Physical properties of shrimp-based fertilizers, substrates and commercial fertilizers.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3650",leadTitle:null,fullTitle:"Radio Communications",title:"Radio Communications",subtitle:null,reviewType:"peer-reviewed",abstract:"In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. \r\nIn this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. 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The main fungi that produce aflatoxins are Aspergillus flavus and Aspergillus parasiticus, which are abundant in warm and humid regions of the world. Aflatoxin-producing fungi can contaminate crops in the field, at harvest, and during storage.
\r\n\r\n\tPeople can be exposed to aflatoxins by eating contaminated plant products (such as peanuts) or by consuming meat or dairy products from animals that ate contaminated feed. Farmers and other agricultural workers may be exposed by inhaling dust generated during the handling and processing of contaminated crops and feeds.
\r\n\r\n\tAflatoxins pose a potential threat to human and animal health through the consumption, contact, or inhalation of foodstuffs and feedstuffs prepared from these commodities. As a result of the adverse health effects of mycotoxins, their levels have been strictly regulated especially in food and feed samples. Therefore, their accurate identification and determination remain a Herculean task due to their presence in complex food matrices. The great public concern and the strict legislation incited the development of reliable, specific, selective, and sensitive analytical methods for mycotoxins monitoring.
",isbn:"978-1-83969-304-5",printIsbn:"978-1-83969-303-8",pdfIsbn:"978-1-83969-305-2",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"34fe61c309f2405130ede7a267cf8bd5",bookSignature:"Dr. Lukman Bola Abdulra'uf",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10502.jpg",keywords:"Extraction, Chromatography, Health Risk, Carcinogenic, Chemical, Foods, Contaminations, Exposure, MRLs, Daily Intake Level, LD50, Toxicology",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 11th 2020",dateEndSecondStepPublish:"December 9th 2020",dateEndThirdStepPublish:"February 7th 2021",dateEndFourthStepPublish:"April 28th 2021",dateEndFifthStepPublish:"June 27th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Lukman Bola Abdulra’uf holds a Ph.D. degree in Analytical Chemistry from the University of Malaya, Kuala Lumpur, Malaysia. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"67701",title:"Comparative Assessment of Shrimp Hydrolyzates as Alternative Organic Fertilizers for Legumes",doi:"10.5772/intechopen.86914",slug:"comparative-assessment-of-shrimp-hydrolyzates-as-alternative-organic-fertilizers-for-legumes",body:'The global annual production of shrimp is nearly 4 million tons generating almost half of this weight in waste. This waste in turn, is composed of chitin, which forms microfibrillar arrangements embedded in a protein matrix with CaCO3. A green alternative for the use of this waste is to use it as an organic fertilizer in form of pellets or as a hydrolyzed material. The search for new organic fertilizers is important due to the limited availability of manure and compost in coast lines resulting promising the use of shrimp waste as an alternative organic fertilizer for crops. Currently, there is no information regarding the organic cultivation of legumes fertilized with shrimp-based waste.
A rapid and efficient shrimp waste hydrolysis could be accomplished by microwaves, which are non-ionizing electromagnetic radiation having wavelengths from 1 mm to 1 m corresponding to frequencies from 300 GHz to 300 MHz, respectively. This radiation could provide the energy required to break the chemical bonds found in organic molecules such as C-C bonds (347 kJ/mol), and hydrogen bonds such those found in the lignocellulosic biomass of rice straw (3.9–10.1 kJ/mol) rendering a 5-fold increase in the yield of sugars [1].
Leguminous crops have been used for several centuries as a source of food for humans and animals [2]. These plants are originated from the Americas but they are now cultivated all over the world due to their high nutritional and culinary values. In fact, they contain high amounts of protein, vitamins (i.e., thiamine, pyridoxine, and folic acid), dietary fiber, complex carbohydrates (i.e., starch), and nutrients such as iron, potassium, phosphorous, selenium, molybdenum and calcium. They are highly desirable in the human diet since are low in sodium and calories [3]. Further, legumes are so important for human nutrition that ∼12 million tons of Phaseolus vulgaris (PV) are consumed every year worldwide. Moreover, in 2014 the U.S. produced more than 86,700 metric tons of merely kidney beans. In fact, every day ∼14% of the U.S. population eats dry edible beans. Legumes are a vibrant part of food security across the world, especially in many developing countries. Thus, ∼400 million people in the tropics eat beans as part of their daily diet. Legumes also provide income for millions of farmers, typically in Latin America and Africa.
The growth and development of legumes would require appropriate quantities of nutrients for their optimal development; otherwise, physiological deficiency symptoms could occur [4]. Nowadays, the current trend is the use of organic fertilizers for optimal vegetable development. However, the heterogeneity of the physical and chemical characteristics of the different organic fertilizers may give rise to different crop yields. Interestingly, legumes are known to be nitrogen fixers as they take nitrogen from the air by demand and release it into the soil, fulfilling their own nitrogen needs. This implies the need for an organic fertilizer which provides low levels of nitrogen accordingly [5]. For this reason, the intense use of chemical fertilizers for plant development is not advisable since it causes depletion of beneficial soil microbiota and potential pollution of soil and water [6].
Nowadays, organic fertilizers derived from worm castings, peat, manure, and poultry guano have been used to obtain an efficient organic crop production of several plant species [7]. They increase the organic matter and microorganism activity, improve porosity, water retention, and ion exchange capabilities of the soil. They also prevent root burning or destruction of soil microflora since they contain amino acids, organic matter and a variety of micronutrients that replenish the nutrient level of the soil and feeding important soil microorganisms [8]. For instance, the application of vermicompost in soil decreases root rot of beans and produces vigorous plants [9].
The main objective of the current study was to compare the physical characteristics of several shrimp-based fertilizers and their microwave-assisted hydrolyzates on the development of leguminous plants treated with these fertilizers under greenhouse conditions following an organic production. Fertility and substrate management in organic greenhouse production is important in short-term, low fertility requiring crops. Developing organic fertilizers that slowly release nutrients could improve the crop management of legumes produced organically in container production systems.
Dry shrimp exoskeletons were obtained from the pacific coast of Tumaco (Colombia), milled on a cutting mill (Model 3, Willey Arthur Thomas Co., Philadelphia, USA), and passed through a # 100 mesh sieve. This material was labeled as F0. In a separate experimental set, pellets were produced using microcrystalline cellulose (MCC) as a pelletization aid. Thus, pellets made of pure MCC were made by wetting ∼20 g of MCC with 20 mL of distilled water and passed through a #16 mesh sieve (1190 μm size) with a force ≤11.2 N/cm2 measured with a load cell (LCGD-10 K, Omega Engineering, Inc., Stamford, CT). The extruded thus obtained was put in the spheronizer chamber (Model 1LA70-4YA60, Siemens), which was operated at the spheronization rate of 15 Hz and spheronization time of 120 s producing beads, which were then oven-dried at 40°C for 24 h. These pellets were then labeled as FPC. In another experimental set, a 50:50 mixture of raw waste and MCC was wetted with 42.5 mL of water and submitted to spheronization under the same conditions as explained for the raw MCC. These pellets were labeled as FPE. On the other hand, a hydrolyzed shrimp waste was obtained using a focused microwave apparatus (Samsung, Model MW 630 WA). A 10% power was applied to ensure reproducibility. Approximately, 20 g of sample was dispersed in 200 mL of a 5% NaOH solution and submitted to a refluxing action keeping the temperature between 50 and 60°C. Radiation was continued for the selected exposure times of 0.85 h so a hydrolysis degree of 42% was obtained. The hydrolyzed product was then cooled down, neutralized with 1 N HCl, filtered and dried at 60°C for 24 h. Further, pellets of this material were made under the same conditions employed for FPE and labeled as FHPE.
The physicochemical and functional properties of these pellets were compared to those of the untreated soil substrate (SS), untreated cotton substrate (CS) and two commercial fertilizers named as CF1 and CF2. SS was obtained from a local farm and contained a mixture of virgin soil (fine loam) and rice husk at a 3:2 ratio. CF1 and CF2 (N-P-K of 13.2-1-0) corresponded to an organic and extruded synthetic fertilizer, respectively.
The greenhouse study was conducted in a non-temperature controlled agricultural research station near Medellin (lat. 6.12° N, long. −75.54° E, altitude 2550 m) having a 4 × 4 m (width × length) greenhouse surrounded by a 10-mm light diffusive template glass. The growing condition in the greenhouse was a mean temperature of 23°C day/15°C night and from 65 to 85% RH as recorded during the growth season. No supplementary light or heating was applied in the greenhouse station.
The soil used in the study was a mixture of fine loam (taken from 0 to 30 cm of a virgin soil) and rice husk at a 3:2 ratio. The soil was put in 2 kg PVC pots (15 cm diameter). Healthy and mature legume seeds were obtained from a retail center of Medellin. Subsequently, one seed was sown in each pot randomly and irrigated uniformly with tap water. A plastic saucer was placed under each pot to prevent water loss by leaching. The plants were irrigated using one dripper per plant (at a discharging rate of 10 mL/h) and the total daily irrigation during the growing season ranged from 240 to 350 mL/plant. The irrigation volume ensured that soil was maintained wet in the growing medium.
