeshwar (Deemed to Be University), Mullana, Ambala, Haryana, India
2Department of Biotechnology, NIMS Institute of Allied Medical Science and Technology, NIMS University Rajasthan, Jaipur, Rajasthan, India
3Department of Mechanical Engineering, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala (Haryana), India
4Department of Agriculture Engineering, SCRIET, CCS University, Meerut, India
Corresponding Author E-mail: dr.rajsingh09@gmail.com
DOI : http://dx.doi.org/10.13005/bbra/3253
ABSTRACT: Composting is the natural process of transforming organic wastes, such as leaves and food scraps, into fertile manure that can enrich the soil with humus, helpful bacteria, and critical plant nutrients, thus enhancing soil fertility and structure. India's soil today is low in organic matter and nutrients, particularly micronutrients. Compost includes 2% nitrogen, 0.5–1.0% phosphorus, around 2% potassium, and trace amounts of all critical micronutrients. Biodegradable weeds, including Parthenium hysterophorus, Solanum nigrum, Calotropis procera, and Trianthema portulacastrum, were degraded using the Bangalore pit compost method. The fresh vegetation of Parthenium, Solanum, Calotropis and Trianthema for making compost were collected from nearby localities of Mullana village from November, 2022 to April 2023. As a result, applying compost to soil increases organic matter and enhances soil fertility, restoring minerals and organic matter lost during harvesting. It also enhances the chemical, physical, and biological qualities of the soil, increasing crop output. Compost increases the soil's water holding capacity, which reduces crop water requirements and irrigation frequency. The nitrogen, phosphorus, potassium, and carbon-to-nitrogen ratios of the compost were measured every 20 days for up to 100 days. The results demonstrate that compost has a high calcium, nitrogen, phosphorus and potassium content but a lower carbon and nitrogen ratio.
KEYWORDS: Biodegradable Weeds; Compost; Humus; Micronutrient; NPK,pit
Download this article as:Copy the following to cite this article: Taneja T, Sharma I, Singh B. J, Singh A, Kumar M, Singh R. Composting as a Sustainable Option for Converting Undesirable Weeds Like Parthenium Hysterophorus, Solanum Nigrum, Calotropis Procera and Trianthema Portulacastrum into Organic Manure. Biotech Res Asia 2024;21(2). |
Copy the following to cite this URL: Taneja T, Sharma I, Singh B. J, Singh A, Kumar M, Singh R. Composting as a Sustainable Option for Converting Undesirable Weeds Like Parthenium Hysterophorus, Solanum Nigrum, Calotropis Procera and Trianthema Portulacastrum into Organic Manure. Biotech Res Asia 2024;21(2). Available from: https://bit.ly/3xpwQUW |
Introduction
is a deadly herbaceous weed that originated on the American continent and has since spread to more than 45 tropical countries throughout the world. Some adaptive processes, such as the lack of natural enemies, extensive adaptive success, drought tolerance, , rapid seed production rate, simple seed dispersals, and allelopathic qualities7,8. Parthenium contains several allelochemicals, including sesquiterpene lactones and phenolic acid (gallic acid, chlorogenic acid, ferulic acid, etc.), which can be toxic to grazing animals and have a negative impact on crop production, plant diversity, and the distribution of native flora and fauna6.
The annual plant Solanum nigrum has oval or lance-shaped, juicy, dull dark green leaves that are toothless to moderately serrated on the margins. It can grow up to a height of 90 cm with numerous lateral branches. With a short pedicellate and five widely spaced petals, the flowers are tiny and white9. When ripe, its fruits are tiny and black. Solanum nigrum is mostly found near waste land, abandoned fields, ditches and roadside fence rows, as well as the boundaries of wooded areas and cultivated land. It is a typical plant that may be found over most of Europe and the continent of Africa. 10,11. Although it is regarded as a rich source of one of the most well-known plant poisons but it has been shown to be a reservoir of phytochemicals with potential for use in pharmaceuticals.
The genus Calotropis is found in tropical and subtropical parts of Asia and Africa. Calotropis procera, a member of the family, is an erect soft-wooded evergreen perennial shrub. Akra and milk weed are popular names for the plant Calotropis procera. This plant is well known as it yields lot of latex. All components of this plant, including root, stem, leaf and flowers, are frequently used in traditional medicine12. Calotropis is a plant genus that produces milky sap and is therefore, frequently referred to as milkweed. latex is supposed to have mercury-like effects on the human body, is frequently referred to as vegetable mercury, and is used in place of mercury in aphrodisiacs. 13. Cattle avoid and plants because of their disagreeable taste and the presence of cardiac glycosides in their sap. root bark has a digitalis-like action on the heart; however, it was formerly utilized as an alternative to ipecacuanha14. Calotropin, a toxic chemical discovered.