After germination, only vigorous seedlings were selected for growth in each pot. Five replications of each treatment were arranged in a completely randomized design. The germinated seeds were then treated with ∼4 g of the fertilizers in three amendments and these treatments were started on 1-week-old legume seedlings that emerged from direct seeding [12 d after direct seeding (DADS)]. Four and eight weeks after direct seeding, a second and third treatment was applied, whereas in the control treatments, no fertilizer was added (water only). The composition and physical properties of the fertilizers are listed in Table 1. Legume plants were trained to a single vertical pole around the main stem and fixed to a wooden stick having 1.5 m high from the ground to support the plant. There was no need to apply pesticides to control insects since plants were healthy and developed normally.
Sample | MC (%) | Sugars (mg/g) | pH | ε (%) | Prot (%) | Ash (%) | CHO (%) | ξ (mV) | BD (g/cm3) | Soil con (μS/cm) | Soil pH | IE (meq/g) | Con (μS/cm) | Water sorption parameters (Young-Nelson model) | ||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A (10−4) | B | E | ΔH (kJ/mol) | r2 | ||||||||||||||
F0 | *11.0 ± 0.3 | *29.5 ± 0.1 | *8.3 ± 0.2 | *81.8 ± 0.0 | *3.20 ± 0.16 | *3.6 ± 0.37 | 90.3 ± 0.31 | −15 ± 0.5 | *0.32 ± 0.01 | 20.9 ± 3.4 | 6.7 ± 0.2 | 0.81 ± 0.14 | *210 ± 4.4 | 1.63 | 0.20 | 16.41 | −6.94 | 0.9668 |
FPC | 2.5 ± 1.1 | 0.0 ± 0.0 | *5.0 ± 0.1 | *48 ± 0.0 | 0.0 ± 0.0 | 0.05 ± 0.01 | *98 ± 1 | −16.4 ± 0.5 | 0.89 ± 0.0 | 20.6 ± 2.6 | 7.1 ± 0.2 | 0.26 ± 0.03 | 53.8 ± 2.1 | 13.3 | 0.14 | 6.50 | −4.64 | 0.9593 |
FPE | 3.5 ± 1.2 | *10.5 ± 0.1 | *8.5 ± 0.1 | 62 ± 0.0 | *1.2 ± 0.12 | 1.7 ± 0.3 | *94 ± 1 | −11.9 ± 1.6* | 0.57 ± 0.02 | *27.6 ± 2.5 | 7.0 ± 0.2 | 0.33 ± 0.03 | *280.7 ± 1.6 | 99 | 0.11 | 5.36 | −2.70 | 0.9800 |
FHPE | 4.2 ± 1.3 | 0.002 ± 0.000 | *8.5 ± 0.2 | 66 ± 0.0 | 0.0 ± 0.0 | 1.5 ± 0.4 | 90 ± 0.9 | −9.4 ± 0.5* | 0.51 ± 0.02 | 18.4 ± 2.7 | 7.1 ± 0.2 | 0.34 ± 0.01 | *142.3 ± 0.6 | 4.9 | 0.02 | 0.90 | 0.27 | 0.9826 |
SS | *44 ± 1.3 | 0.002 ± 0.000 | 7.2 ± 0.4 | *83.3 ± 0.0 | *9.41 ± 0.43 | *45.4 ± 1.3 | *0.6 ± 0.1 | −22.1 ± 0.3 | *0.20 ± 0.02 | 16.4 ± 8.4 | 6.9 ± 0.1 | 0.67 ± 0.1 | 40.1 ± 3.1 | 3.11 | 0.61 | 46.09 | −9.50 | 0.9582 |
CS | *8.5 ± 0.6 | 0.0 ± 0.0 | 7.1 ± 0.2 | *89.8 ± 2.1 | 0.0 ± 0.0 | 0.05 ± 0.01 | *98 ± 1 | −21 ± 2.6 | *0.15 ± 0.03 | *35.3 ± 3.2 | 7.2 ± 0.1 | *2.21 ± 0.51 | *79.8 ± 2.2 | 0.423 | 0.34 | 24.13 | −7.89 | 0.9888 |
CF1 | *8.3 ± 0.5 | 0.002 ± 0.000 | 6.7 ± 0.2 | 58.3 ± 3 | 0.0 ± 0.0 | *42.8 ± 0.40 | *20.2 ± 1.2 | −17.8 ± 4.6 | *0.58 ± 0.02 | 10 ± 5.4 | 6.9 ± 0.2 | 0.41 ± 0.12 | *459 ± 2.8 | −0.81 | 0.28 | 10.69 | −5.87 | 0.9941 |
CF2 | 5.8 ± 0.3 | 0.001 ± 0.000 | 6.7 ± 0.2 | *70.3 ± 2.1 | 0.0 ± 0.0 | *5.3 ± 0.3 | *10 ± 2 | −22.9 ± 5.2 | *0.51 ± 0.02 | 12.5 ± 2.5 | 6.8 ± 0.2 | *9.49 ± 3.1 | *704 ± 3.7 | −19 | 0.63 | 4.64 | −3.81 | 0.9902 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | NA | NA | NA | NA | NA |
Physical properties of shrimp-based fertilizers, substrates and commercial fertilizers.
MC, moisture content; Prot, proteins; CHO, carbohydrates; Con, conductivity; BD, bulk density; ε, porosity; ξ, zeta potential; FPC, cellulose pellets; FPE, exoskeleton pellets; FHPE, hydrolyzed exoskeleton pellets; SS, soil substrate; CS, cotton substrate; IE, ionic exchange; F0, raw waste; CF1 and CF2 correspond to the commercial fertilizers; A and B correspond to the fraction of adsorbed and absorbed water in the particle, respectively; E, equilibrium constant between the mono layer and liquid water; ΔH, heat of sorption. NA, not applicable; mean values with an asterisk within the column are significantly different according to the Tukey’s test at p < 0.05.
Plant height was evaluated on a monthly basis during the crop cycle. Harvesting started at 90 DADS and finished at 110 DADS. Legume plants were harvested twice a week when the pots reached maturity. Yield parameters that were measured for crop performance included pod length, pod mass, seed mass and pod number. The soil samples for chemical and microbiological analyses were collected from the surface layer (0–10 cm).
The pH of the 1% w/v fertilizer dispersion was measured with a handheld combo electrical conductivity (EC) and pH meter (EC600, Extech Instruments, Melrose, MA, USA). The moisture content of the materials was obtained by gravimetric methods, using a moisture balance analyzer (MB200, Ohaus, Parsippany, NJ, USA) equipped with a halogen lamp at 120°C. The sensitivity of the measurements was 0.01%. The total ash content was determined following the methodology described in the AOAC [10]. Briefly, samples were heated on a muffle oven (N31R, Mueller and Krempel, Nabertherm, Germany) at 546°C for 7 h. The amount of the cooled residue was taken as the total ash content. The content of sugars was determined by the phenol-sulfuric acid colorimetric method [11].
The elemental analysis was conducted by Energy Dispersive X-ray analysis (EDX) (JEOL 6490LV, Peabody, MA). About 0.2 g of the samples were spread evenly over an aluminum stub and sputter-coated on a vacuum chamber (Desk IV, Denton Vacuum, Moorestown, NJ USA) with a 30% gold coating for 5 min and operated at 15 kV. X-ray diffraction patterns were taken using an X-ray generator with CuKα radiation and the linear surface sample scanning was conducted for 300 s, 10 mm depth of field and 50 μm diffusion. A Malvern Nano-ZS90 Zetasizer equipped with a Zetasizer Software (vs 7.11, Malvern Instruments Ltd., UK) was employed to determine the particle charge at 25°C using the principle of Laser Doppler Velocimetry (LDV). The zeta potential (PZ) measurements were performed by adding 700 μL of the sample in a polystyrene cell. Samples were analyzed between 12 and 16 cycles with a voltage of 4 mV. The ionic exchange test was carried out by weighing from 0.5 to 1 g of sample and 10 mL of 6 N HCl was added and allowed to stand for 24 h followed by centrifugation for 20 min at 1550 rpm. Subsequently, it was submitted to washing with 1% saline solution twice and titrated with 0.8 N NaOH solution. All measurements were expressed on a dry weight basis.
Water sorption studies were conducted employing the static gravimetry method on chambers having several saturated salts rendering different relative humidities. Thus, K2CO3, NaBr, NaCl, KCl, KNO3 and H20 rendered constant relative humidities of 43, 58, 68, 75, 94 and 100%, respectively. The isotherms were built at 25°C and samples were allowed to reach equilibrium for 2 weeks when the difference between two consecutive measurements was not larger than 0.1%. Data were fitted to several sorption models, and only the one that presented the best fit was discussed in this study. The ability of the fertilizers for water sorption was studied by applying the Young and Nelson model which is expressed as:
where mm, mc and mi correspond to the tightly bound water, condensed external water and internally absorbed water, respectively. Further, m corresponds to the total moisture content, θ is the fraction of surface covered by a monomolecular layer, ψ is the fraction of surface covered by a water layer of two or more molecules thick, and β is the total amount of adsorbed moisture in the multilayer. Moreover, H1, Hl, k and T, correspond to the heat of adsorption of water bound to the surface, heat of condensation, the gas constant and temperature, respectively. A and B are dimensionless constants related to the fraction of adsorbed and absorbed water in the particle, respectively, and E is the equilibrium constant between the monolayer and liquid water. The product Aθ is related to the amount of monolayer moisture, A(θ + β) is the externally absorbed moisture during the sorption phase, whereas Bψ corresponds to the amount of absorbed moisture during the sorption phase [12].
These tests were conducted on samples without any previous treatment according to the National Technical Standard 4092 of microbiology. Briefly, 1 g of sample was dispersed in 10 mL of peptone water, making the pertinent dilution factors from 1×10−1 to 1×10−10. Subsequently, 1 mL of the solution was poured onto a 20 mL culture plate (Merck). Samples were then incubated at 37°C between 24 and 48 h. The results were reported as colony forming units per gram of fertilizer (CFU/g).
The principal component analysis (PCA) was the type of multivariate analysis used to identify and compare the relationships and patterns among the physicochemical and functional properties of the fertilizers. The software Minitab® (v. 16 Minitab, Inc., State College, PA) was used for data processing. The relationship between the different crop characteristics was assessed by the Pearson’s correlation coefficient at a significance level of p < 0.05. Additional post hoc assessment was performed using the Tukey’s test (p < 0.05) when significant differences between means were observed. The condition of normality was checked using the Shapiro-Wilk test.
Microwave radiation accelerated the degradation of alkaline shrimp waste forming a product having a hydrolysis degree of 42%. Thus, hydroxyl radicals of the alkaline media along with microwave radiation contributed to molecular weight reduction of waste compounds such as carbohydrates and proteins and avoided the need for a time-consuming composting of the raw waste and thus, decreased the initial microbial population avoiding further release of putrescine and other nitrous volatile compounds. Shrimp waste possesses the striated type muscle arranged into muscle fibers that are bound together by a connective tissue where the prevalent amino acid is lysine. These muscle proteins are associated to chitin and minerals such as calcium phosphate. The protein and chitin availability are important since they will eventually turn into accessible nitrogen for legumes. The magnitude of the peptide and glycosidic bonds cleavage during microwave hydrolysis rendered an organic fertilizer having a moderate hydrolysis degree.
During the wet massing process MCC was essential as spheronization aid. Previous studies (data not shown) determined the need of at least 50% MCC as optimal in order to obtain a spherical pellet having good mechanical properties (FPE and FHPE). Thus, MCC fibers alone or combined with waste coalesced and formed larger particles which were then shaped once they passed through the screen orifices. These, in turn, were molded in the spheronizer which cut-off and rounded-off the sharply and roughly surfaces. The rotating plate operating at the 15 Hz rate and residence time of 120 s produced a denser and smoother pellet surface due to the combined action of the centrifugal force created by plate rotation, the vertical force formed by collision, and the gravitational force allowing for the formation of a toroidal or twisted rope motion having an spiral pattern. As a result, this high frequency and short residence time generated more frictional and rotational forces where the initial small, oblong and irregular particles experienced growth, folding and edge rounding which was subsequently shaped into dumb bells. These dumb bells were then twisted, broken, rounded and transformed into spherical or semispherical beads.
On the contrary, raw waste per se failed to produce pellets or aggregates due to the lack of plasticity needed for the spheronization process, this fact also occurred by employing a very short residence time resulting in pellets of a predominantly small size, oblong shape and rougher surface. The spheronization platform usually renders bead sizes of about 1000 μm. In this case, by using a #20 screen sieves the size of the resulting beads ranged from 1.2 to 3 mm. Particle size tends to increase with residence time and this variable was kept at 120 s avoiding loss of moisture and maintaining the required plasticity for pellet growth. This high spheronization rate guarantees the formation of beads with diameters larger than 1 mm. The spherical morphology and particle size played a major role on densification and porosity. This occurrence was reflected on the resulting porosity which in turn, decreased with pelletization. On the other hand, the degree of densification decreased by the spheronization process. This is explained by the highly regularly-shaped particles that are less likely to accommodate in the powder bed under the action of an external force as compared to the non-spheronized irregular particles. Flowability is the property that reflects the way in which gravity overcomes the cohesive forces and the interlocking structure of the particles. In general, the flowability of the pellets was high ranging from 13.4 to 16.4 g/s, independent of the average bead mass. A constant plate diameter of 30 cm was employed at spheronization rates of 15 Hz which is equivalent to 900 rpm and peripheral velocity of 1415 cm/s, respectively. This rotational speed and short residence time (120 s) was suitable to obtain spherical beads.
The nutritional content of the shrimp-based fertilizers (SBF) is listed in Table 1. The hydrolyzed product retained much of the initial nutrients contained in the raw shrimp exoskeletons. The alkaline microwave hydrolysis disrupted the inter and intra-molecular hydrogen bond pattern of complex carbohydrates and proteins initially present in the material, disturbing the regularity of the 3D packing and stereochemistry between chains, especially of the most accessible amorphous regions. As a result, the alkaline hydrolysis of the non-crystalline fraction removed monomer blocks of repeated units, especially those located at the crystallite surface and hence, NaOH accessed the β-1,4N-acetyl and peptidic linkages, simultaneously. The net result was a reduction in the crystallinity of the shrimp fertilizer. In fact, the application of high intensity waves caused chemical and mechanical degradation in the waste particles, resulting in changes in the native shrimp protein and carbohydrate structure into a molten globule state.
The pH and moisture content of these fertilizers ranged from 5.0 to 8.5 and from 2.5 to 11%, respectively. Once the fertilizers were incorporated into the soil maintained a slightly neutral ambient (∼6.7–7.2) and the electrical conductivity ranged from ∼12 to 28 μS/cm. A neutral pH ensured a good availability of the nutrients to the leguminous plants. The high moisture content eased the transformation of macromolecular N into NH4+ and NO3− by bacteria action resulting in its mineralization and easy uptake by plants as reported previously [13]. The slightly alkaline pH of F0 is attributed to the presence of peptides, and elements such as Ca2+ and Mg2+. Further, these divalent ions can then be adsorbed onto the surface of tiny clay particles of the soil which had a net negative charge. The magnesium level in the shrimp-based fertilizers (SBF) was lower than that of calcium so its effect on the soil structure was negligible. The negative surface charge of soil particles is believed to improve P availability in form of phosphates as present in shrimp waste. These phosphates along with the P2O5 of CF2 could be responsible for the large PV crop yield found in F0 and CF2, respectively. Conversely, K was virtually absent in most fertilizers and its synergistic effect on crop yield was not noticed.
The zeta potential indicates the average charge in the particles and gives a measurement of the ion activity of the fertilizers. All materials exhibited a net negative charge and CF2 had the largest ion exchange capability and electrical conductivity altogether. Conversely, FHPE exhibited the smallest value of electrical conductivity. Interestingly, CS showed a large ionic exchange capability, but a moderate electric conductivity due to the residual ionized functional groups present in this type of cellulose.
Table 2 lists the elemental composition of each type of SBF, substrates and commercial fertilizers. Alkaline microwave hydrolysis had a marked effect on the nutritional content of the shrimp waste. This had a large content of essentially C, N, Ca and P. On the other hand, Fe, Si, Al, Mg and Cl were present as the main microelements. The content of Mg, was larger in the F0 than SS, CS and pellets, whereas the K content was low in all cases except for CF1. The C/N ratio was slower than 10 for F0, FPE, FHPE, CF2, and FPC whereas CF1 (10.5) and SS (33.1) showed the largest C/N ratio due to their low content of N. Further, the SS and CF1 were poor in organic nitrogen, but rich in carbon, silicon and aluminum. On the other hand, CS had a poor content of most elements except for carbon and oxygen. The SS, FPC and CS presented low levels of essential elements such as N, P, and Ca as compared to F0, FPE and FHPE. Interestingly, CF1 and SS showed traces of other microelements such as K, Ti, and essentially CF1 was the only fertilizer which contained traces of Mn. On the other hand, CF2 contained N from urea and P from P2O5 at a 13:1 ratio.