(horse purslane) an Aizoaceae family member, is a common weed that affects a variety of crops in India, Pakistan, and Sri Lanka, including direct-seeded rice, cotton, sugarcane, pearl millet, sorghum, maize, summer rainy season pulses, oilseed crops, fodder crops, vegetables, and horticultural crops15. is a dangerous weed in Australia, Ghana, India, the Philippines, and Thailand, and a big weed in Cambodia, Guyana, and Nicaragua. This weed is also responsible for significant yield loss in maize, soybean and peanut14. Horse purslane extracts have been shown to have adverse allelopathic effects on germination of soybean seed, seedling vigor and its productivity. A few available strategies to control this weed include physical or mechanical harvesting, eradication, prevention quarantining of agricultural items and biological control16,8. However, no effective technique for controlling this weed has yet been identified. Several studies have been conducted to investigate the possible use of weed biomass as a resource for the production of various industrial products, including anaerobic digestion, ethanol-fermented compost, and vermicompost17-20 Composting has been suggested as a useful approach for reducing weed biomass while also maintaining soil fertility using organic additions16. Composting is an environmentally benign method that turns organic material into a product, which is high in humic acid and plant-available nutrients with very low population of pathogenic bacteria. According to a few earlier research, composting and vermicomposting may be a sustainable method for recovering nutrients and other components from weed biomass 21-23 compost from Parthenium hysterophorus, Solanum nigrum, Calotropis procera and Trianthema portulacastrumthe. It offers weed management, improve material recycling, soil structure, nutrients contents and to develop inexpensive technology.
Material and Methods
This experiment was done at the Department of Agriculture’s Research Farm in Maharishi Markandeshwar. Deemed to be University, Mullana, district of Ambala, Haryana, . The fresh vegetation of Parthenium, Solanum, Calotropis and Trianthema for making compost were collected from nearby localities of from November, 2022 to April 2023 and chopped in the pit (30.2753°N, 77.04760 E). The raw material of these plants was converted into compost was using pit method. The process of composting was followed as described Vyankatrao 24. After six months, depending on various factors, the compost matured into a dark, crumbly substance with an earthy aroma, ready to be used as a nutrient-rich soil amendment. The uniformly mixed samples (100 g) from each treatment were collected, oven-dried every 20 days for up to 100 days, and used for nutrient analysis.
Chemical analysis
The samples were collected separately from the decomposing Parthenium, Solanum, Calotropis and Trianthema and chemically at every 20 days up to 100 days. The chemical analysis of compost provides valuable insights into its nutrient content and overall quality as a soil amendment. Calcium, Nitrogen, Potassium and Phosphorus test using by soil test kit.
Calcium test
The calcium content of the sample was determined by titrating the acid-soluble ash solution with a 0.01N KMnO solution and using methyl red as an indicator.
Nitrogen test
The nitrogen concentration of the sample was determined using the micro-kjeldahl method after it had been digested with concentrated sulphuric acid.
Potassium test
A flame photometer was used to evaluate potassium concentration, as recommended by Jackson25.
Phosphorus test
The available phosphorus in soil was determined by the Olsen’s method.
by walkley and black’s rapid titration method.
OC = Organic carbon, B = Blank reading and S= sample
Figure 1: Compost preparation: (1) Fresh weeds dumped in pit and (2) Compost prepared after decomposition of weeds Click here to view Figure |
Results
The nutritional value of weed compost samples obtained by using pit method is shown in table 1.
Table 1: Percentage of nutritional contents in per gram compost
Duration(days) | Nitrogen (%) | Phosphorus(%) | Potassium(%) | Calcium (%) | C:N(%) |
20 | 0.45 | 0.06 | 0.06 | 1.98 | 35.67 |
40 | 0.50 | 0.08 | 0.08 | 2.11 | 34.55 |
60 | 0.57 | 0.09 | 0.10 | 2.28 | 28.66 |
80 | 0.70 | 0.10 | 0.13 | 2.41 | 27.57 |
100 | 0.79 | 0.18 | 0.16 | 2.47 | 26.45 |
Further depicts the changes in nitrogen percentage of all manure types from 20 to 100 days. In the end of composting after 100 days, the maximum percentage of calcium was reported in the compost followed by nitrogen, phosphorus and potassium with lowering carbon and nitrogen ratio. High nitrogen levels are most likely caused by ammonia excretion and organic waste conversion to nitrogen during the composting process.