Element | F0 | FPC | FPE | FHPE | SS | CS | CF1 | CF2 | p-value |
---|---|---|---|---|---|---|---|---|---|
C | 43 ± 3.3 | 33.1 ± 5 | 43 | 38.1 | 33.1 ± 1.1 | *53.5 ± 4.5 | 41.0 ± 10.3 | 39.0 ± 3.63 | 0.00 |
O | 33.3 ± 2.1 | *42.5 ± 3 | 33.3 | 37.9 | *42.5 ± 0.4 | *46. ± 5.2 | 39 ± 6 | 37.0 ± 2.4 | 0.00 |
N | *15.9 ± 5.4 | 0 ± 0 | *15.9 | *7.95 | 0 ± 0 | 0 ± 0 | 0 ± 0 | *15.9 ± 4.9 | 0.00 |
Ca | *5.8 ± 2.7 | 0 ± 0 | *5.4 | *2.8 | 0.2 ± 0.1 | 0 ± 0 | 0.9 ± 0.2 | 0.14 ± 0.14 | 0.00 |
P | *1.5 ± 1.4 | 0 ± 0 | 1.5 | 0.8 | 0 ± 0 | 0 ± 0 | 0 ± 0 | 1.2 ± 1.2 | 0.00 |
Si | 0.2 ± 0.1 | *18.2 ± 1 | 0 ± 0 | 0 | *18.1 ± 1.6 | 0 ± 0 | *11.4 ± 3.2 | 0.9 ± 0.4 | 0.00 |
Fe | 1.5 ± 1.0 | 0 ± 0 | 0 ± 0 | 0.59 | 1.2 ± 0.8 | 0.0 ± 0.0 | 1.8 ± 1.8 | 5.4 ± 5.4 | 0.08 |
Al | 0.1 ± 0.1 | *4.1 ± 0.5 | 0 ± 0 | *2.1 | *4.1 ± 0.7 | 0.5 ± 0.5 | *2.7 ± 0.5 | 0.0 ± 0.0 | 0.00 |
Mg | 0.4 ± 0.1 | 0.21 ± 0.1 | 0.35 | 0.28 | 0.2 ± 0.1 | 0 ± 0 | *0.6 ± 0.4 | 0.4 ± 0.3 | 0.05 |
Cl | *0.2 ± 0.1 | 0 ± 0 | *0.23 | 0.12 | 0 ± 0 | 0 ± 0 | *0.3 ± 0.1 | 0.03 ± 0.03 | 0.00 |
Na | 0.2 ± 0.1 | 0.05 ± 0 | 0 ± 0 | 0.1 | 0.1 ± 0.1 | 0.0 ± 0.0 | *0.8 ± 0.2 | *0.47 ± 0.13 | 0.00 |
K | 0 ± 0 | 0.13 ± 0 | 0 ± 0 | 0.1 | 0 ± 0 | 0 ± 0 | *1.1 ± 0.2 | 0 ± 0 | 0.00 |
Ti | 0 ± 0 | 0 ± 0 | 0 ± 0 | 0 | 0.5 ± 0.5 | 0. ± 0.0 | *0.4 ± 0.4 | 0 ± 0 | 0.06 |
Mn | 0 ± 0 | 0 ± 0 | 0 ± 0 | *0.1 | 0 ± 0 | 0 ± 0 | 0.03 ± 0.03 | 0 ± 0 | 0.00 |
C/N | 2.7 | 33.1 | 2.7 | 4.8 | 33.1 | 53.5 | 41 | 2.5 | NA |
Elemental analysis of the shrimp-based fertilizers, substrates and commercial organic fertilizers (n = 3).
F0, raw waste; FPC, cellulose pellets; FPE, exoskeleton pellets; FHPE, hydrolyzed exoskeleton pellets; SS, soil substrate; CS, cotton substrate CF1 and CF2 correspond to the commercial fertilizers; mean values with an asterisk within the column are significantly different according to the Tukey’s test at p < 0.05.
The water vapor sorption isotherm of a material describes the relationship between the relative vapor pressure or water activity, (aw) and water content over a range of aw values obtained at a given temperature [14]. The fitting water sorption parameters obtained from the Young-Nelson model revealed a good fitting to this model having an r2 larger than 0.9582 as compared to other models not shown in this study.
Figure 1a shows that during the first sorption stage (aw < 0.45), the isotherms exhibited a convex shape as the water molecules rapidly sorb onto the available sorption sites until a monolayer is formed. The shape of the isotherms during this first stage did not differ substantially among the different SBF, but was larger for SS and CF2. Thereafter, there was a gradual increase in water content with aw up to ∼0.80 where an abrupt increase of water content was observed possibly due to capillary condensation phenomena. Interestingly, most fertilizers showed a steady increase in monolayer and multilayer formation up to aw of 0.45, afterwards the water molecules although still in vapor form, begin to diffuse within the particle core except for SS, FPE and FPC in which this process started at a very low aw (Figure 1b). Therefore, in these materials isotherms proved that water did not form a continuous monolayer because the multilayer and particle water absorption occurred simultaneously. This phenomenon has been attributed to the tendency of water molecules to cluster around exchangeable cations found in different soils [14]. As a result, water molecules bind as succeeding layers of water molecules rather to empty sites on the surface of the particle. Thus, the formation of a second layer probably started at lower concentration than those corresponding to the monolayer formation. Clustering was expected to occur in most cases since the amount of water molecules on the particle was higher than the quantity that can be bound within the particle. Further, SS and CF2 per se had an innate ability to uptake and keep water within the particles and were able to preserve the wet environment for the optimal root and microbiota development.
Water sorption isotherms fitted to the Young-Nelson model. (a) Fitted isotherms, (b) deconvoluted sorption behavior for the monolayer and multilayers, and (c) deconvoluted sorption behavior for the intrinsic absorbed water (n = 3).
CF2 at all aw showed the lowest tendency for clustering, but the largest sorption within the particle core. The deconvoluted curves showed that the monolayer formation presented a type III Langmuir isotherm, whereas the curves for the multilayer sorption showed a type II isotherm. Interestingly, CF2 also showed the largest cation exchange capability and ionic conductivity. This agrees with previous studies that reported a relationship between the high water sorption and the ion exchange capability of the soil [15].
The raw soil substrate (SS) showed the largest E parameter and hence, presented the largest heat of endothermic sorption (ΔH). Further, SS and CF2 showed the largest intrinsic absorbed water (B parameter), whereas CS showed the largest adsorption ability forming multilayers. CF2 and SS showed the largest hygroscopicity, especially at a water activity larger than 0.4. Further, these two samples had the largest ability to absorb water intrinsically, whereas SS and CS per se were able to form large water multilayers around the particles. In addition, the water sorption behavior of SBF was comparable to that of CF1.
The ionic exchange capability of the SBF decreased upon hydrolysis as compared to F0 due to leakage of some ions such as calcium and phosphates. Further, the incorporation of these fertilizers into the soil did not have a marked effect on the physicochemical properties of the topsoil due to a dilution effect. Thus, the electrical conductivity of the soil was low (10–28 μS/cm) as compared to the pure fertilizers, but outside the range recommended for other horticultural plants (0.76–4.0 mS cm−1) [16]. Further, the negative charge of the SBF is due to the residual amine groups of chitin and amino acids. The ash content of the SS (45.4%) and CF1 (42.8%) were larger than most fertilizers (<5.3%) mainly due to their high silicate and carbonate content. The content of carbohydrates of FPE and FHPE (90–94%) was lower than that of CS and FPC; whereas the content of proteins was relatively low and tended to disappear upon hydrolysis as happened for sugars. Moreover, densification (0.51–0.89 g/cm3) and porosity (48–66%) increased upon pelletization, whereas CS and SS as expected showed the lowest bulk density, but the largest total porosity.
It was estimated that complex carbohydrates present in SBF such as chitin could act as a cementing agents bonding soil particles together improving soil structure and stability. Further, it is reported that calcium ions could act as a cementing agents, bonding soil particles into aggregates resulting in the formation of strong, water-stable aggregates [17]. However, the net postharvest bulk density of the soil did not vary significantly upon treatment with fertilizers probably due to the low applied rate, and density remained in the range generally considered suitable for the normal growth of crops. This low bulk density made root growth and penetration easier and improved the size and system of voids in the soil matrix enabling aeration and water movement. Moreover, the particle size of the powdered fertilizers ranged from 50 to 150 μm and that of the soil and pellets were about 300 and 2 mm, respectively being able to decompose slowly matching the particle size of the soil.
Figure 2 depicts legume growth as a function of time. The largest and fastest growing period of both legumes occurred within the first 2 months of the crop cycle. Both plants followed a sigmoid or S-shaped curve during the growing season corresponding to the period of rapid nutrient uptake. Further, both legumes showed the best growing phase upon fertilization with CF2. Conversely, a slow growth profile for both plants was observed once fertilized with FPE and FHPE. This phenomenon is explained by the reduction of essential nutrients different from C and O.
Growth profiles given by shrimp-based fertilizers, substrates and commercial fertilizers: (A) Phaseolus vulgaris and (B) Pisum sativum.
On the other hand, the pod length, pod mass, and seed mass of PV were outstanding when treated with CF2 and comparable to those of F0 (Table 3). Conversely, crop quality of Pisum sativum (PS) as described by these parameters was superior for SS and only FHPE showed good characteristics among the fertilizing pellets. Further, FPC had the worst crop quality an in this particular case plants were not able to render any kind of grain. Likewise, the fact of having a large pod number was not necessary translated into a large crop yield, but pod length, pod mass and seed mass were all good indicatives of crop yield for both legumes (r > 0.859).
Sample | Phaseolus vulgaris | Pisum sativum | ||||||
---|---|---|---|---|---|---|---|---|
Pod length (cm) | Pod mass (g) | Seed mass (g) | Pod number | Pod length (cm) | Pod mass (g) | Seed mass (g) | Pod number | |
F0 | *10 ± 1.2 | *4.56 ± 2.0 | 1.83 ± 0.3* | 2 ± 0.3 | 5.5 ± 1.1 | 1.23 ± 0.8 | *0.7 ± 0.1 | *3 ± 1 |
FPC | 7 ± 0 | 0.77 | 0.1 ± 0.0 | 1 ± 0.0 | *0 ± 0 | 0 ± 0 | *0 ± 0 | *0 ± 0 |
FPE | 4.1 ± 1.3 | 0.46 ± 0.1 | 0.22 ± 0.1 | *4 ± 0.5 | 5 ± 0 | 0.6 ± 0 | *0.28 ± 0 | 1 ± 0 |
FHPE | 5 ± 1.6 | 0.82 ± 0.2 | 0.41 ± 0.1 | 1.5 ± 0.5 | 4.4 ± 0.5 | 0.4 ± 0.1 | 0.3 ± 0.1 | *3 ± 0 |
SS | 4.9 ± 1.3 | 0.62 ± 0.2 | 0.35 ± 0.1 | 1 ± 0.0 | *6.5 ± 1.2 | *3.9 ± 0.9 | *1.43 ± 0.1 | 1 ± 0 |
CS | 4.3 ± 1.3 | 0.26 ± 0.1 | 0.1 ± 0.0 | 2 ± 0.1 | 5 ± 0.5 | 1.1 ± 0.5 | 0.43 ± 0.1 | 2 ± 0.1 |
CF1 | *7.7 ± 1.1 | 2.13 ± 0.8 | *0.73 ± 0.1 | *3 ± 1.0 | 6.0 ± 0.8 | 0.74 ± 0.1 | 0.46 ± 0.1 | 2 ± 1 |
CF2 | *10.1 ± 0.1 | *4.7 ± 0.1 | *1.2 ± 0.1 | 1.1 ± 0.2 | 5.5 ± 0.6 | 1.5 ± 0.6 | 0.6 ± 0.2 | 2 ± 0.5 |
p-value | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
Effect of shrimp-based fertilizers, substrates and commercial fertilizers on plant development for Phaseolus vulgaris and Pisum sativum.
FPC, cellulose pellets; FPE, exoskeletons pellets; FHPE, hydrolyzed exoskeleton pellets; SS, soil substrate; CS, cotton substrate; CF, commercial fertilizers; mean values with an asterisk within the column are significantly different according to the Tukey’s test at p < 0.05.
The SBF were applied at a rate of 4 g/kg soil in three monthly amendments. SBF having 8–20% N had a variable effect on legume growth characteristics depending on the composition. As a result, they showed distinctive quantitative and qualitative traits of grain yield of legumes, especially for PV. It has been reported that a large amendment of 20% organic fertilizer (vermicompost) was needed to get the highest pod weight, pod number, pod dry weight and pod length of legumes [5]. In this study, there was a remarkable mismatch between plant growth and plant yield. For instance, CF2 and F0 rendered plants with a good growth and crop yield especially for PV, whereas CP2 only led to a good plant growth rather than crop yield in PS. This is explained by the content of urea:P2O5 (N/P ratio of 13.2), which is recommended by the supplier for the rapid plant growth. In all cases, the N uptake and growth rate were prominent within 30 and 60 days after sowing. In other words, the growth rate progressively increased over time during the vegetative growth up to 4–8 weeks after which growth slowed down as the reproductive phase initiated. Legume growth was not significantly improved with most SBF despite of having a considerable content of available N due to the slow release of this element. However, macroelements such as N, C, P, and Ca were available 45 days after sowing for the appropriate blooming and protein development. Interestingly, the unfertilized CS showed a slow development and crop quality for both legumes, especially for PS and thus these plants were not very efficient as atmospheric N fixers to compensate for the lack of N in the CS. In this case, the branched root hair systems of the legumes were not sufficient to ease N mineralization during the growing phase and as a result, they showed the poorest crop yield.
The soil amended with the fertilizers had pH values between 6.7 and 7.1, which are considered optimum for the rapid development of most ubiquitous microorganisms. At this pH range N loss due to ammonia volatilization is prevented since this phenomenon only occurs at acid pH (<6.0) [18]. Soil porosity was ∼83% and moisture at saturation was >40% and these levels were not affected by fertilization. The lower water content of SBF was attributed to the presence of insoluble carbohydrates, proteins and of calcium ions. The high moisture content in the soil near to field capacity was responsible for the high diversity of viable microbial during the legume developing phase. These in turn, promoted mineralization and increased available N. The high population of aerobic bacteria found during the whole crop cycle eased nitrogen fixation from the fertilizers and the atmosphere. Interestingly, PV was able to modify its own root environment to maximize nutrient uptake. Thus, the inherent absence of N of the unfertilized substrate forced the plant to increase the root pattern so the nitrogen demand could be obtained by microbial (especially fungi) N2 fixation, as reported previously [19]. However, this N uptake was not sufficient to achieve an optimum plant growth of PS since the unfertilized substrates showed the poorest growth rate in the CS. Conversely, SS showed better crop quality than CS due to the higher content of Si, Mg, Fe and Ca which were absent in CS.
It is accepted that during the decomposition of an organic fertilizer the microbial population requires an optimal diet with a C:N ratio of ∼15:1 to meet their needs for nutrients. Since the F0, FPE, FHPE and CF2 had a C:N ratio of less than 15:1 they had more N than the microflora require for their own growth in the initial crop cycle and are likely to provide significant plant available N leading to an increased mineral N levels through mineralization carried out by microbial metabolism (production of NH4+ and NO3−) [20]. This phenomenon was reflected on a large microbial population in the soil within the first month (>50,000 cfu/g of bacteria and > 100 cfu/g for fungi). Conversely, the SS, FPC, CS CF1 had a C:N ratio of more than 25:1 and thus, it is assumed a rapid immobilization of the scarce N by microorganisms in this growing phase [21]. Since those fertilizers had N content above 2.5%, they are expected to release nutrients once decomposed by the soil microbiota. The N, P and K ratio of the SBF, and SS were ∼1–0.1–0.0, and 0–0–0.1, respectively. These ratios are different from other reported for fertilizers such as cow manure (0.97–0.69–1.66) and compost of raw straw (0.81–0.18–0.68).
The fertilizers once incorporated into the soil showed a variable microbial population which decreased over time, possibly due to depletion of soil nutrients that share with plants in a symbiotic way. In fact, the bacteria population was larger in soils containing PV than PS (Table 4). Conversely, the latter favored the proliferation of fungi in the soil. Further, fertilizer type also influenced the bacterial proliferation; for instance, CF1 rendered the largest bacterial population in the soil, whereas CF2 maintained a virtually constant bacterial count. On the contrary, the soil population of fungi tends to increase over time except for soils treated with CF1 and fertilizing pellets where tended to decrease. This fact was reflected on the bacteria to fungi ratio which decreased over time except for the fertilizing pellets and commercial products which increased and remained unchanged, respectively. The high microbial content of the fertilizers mingled with those of the soil microflora favoring the rapid development of bacteria and fungi, which in turn decreased during the crop cycle.
Time (month) | F0 | FPC | FPE | FHPE | SS | CS | CF1 | CF2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PV | PS | PV | PS | PV | PS | PV | PS | PV | PS | PV | PS | PV | PS | PV | PS | |
Mesoaerobic bacteria (cfu × 104/g). Basal count: 5×104/g | ||||||||||||||||
1 | 100 | 40 | 88 | 38 | 72 | 40 | 90 | 20 | 102 | 36 | 1 | 0.2 | 580 | 370 | 12 | 32 |
2 | 103 | 26 | 0.36 | 8.25 | 98 | 6 | 126 | 100 | 130 | 102 | 3 | 0.1 | 785 | 64 | 17 | 33 |
3 | 56 | 4.9 | 0.3 | 8.9 | 68 | 4.9 | 52 | 48 | 33 | 30 | 2 | 0.1 | 54 | 23 | 19 | 28 |
Fungi (cfu × 103/g). Basal count: 100/g | ||||||||||||||||
1 | 0.1 | 2 | 2 | 3 | 14 | 2 | 20 | 36 | 10 | 23 | 0.25 | 0.5 | 7.8 | 6 | 18 | 48 |
2 | 1 | 6 | 0.1 | 1.7 | 1.1 | 1 | 1.2 | 1.4 | 14.5 | 15 | 1 | 0.5 | 2.5 | 4 | 27 | 37 |
3 | 10 | 12 | 0.6 | 0.1 | 0.2 | 5 | 1.6 | 0.81 | 23.6 | 28 | 1 | 0.5 | 1 | 3 | 18 | 47 |
Bacteria/fungi ratio | ||||||||||||||||
1 | 10,000 | 200 | 440 | 127 | 51 | 200 | 45 | 6 | 102 | 16 | 40 | 4 | 744 | 617 | 7 | 7 |
2 | 1030 | 43 | 36 | 49 | 891 | 60 | 1050 | 714 | 90 | 68 | 30 | 2 | 3140 | 160 | 6 | 9 |
3 | 56 | 4 | 5 | 890 | 3400 | 10 | 325 | 593 | 14 | 11 | 20 | 2 | 540 | 77 | 11 | 6 |
Total aerobic bacteria and fungi of the soil fertilized with the shrimp-based fertilizers and commercial fertilizers.