Figure 2: Main Effects Plot for N Changes in nitrogen ratio of weed manure prepared by pit methods from 20 to 100 daysClick here to view Figure |
According to the graph in Figure 2, 3, 4, 5 and 6, the changes occurred in content of phosphorus, potassium and calcium including the carbon to nitrogen ratio. Increased nutritional content was evident from the data in Table 1. The current experiment’s results confirm the findings26, who also reported the mobilization and mineralization of phosphorus caused by bacterial and fecal phosphatase activity during the composting process, which caused the overall rise in phosphate level from starting to end of the experiment. According to Jaikumar27, the prepared manure had the second-highest potassium content but released potassium into the soil very slowly, preventing wastage from washing it away.
Fig. 2 representing the carbon to nitrogen ratio changes from 20 to 100 days of composting. Similarly Ansari and Rajpersaud28 demonstrated a decrease in the carbon to nitrogen ratio as the litter broke down and decomposed. The microorganisms consume the carbon for respiration while the nitrogen is and converted to nucleic acids, ammonia, urea and nitrates29. Overall, the carbon to nitrogen ratio decreased as a result of degradation process. The content of nitrogen, phosphorus, potassium and calcium was found maximum in the weed-compost prepared by using pit method at the end of 100 days. Earlier investigations with weed-composting also revealed high content of nitrogen, phosphorus and potassium24. The nutritional levels in compost prepared from weeds were observed consistent from 20 to 100 days30. All forms and types of compost manure exhibited an increase in the content of nitrogen, phosphorus, potassium and calcium and a decrease in carbon to nitrogen ratio. Microorganisms were observed more active at breathing with the advancement of decomposition process.
Figure 2 showed that main effect of nitrogen according to days, pH, EC and MC. The relationship between N and days is statically significant (<0.05). The regression model may explain a significant portion of the variation in N (Refer Annexure A for details). Positive correlation (r=0.99) that when days increases N also tends to increases. The relationship between N and pH is statistically significant (<0.005) and 99.81% the regression model can explain the majority of the variation in N. The relationship between N and EC is statically significant (<0.05) and 96.88% of the variation in N be explained in graph. The positive correlation (r=0.98) that when EC increases then N also tends to increase. The relationship between N and MC is statically significant (<0.05) and of the variation explained by model. Negative correlation (r= -1.00) that MC increases tend N decreases.
Figure 3 that main effect of posphorus according to days, pH, EC and MC. The relationship between P and days is statically significant (<0.05). The 79.72% regression model may explain a significant portion of the variation in P (. Positive correlation (r=0.89) indicates that when days increases, P also tends to increase The relationship between P and pH is not statistically significant (>0.05) and 75.54% the regression model can explain the majority of the variation in P. The correlation between P and pH is not significant (p>0.05). The relationship between P and EC is not statically significant (p>0.05) and 74.57% variation can be explained by regression model. The correlation between P and EC is not significant (>0.05). The relationship between P and MC is statically significant (<0.05). The 85.11% of the variation in P explained by model and negative correlation indicate (r= -0.02) that MC increases and P decreases.
Figure 3: Main Effects Plot for P ratio of weed manure prepared by pit methods from 20 to 100 days. |
Figure 4 that main effect of potassium according to days, pH, EC and . The relationship between K and days is statically significant (<0.05). The 99.91% regression model may explain a significant portion of the variation in K (Refer Annexure C for details). The relationship between K and pH is not statistically significant (<0.05) and 98.23% the regression model can explain the majority of the variation in K. The positive correlation found between K and pH is significant (r= 0.99) an The 97.55% of variation K explained by model is statically significant (<0.05). Positive correlation (r= 0.99) indicate that EC increases then K also increases. The relationship between K and MC is statically significant (<0.05) and 98.78% K variation can be explained by regression model. The negative correlation indicates (r= -0.99) that, K content decreases when MC is increase.
Figure 4: Main effect of Potassium content of weed manure prepared by pit methods from 20 to 100 days |
Figure 5 that main effect of calcium according to days, pH, EC and MC The relationship between Ca and days is statically significant (<0.05). The 97.87% regression model may explain a significant portion of the variation in Ca. The positive correlation (r=0.99) indicate that days increases and Ca also increases. The relationship between Ca and pH is statistically significant (<0.05) and 97.36% % the regression model can explain the majority of the variation in Ca (Refer Annexure D for details).. The positive correlation (r= 0.99) pH increases tend to increase the Ca content. The 99.90% of variation Ca explained by model. The relation Ca and MC is statically significant (<0.05). The 89.76% variation of Ca explained by regression model.