FPC, cellulose pellets; FPE, exoskeleton pellets; FHPE, hydrolyzed exoeskeleton pellets; SS, soil substrate; CS: cotton substrate; CF, commercial fertilizers; PV, Phaseolus vulgaris; PS, Pisum sativum.
The multivariate analysis rendered interesting facts about this study. The first three components explained 73.3% of data variability (Figure 3). In the PCA plot four great clusters are observed apart from the center. The first one depicts the influence of Mg and Na on crop quality of PV and the second cluster relates the pod number of PV with the content of Na, P, N, and sugars. The third cluster is related to the crop quality of PS and soil pH; whereas the fourth cluster relates Si, Al with the C/N ratio. Moreover, a correlation analysis confirmed that fertilizers having a high content of Si also had high Al (r > 0.920). Likewise, fertilizers having a high level of N also showed low levels of O and C/N (r > −0.874). Further, high levels of Mg were correlated with those of Na (r = 0.806) and fertilizers having a high content of N also showed high levels of P (r = 0.999).
Principal component plot for key properties of fertilizers.
The raw waste rendered an optimal crop quality, especially for PV, but showed a lower growth as compared to CF2. Conversely, the pelletization of raw shrimp waste had a deleterious effect on the crop quality of both legumes. Further, the absence of N, Ca and P in the unfertilized substrates limited legume growth, and microbial activity. This suggested that nutrient sufficiency ranges may require minor adjustment for plant development. Further, viable microorganism population increased in the beginning of the crop cycle and then declined possibly due to depletion of nutrients, but provided short-term fertility benefits for the legumes productivity. These fertilizers are considered more ecofriendly, more efficient, and accessible to marginal and small farmers located in the coast lines. Shrimp-based fertilizers were found to be an alternative soil amendment for legume crops grown using organic methods.
The authors are grateful to Colciencias for providing the financial resources for the execution of this study through the Grant No. 111571551545 and contract no. 036-2016. Authors thank CODI for their sustainability strategy 2018–2019 of University of Antioquia.
The authors declare no conflict of interest.
Land use and land cover changes have significant environmental consequences at local, regional, and global scales. These changes have intense implications at the regional and global scales for global loss of biodiversity, distresses in hydrological cycles, increase in soil erosion, and sediment loads [1]. At the local level, changes in the use of land and its cover affect watershed runoff, microclimatic resources, processes of land degradation and landscape-level biodiversity, soil erosion, and sediment loads [2]. All these have direct impacts on livelihoods of local societies.
The Shire River in Malawi, southern Africa, is among the areas where land use land cover change (LUCC) has become more prevalent in recent years resulting into severe soil erosion and causing heavy siltation downstream [3, 4, 5, 6, 7, 8, 9]. The river is an important source of livelihood to many people, using the water for agriculture, domestic purposes, and the generation of electricity [6, 8, 10]. One of the most important structures across the Shire River is the Nkula B Hydroelectric Power Station situated in the middle section of the river. The dam at Nkula Falls that supplies water into the power station has, in recent times, been threatened with massive siltation, some studies attributing this to increased human population and agricultural activities [5, 6, 8]. The conceptual setting of this study originates from a strong link that exists between land use change and soil erosion [8, 11, 12, 13, 14, 15]. Land use and management practices are important factors in determining the extent of soil erosion [8, 15]. Good vegetation cover promotes infiltration of water into the ground and soil retention, while deforestation results into increased runoff than infiltration occurring during periods of more precipitation [16, 17, 18]. Increased runoff consequently leads to stronger soil erosion usually in areas with poor vegetation cover [8, 19, 20]. Erosion of soil under continuous cultivation is the most serious form of resource degradation occurring in Malawi [3, 8, 19, 21, 22, 23]. The rate of soil loss in Malawi is currently estimated at 29 t/ha/year [24], which is higher than the previously reported 20 t/ha/year [21]. In the middle Shire River, estimated soil loss between the year 2000 and 2014 ranged from 0.1 to 21.1 t/ha/year [24, 25]. According to the Malawi Government Report (2015), the middle Shire River catchment has many bright spots (areas experiencing high soil loss but declining trends over time), for example, Neno and Ntcheu in the west and Zomba and Chiradzulu in the eastern side of the river.
The question regarding land use changes over time, and its driving forces in the middle Shire River catchment nevertheless remain unresolved [4, 6]. Such knowledge is critical to the development of policies and action plans necessary for changing current LUCC trends in the area as it has been observed in other places [26, 27, 28, 29, 30]. Furthermore, problems of LUCC are global and serious in many developing countries where increasing population has resulted into excessive pressure on natural resources [8, 30].
The study was carried out to understand the impact of land use and land cover changes on the Nkula Dam in the middle Shire River catchment, Malawi. The LUCC drivers analyzed in this study include biophysical changes (e.g., climate change) and human activities (e.g., population, poverty, land policies, and GDP growth) [3, 4, 6]. Climate and socioeconomic data were compiled to analyze the drivers of LUCC in the study area. Geographic information systems (GIS) and remote sensing techniques which are gaining increased recognition globally as rapid methods of acquiring and analyzing up-to-date information over a large geographical area were used in the study [30, 31, 32, 33].
The Shire River is the largest river in Malawi, originating from Lake Malawi which supports vast agricultural and socioeconomic activities in its catchment (Figure 1) [34]. The river is divided into three sections, namely, the upper, middle, and lower Shire [34, 35]. This study focused on the catchment of the middle section of the river which includes the Shire Plain which is bounded by mountains on both sides and the Nkula Dam downstream [34, 36]. The plain is more extensive to the west of the river than it is to the east (Figure 1). The middle section of the Shire River has eight administrative districts, supporting a population of about 5 million people (Figure 1).
Map of Malawi (left) showing the middle Shire River and its catchment (right). Eight administrative districts are located in the study area.
Climate in the middle Shire River catchment area varies due to differences in altitude with annual average precipitation ranging from 750 to 2500 mm [35, 37]. Highlands receive more rain which begins in November and ends late in April [6, 37]. Annual average temperature of the area is around 23°C, with highlands in the east experiencing cooler temperatures than plains in the west [6, 35]. The rocks in the study area are mainly composed of Precambrian basement complex and igneous rocks [37]. Amphibolite and granulite facies are dominant in the western and eastern side of the Shire River, respectively, while soils in the river’s catchment are dominated by Cambisols [6, 24, 37].
The following procedures were followed in order to answer the study questions: firstly, six Landsat images for the dry seasons (to avoid cloud cover effects) of 1989, 1993, 2000, 2006, 2011, and 2015 were downloaded from the United States Geological Survey (USGS,
Landsat images were processed using ENVI 5.1 Software to study information on the types of land use and their spatial patterns. To analyze these spatial patterns, the following steps were followed: firstly, relative radiometric correction was done on each band to eliminate errors arising from radiation caused by weather conditions; secondly, multiband combination of Landsat images was done in preparation for research spectral characteristics of various types of land use; thirdly, geometric correction of remote sensing images was done using Malawi DEM, Universal Transverse Mercator Projection, Arc 1960, and UTM Zone 36S, based on 1:50,000 topographic map scale so that it fits with the Landsat images [38, 39]. This helps to eliminate position errors of Landsat images which the terrain, position of the sun, and angle sensor may produce. A mosaic of required images was prepared and a single image generated. Atmospheric Landsat images were then corrected by ENVI 5.1 FLAASH module.
After processing the Landsat images, identification of different land use classes was done where some visual designs like texture, tone, and the effect zones were used [38]. The land in the study area was classified according to its use or description such as cultivated land, water, forest (indigenous and plantations were combined), etc. When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it’s among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38, 39, 40, 41, 42, 43, 44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41, 45]. Representative zones for each desired class were located in the image with adequate number of pixels covering the known classes to reduce the image noise [38]. Secondly, training area number and percentage were identified in order to classify several training and test areas. These results were compared with supporting ground data so that the new training statistics could be derived. Thirdly, a statistical file known as spectral signature was created by the image processing software for each class because each and every pixel can only be assigned to one spectral class. Lastly, each pixel was allocated to the most likely class based on the maximum likelihood algorithm where each pixel is assigned to the spectral class that has the greatest probability density function for the multispectral values of the pixel. Maximum likelihood algorithm is the most commonly used algorithm in which a pixel is classified into the corresponding class [38, 43, 46]. Land cover types were then classified into the following eight main classes according to Anderson et al. [47]: (1) forest, (2) shrubland, (3) grassland, (4) cultivated land, (5) bare land, (6) water bodies, (7) wetland, and (8) artificial surfaces (Table 1).