Figure 5: Main Effects Plot for Ca content of weed manure prepared by pit methods from 20 to 100 days |
Figure 6 that main effect of calcium and nitrogen according to days, pH, EC and . The relationship between and days is statically significant (<0.05).The regression model may explain a significant portion of the variation in . The negative correlation (r=-0.95) indicate that days increases and CN also decreases. The relationship between CN and pH is statistically significant (p<0.05) and 87.13% the regression model can explain the majority of the variation in C: N (Refer Annexure E for details). The negative correlation (r= 0.93) of increases tend to decrease C: N ratio. The relationship between CN and EC is statically significant (<0.05). The 94.27% of variation explained by model. The negative (r= -0.97) indicates EC increases and CN decreases. The relation CN and MC is statically significant at (<0.05). The 77.74% variation of CN explained by regression model. The p correlation (r= 0.88) that MC is increases and CN increases.
Figure 6: Main Effects Plot for C:N content of weed manure prepared by pit methods from 20 to 100 days. |
Discussion
The best method for managing weeds is composting, which also improves the quality of the final product18,19. Through this method, weed biomass may be safely disposed of and converted into extremely rich manure, which advances sustainable development31. According to the findings of Sivakumar32, the compost prepared from Parthenium, Solanum, Calotropis and Trianthema when used sparingly with cow or goat manure aids in the eradication and better usage of these plants. Composting helps in controlling weeds while feeding the intended crops with nutrients33,34. Sustainable farmers grow food crops without harming the environment by employing farming methods like composting. Sustainable farms genuinely improve and protect the , so that future generations can continue to use it for food production. Employing troublesome weeds to prepare compost, the environmental risks caused using chemical herbicides and fertilizers ed. This strategy of increasing soil productivity is environmentally benign and promotes sustainable agriculture. By turning toxic weeds including Parthenium Solanum, Calotropis and Trianthema into compost can be a source of nutrients for the agricultural crops. Composting in a pit, which is the most effective approach, increased the content of nitrogen, phosphorus, potassium and calcium but decreased the carbon to nitrogen ratio from 20 to 100 days. Nutrient-rich compost can be prepared from weeds. Composting technology is simple to use, safe for the environment and profitable. Utilizing weeds to prepare compost will open up new possibilities for managing weeds and soil nutrients.
Conclusion
Composting is the best method of recycling organic waste, enabling the production of useful ingredients viz. humus contents and minerals from organic waste. The plants can safely be used to prepare the useful and nutrients rich compost. It also offers adaptable total waste management, improve material recycling and is relatively inexpensive to implement. The utilization of composting presents a compelling and sustainable solution for managing unwanted weeds like Parthenium hysterophorus, Solanum nigrum, Calotropis procera and Trianthema portulacastrum, effectively converting them into valuable organic manure. Overall, the integration of composting as a waste management strategy not only mitigates environmental challenges posed by weed proliferation but also contributes to the creation of a circular economy where waste is transformed into a valuable resource. As we strive for more sustainable agricultural practices, composting stands out as a practical and environmentally friendly solution that holds promise for addressing both waste management and soil fertility concerns in the long term.
Conflict of Interest
Authors have no conflict of interest.
Funding Sources
There was no source of funding.
Ethical Approval
There are no ethical issues in this manuscript.
Availability of data and materials
Data as well as material available and will provide on request.
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Annexure-A
Regression Analysis- N (%age per gm) Versus Days, pH, EC and MC
Regression Equation
N | = | 0.3380 + 0.004400 Days | |
N | = | -0.2177 + 0.1234 pH | |
N | = | -0.534 + 0.3567 EC | |
N | = | 2.2563 – 3.475 MC |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 0.3380 | 0.0273 | 12.36 | 0.001 | |
Days | 0.004400 | 0.000412 | 10.67 | 0.002 | 1.00 |
pH | 0.1234 | 0.0111 | 11.13 | 0.002 | 1.00 |
EC | 0.3567 | 0.0369 | 9.66 | 0.002 | 1.00 |
MC | -3.475 | 0.193 | -18.04 | 0.000 | 1.00 |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
Days 0260768 | 97.43% | 96.58% | 91.57% |
pH 0.0250349 | 97.63% | 96.85% | 90.01% |
EC 0.0287394 | 96.88% | 95.84% | 91.55% |
MC 0.0155571 | 99.09% | 98.78% | 97.85% |
Annexure-B
Regression Analysis- P (%age per gm) Versus Days, pH, EC and MC
Regression Equation
P | = | 0.0240 + 0.001300 Days |
P | = | -0.1335 + 0.0355 pH |
P | = | -0.223 + 0.1022 EC |
P | = | 0.603 – 1.052 MC |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 0.0240 | 0.0251 | 0.96 | 0.410 | |
Days | 0.001300 | 0.000379 | 3.43 | 0.041 | 1.00 |
pH | 0.0355 | 0.0117 | 3.04 | 0.056 | 1.00 |
EC | 0.1022 | 0.0345 | 2.97 | 0.059 | 1.00 |
MC | -1.052 | 0.254 | -4.14 | 0.026 | 1.00 |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
Days 0.0239444 | 79.72% | 72.96% | 20.90% |
pH 0.0262969 | 75.54% | 67.38% | 8.95% |
EC 0.0268092 | 74.57% | 66.10% | 12.03% |
MC 0.0205124 | 85.11% | 80.15% | 35.61% |
Annexure-C
Regression Analysis- K (%age per gm) Versus Days, pH, EC and MC
Regression Equation
K | = | 0.03100 + 0.001250 Days |
K | = | -0.1259 + 0.03492 pH |
K | = | -0.2154 + 0.10094 EC |
K | = | 0.5718 – 0.9785 MC |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 0.03100 | 0.00507 | 6.12 | 0.009 | |
Days | 0.001250 | 0.000076 | 16.37 | 0.000 | 1.00 |
pH | 0.03492 | 0.00270 | 12.91 | 0.001 | 1.00 |
EC | 0.10094 | 0.00923 | 10.94 | 0.002 | 1.00 |
MC | -0.9785 | 0.0627 | -15.60 | 0.001 | 1.00 |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
Days 0.0048305 | 98.89% | 98.52% | 95.88% |
pH 0.0061010 | 98.23% | 97.64% | 92.82% |
EC 0.0071802 | 97.55% | 96.74% | 92.91% |
MC 0.0050660 | 98.78% | 98.38% | 97.51% |
Annexure-D
Regression Analysis- Ca (%age per gm) Versus Days, pH, EC and MC
Regression Equation
Ca | = | 1.8660 + 0.006400 Days |
Ca | = | 1.062 + 0.1789 pH |
Ca | = | 0.5841 + 0.5232 EC |
Ca | = | 4.535 – 4.801 MC |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 1.8660 | 0.0361 | 51.65 | 0.000 | |
Days | 0.006400 | 0.000545 | 11.75 | 0.001 | 1.00 |
pH | 0.1789 | 0.0170 | 10.52 | 0.002 | 1.00 |
EC | 0.5232 | 0.0311 | 16.83 | 0.000 | 1.00 |
MC | -4.801 | 0.936 | -5.13 | 0.014 | 1.00 |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
Days 0.0344480 | 97.87% | 97.16% | 92.17% |
pH 0.0383611 | 97.36% | 96.48% | 94.05% |
EC 0.0241816 | 98.95% | 98.60% | 95.85% |
MC 0.0755884 | 89.76% | 86.35% | 66.08% |
Annexure-E
Regression Analysis- C: N (ratio) Versus Days, pH, EC and MC
Regression Equation
C:N | = | 38.21 – 0.1271 Days |
C:N | = | 53.80 – 3.497 pH |
C:N | = | 64.18 – 10.55 EC |
C:N | = | -13.4 + 92.3 MC |
Coefficients
Term | Coef | SE Coef | T-Value | P-Value | VIF |
Constant | 38.21 | 1.59 | 24.10 | 0.000 | |
Days | -0.1271 | 0.0239 | -5.32 | 0.013 | 1.00 |
pH | -3.497 | 0.776 | -4.51 | 0.020 | 1.00 |
EC | -10.55 | 1.50 | -7.02 | 0.006 | 1.00 |
MC | 92.3 | 28.5 | 3.24 | 0.048 | 1.00 |
Model Summary
S | R-sq | R-sq(adj) | R-sq(pred) |
Days 1.51160 | 90.41% | 87.21% | 77.53% |
pH 1.75122 | 87.13% | 82.84% | 76.18% |
EC 1.16878 | 94.27% | 92.35% | 87.08% |
MC 2.30271 | 77.74% | 70.32% | 45.36% |
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