No. | Land cover class | Description |
---|---|---|
1 | Forest | Woodland open general (15–65%) with herbaceous layer. Broadleaved deciduous trees, closed >(70–60)%. Vegetative cover is in balance with the abiotic and biotic forces of its biotope |
2 | Shrubland | Closed to open (thicket) (15–100%) scattered trees |
3 | Grassland | Herbaceous closed vegetation (15–100%) with some trees, shrub Savannah, and permanent marsh |
4 | Cultivated land | Areas where the natural vegetation has been removed or modified and replaced by other types of vegetative cover of anthropogenic origin. All vegetation that is planted or cultivated with intent to harvest is included in this class |
5 | Bare land | Bare rock and/or coarse fragments. Areas that do not have an artificial cover as a result of human activities. These areas include areas with less than 4% vegetative cover |
6 | Water bodies | This class refers to areas that are naturally covered by water, such as lakes, rivers, snow, or ice |
7 | Wetlands | Areas that are transitional between pure terrestrial and aquatic systems and where the water table is usually at or near the surface or the land is covered by shallow water |
8 | Artificial surfaces | Areas that have an artificial cover as a result of human activities, such as construction (cities, towns, and transportation), extraction (open mines and quarries), or waste disposal |
Land cover classes considered and their description [71].
A total of 165 training sites (sampled portions of the scene, purposely selected, for the derivation of the training statistics) were chosen for each image to ensure that all spectral classes constituting each land use and land cover categories were adequately represented in the training statistics to classify the entire scene [48]. Classification was done using ground checkpoints, digital topographic maps, vegetation cover map, and the researchers’ knowledge of the study area [49, 50]. A total of 156 sampling points (GPS + photograph) were collected out of the 165 training sites during the dry season to avoid cloud cover effects which is more common in rainy season. Land use types at the sampling sites were evaluated according to field surveys (photographs + GPS) where photographs were taken using a camera and coordinates of the spot were taken using GPS. Accuracy of the supervised classification methods was checked by a confusion matrix of accuracy (Table 2) [38, 44, 51] to ensure that various measures, such as error-rate, accuracy, specificity, sensitivity, and precision, were checked.
Actual type | Classified type | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Forest | Shrubland | Grassland | Cultivated land | Artificial surfaces | Wetland | Water bodies | Bare land | Actual sum | Accuracy | |
Forest | 9 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 11 | 82% |
Shrubland | 0 | 14 | 1 | 1 | 0 | 0 | 0 | 0 | 16 | 88% |
Grassland | 0 | 1 | 20 | 1 | 0 | 1 | 0 | 1 | 24 | 83% |
Cultivated land | 0 | 0 | 1 | 21 | 1 | 0 | 0 | 0 | 23 | 91% |
Artificial surfaces | 1 | 1 | 1 | 2 | 34 | 0 | 0 | 0 | 39 | 87% |
Wetland | 0 | 0 | 1 | 0 | 0 | 8 | 0 | 0 | 9 | 89% |
Water bodies | 0 | 1 | 0 | 0 | 0 | 1 | 32 | 0 | 34 | 94% |
Bare land | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Classified sum | 10 | 18 | 25 | 25 | 35 | 10 | 32 | 1 | 156 |
Confusion matrix of accuracy evaluation in middle Shire River catchment in 2015.
Landsat image classified type results were compared with the field survey results to evaluate their accuracy and then calculated using confusion matrix evaluation table (Table 2).
LUCC drivers were mainly analyzed using descriptive methods due to inavailability of spatial socioeconomic data from the government database. Pearson correlation coefficients between socioeconomic data and land use types were analyzed in SPSS for Windows version 10.
The overall classification accuracy ranged from 82 to 94% (Table 2). The western side of the Shire River covers an area of approximately 3353 km2, while the eastern side is 2770 km2 comprising 55 and 45% of the total area, respectively. Regions were defined by slope of less than 10o as plain/flat area. According to Table 3, total plain/flat area covers 2417 km2 which is lesser compared to highlands (with slope ranging from 10o to 90o) covering 3706 km2. Eastern and western plain/flat areas cover 988 and 1429 km2, representing 41 and 59% of the total plain/flat area of the study area, respectively (Table 3).
Area/coverage | Plain (≤10°) | Highlands (10–90°) | ||
---|---|---|---|---|
Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | |
Western side | 1429 | 59 | 1075 | 29 |
Eastern side | 988 | 41 | 2631 | 71 |
Total catchment area | 2417 | 100 | 3706 | 100 |
Distribution of plains and highlands in eastern and western side of the middle Shire River.
The middle Shire River catchment is dominated by shrubland, grassland, cultivated land, and forestland, which accounted for 36, 28, 22, and 12% in 1989, respectively (Figure 2).
Land use and land cover changes from 1989 to 2015.
Findings (Table 4) show significant land use and land cover changes in the middle Shire River catchment over the 26-year period.
Land cover type | Year | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | |||||||
Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | Area (km2) | % | |
Forest | 739 | 12.07 | 679 | 11.08 | 545 | 8.90 | 479 | 7.82 | 481 | 7.86 | 662 | 10.80 |
Shrubland | 2201 | 35.95 | 1986 | 32.44 | 2264 | 36.97 | 2043 | 33.37 | 1835 | 31.85 | 2040 | 32.97 |
Grassland | 1719 | 28.07 | 1838 | 30.02 | 1451 | 23.69 | 1692 | 27.63 | 1617 | 24.53 | 1255 | 20.52 |
Cultivated land | 1367 | 22.33 | 1538 | 25.12 | 1745 | 28.50 | 1814 | 29.64 | 2067 | 33.76 | 2073 | 34.09 |
Artificial surfaces | 26 | 0.43 | 28 | 0.45 | 33 | 0.54 | 37 | 0.60 | 39 | 0.64 | 43 | 0.71 |
Wetland | 35 | 0.57 | 23 | 0.38 | 56 | 0.91 | 19 | 0.31 | 38 | 0.63 | 20 | 0.34 |
Water bodies | 31 | 0.51 | 30 | 0.49 | 20 | 0.33 | 30 | 0.49 | 36 | 0.58 | 22 | 0.44 |
Bare land | 4 | 0.06 | 2 | 0.03 | 9 | 0.15 | 9 | 0.15 | 9 | 0.15 | 8 | 0.13 |
Area (km2) and percentages of different land cover types from the year 1989 to 2015.
Artificial and cultivated land increased by 65 and 52%, respectively, in the 26-year period, while forest cover, grass, and shrubland decreased by 35, 27, and 7%, respectively. Other land classes such as wetlands and water bodies show fluctuations (Figure 2 and Table 4). Spatially, in 1989, total cultivated land in the western side was 694 km2 which increased to 1226 km2 by the year 2015, representing 21 and 37% of the total land in the western side, respectively (Table 5).
Location/district | Year | ||||||
---|---|---|---|---|---|---|---|
1989 | 1993 | 2000 | 2006 | 2011 | 2015 | ||
Western side | Balaka | 335 | 556 | 627 | 655 | 688 | 853 |
Mangochi | 59 | 51 | 41 | 80 | 47 | 91 | |
Neno | 25 | 41 | 49 | 38 | 28 | 53 | |
Ntcheu | 275 | 298 | 219 | 226 | 219 | 228 | |
Total area | 694 | 946 | 935 | 999 | 982 | 1226 | |
Eastern side | Blantyre | 359 | 264 | 362 | 381 | 244 | 278 |
Chiradzulu | 33 | 9 | 19 | 17 | 18 | 23 | |
Machinga | 184 | 247 | 264 | 263 | 135 | 368 | |
Zomba | 96 | 71 | 165 | 155 | 122 | 194 | |
Total area | 673 | 591 | 810 | 816 | 520 | 862 |
Changes in cultivated land area (km2) in districts of the middle Shire River catchment.
This suggests an increase of 16% of cultivated land in the western side between 1989 and 2015. In the eastern side, cultivated land increased from 673 to 862 km2 within the same period, representing 24 and 31%, respectively, of the total land area indicating a 7% change. In 1989, the western side of the Shire River catchment mainly consisted of shrubland, grassland, and forestland which accounted for 35, 33, and 10%, respectively. In the eastern side, shrubland, grassland, and forestland accounted for 37, 22, and 15%, respectively. The western side (Balaka, Neno, and Ntcheu) and eastern side (Zomba) are the main districts where forest, shrubland, and grassland decreased the most. For example, in Balaka District, forest area reduced from 11% in 1989 to 2% in 2011 before increasing to 3% in 2015, while shrubland decreased from 38% in 1989 to 18% in 2011 and then increased to 23% in 2015. Forestland in Neno District decreased from 10% in 1989 to 1% in 2011 and then increased up to 5% in 2015, while shrubland decreased from 35% in 1989 to 19% in 2015 and grassland from 27% in 1989 to 17% in 2015 with some fluctuations in between the years. In Ntcheu District, grassland decreased from 35% in 1989 to 15% in 2015. Forest cover in Zomba district declined from 19% in 1989 to 7% in 2006 and then started to increase from 2011 reaching 12% in 2015. Shrubland decreased from 41% in 1989 to 27% in 2015 in the same district.
Results indicate some fluctuations in the amount of rainfall received in the area within the 26-year period that might be due to climate change as a result of land use and land cover changes due to human activities (Figure 3).
Annual rainfall and temperature for the middle Shire River catchment from 1989 to 2015. Circles represent flood years, while rectangles represent drought years (Source: Malawi Meteorological Department).
Rainfall in the catchment area declined continuously from 1989 to 1993, culminating into the drought of 1992 and 1993 (Figure 3) [52, 53]. Malawi is regularly affected by drought and floods [53]. The country (including the study area) was affected by heavy floods in 1989, 1998, 2000, 2001, and 2015, destroying crops and displacing many people (Figure 3) [53]. Earlier studies indicate that rainy season in Malawi is dominated by tropical and extratropical influences with links to the El Niño-Southern Oscillation (ENSO) [54, 55]. Actually, this is reported for the whole of Southern Africa [56].
The population of Malawi which includes districts under study on the western (Mangochi, Balaka, Ntcheu, and Neno) and eastern sides of the middle Shire River (Blantyre, Zomba, Machinga, and Chiradzulu) has been increasing steadily since the 1980s (Figure 4).
Population of districts in the middle Shire River catchment area and GDP (US$) for Malawi from 1989 to 2015 [53].
Increased population is more pronounced in urban areas. For example, in 2015, Blantyre and Zomba cities had 3006 and 2240 people per km2, respectively [34, 53, 57]. There has been a general increase in the GDP over the past 26 years especially between 2006 and 2011 and falling between 1993 and 2003 (Figure 4).
Rainfall affects LUCC in the middle Shire River catchment. Drought and floods in the western side of the river, therefore, have resulted into low crop yield. As a survival mechanism, people resort to cutting down of trees to earn income, causing forest degradation [58, 59]. This may, therefore, explain the concurrent low rainfall received against a sharp decline in forest areas between 2006 and 2011 (Figures 2 and 3). Results in this study agree with an earlier report for the upper Shire River catchment [60] indicating a direct link between poor rainfall (drought/floods) and cutting down of trees.
Rapid population growth is one of the drivers of LUCC in the western side of the middle Shire River earlier reported by [60, 61]. Population increase in the western part of the middle Shire River is mainly attributed to the influx of refugees fleeing the civil war from Mozambique from the 1990s. Population growth leads to urbanization, increase in cultivated land, and residential area [3, 8]. The high population density in Malawi with an estimated growth rate of 2.8% is putting increasing pressure on its natural resources, leading to expansion of farming on marginal lands and forests as well as encroachment into protected forest reserves/parks. Results in this study show a transition of land use from forest, shrubland, and grassland to cultivated land and buildup areas (Tables 4 and 5). These changes mainly occurred between 1989 and 2011 (Figure 2 and Table 4) probably due to increasing anthropogenic pressure on natural forests. Results also show a drastic change in forest/grassland/shrubland between 1989 and 2011 in three out of the four districts (Balaka, Neno, and Ntcheu) in the western side of the middle River Shire. Large proportion of shrubland, grassland, and forestland (84%) in the western part of the river were converted to cultivated land, buildup areas, and/or bare land. This confirms earlier assertion that increasing population results into a decrease in forest area (Figure 5).
Changes in forest, cultivated land in the catchment area, and siltation volume in the Nkula Dam from 1989 to 2015.
The rate of forest decline experienced by Malawi [61] and the Shire River catchment in particular [59], due to heavy dependency on wood for energy, is alarming. Most people around the middle Shire River catchment rely on firewood and charcoal for their daily living [58, 62, 63]. Malawi’s forest cover loss is estimated at 2.6% per annum [64]. The middle Shire River catchment lost, on average, about 4.3% of its forest and shrubland annually between 1989 and 2011 (Table 4), suggesting a negative relationship between population increase and the decline in forest coverage (Figures 4 and 5). Results, nevertheless, showed a recovery in forest cover from 2011 to 2015 (Tables 4 and 5), likely attributed to interventions by the government of Malawi and nongovernmental organizations in strengthening natural resource management policies that started around 2008 up to date [5, 65].
Macroeconomic activities such as increase in manufacturing industries and other businesses which contribute to the growth of GDP often require large areas, which also contributed to the transition of forest/shrubland/grassland into buildup areas. Some of such economic activities include opening of new farms which also require clearing of forest areas (Figures 4 and 5).
National policies in the past have failed to effectively enforce ban of unabated harvesting of forest resources until recently with the introduction of community-based natural resource management groups and intervention of some nongovernmental organizations in afforestation programs. This may explain the increase in forest cover from 2011 to 2015 as earlier indicated (Figure 2 and Table 4). Globally, large expanses of forests are being converted into bare land for domestic purposes and, principally, due to harvesting of timber [66]. In a study carried out between 1989 and 2002 in the upper section of the Shire River, [60] reported impacts of LUCC on the river’s catchment hydrological regime which includes increase in soil erosion. It is reported that agricultural land increased by 18% between 1989 and 2002 [60]. In another LUCC assessment study for Likangala River catchment (a stream from Zomba Mountain which is also a source of several rivers draining into the eastern side of the middle Shire River), woodlands decreased from 135.3 km2 in 1984 to 15.5 km2 in 2013 [67]. These results agree with the present study confirming negative impacts of LUCC. Agriculture is the main source of employment to about 92% of the population in Malawi which lives in rural areas [61, 68]. Increase in agricultural activities leads to cultivated land expansion. Cash crops (e.g., tea, coffee, tobacco, and cotton), subsistence crops (e.g., maize and groundnuts), and animal rearing contribute to the increase in agricultural GDP. Results in the present study agree with a report for the region in which land use change (increase in farming activities) contributed to increase in GDP. Similar findings have also been reported correlating land use to increase in income [67]. The increase in cultivated land and artificial surfaces resulted into a decline in forest and shrubland (Tables 4 and 5).
Furthermore, the country loses about 1.7% of its GDP on average annually due to the combined effects of droughts and floods [69]. Heavy rains received during the 1989 season in the country (Figure 3) were associated with devastating floods that drastically affected the GDP due to crop failure and loss of property as well as human life in the same period but increased in the subsequent year (Figure 4). Although the devastating rainfall in the 1989 season played a role in influencing the GDP, other factors could also be at play due to the fact that drivers of economic growth are diverse and vary in the magnitude of influence. For example, in 1989, Malawi’s economy was associated with high fuel prices due to the war in Mozambique. All fuel transportation routes from the Indian Ocean ports in Mozambique were blocked, and consequently, there was a collapse in commodity prices [68]. Poor sales of tobacco which is the country’s major foreign exchange earner also affected the GDP in 1989 [68]. Increased GDP between 2005 and 2009 has been attributed to stabilization and enhanced income growth, which increased income per capita due to the new economic policies and a stable political environment in 2004 [68].
These study findings show a decline in forests and then an increase over the past 26 years (Figures 2 and 5 and Table 4). Clearing of forests from the catchment of the middle Shire River has subjected the bare soil to erosion which finds its way into the Shire River downstream to the Nkula Dam as a sink. This, thus, may explain the heavy siltation at the Dam which has reduced the volume of water causing problems with normal generation of electricity (Figures 4 and 5). The volume of the Dam at Nkula Falls, which was 3 million m3 at its construction in the 1980s, has recently dropped to nearly half of its original size due to massive siltation which consequently resulted in low production of hydroelectricity, now failing to meet the country’s demand for power. Nkula B Hydroelectric Power Station is the main electricity generation plant in Malawi producing about 124 MW of electricity [70]. The electricity-providing company—the Electricity Supply Commission of Malawi (ESCOM)—is now implementing involuntary power load shedding programs resulting into national frequent blackouts. Consumers now resort to excessive use of firewood/charcoal in place of electricity for cooking and other domestic chores creating a heavy dependency on forest resources.
High soil losses in Ntcheu and Neno Districts could be due to increased population as a result of the refugees’ long time settlement in these areas resulting into removal of forests. The expansion of cultivated land could thus be the cause for increased soil erosion and sediment transport downstream, which consequently accumulate in the Nkula Dam in the middle Shire River (Figure 5). These findings agree with a recent study [6] which confirmed that most of the sediments going into the Shire River and finally depositing at the Nkula Dam originate from the western side of the Shire River. Several studies elsewhere [20, 66] also report the same, linking increased population to deforestation and soil. Loss of forests coupled with agriculture are cause for rapid land use change resulting into increased soil erosion and siltation in the middle Shire River catchment [4, 6, 8] (Figure 5). Malawi, and the middle Shire River in particular, is therefore locked up in a cycle where anthropogenic activities in the river’s catchment meant for a survival alternative to lack of electricity have become a cause for soil erosion and siltation in the river, consequently hampering the generation of the needed electricity.
Findings in this study show significant land use and land cover changes that have occurred in the middle Shire River catchment over the past 26 years which have also affected the Nkula Dam. Forestland and shrubland have declined, while cultivated land and artificial surfaces have increased in the area, and deforestation appears to be more pronounced in the western side of the middle Shire River. Severe siltation downstream in the Nkula Dam appears to be strongly linked to increased soil erosion as a result of land use and land cover change. Notable drivers for LUCC include rapid population growth and GDP, macroeconomic activities occurring especially in the western part of the river such as manufacturing industries, and poor national policies that have failed to effectively enforce ban of uncontrolled harvesting of forest resources.
To solve these problems, there is a need to review and amend weak policies that encourage noncompliance to regulations of managing forests. For example, all policies that may encourage or result in soil erosion such as river bank cultivation must be amended. Powers should be invested in local authorities to take part in protecting the environment and/or in planting trees, and the government should be able to provide seedlings for the operation. This should be done in a competition manner that the village which will perform well should be given some incentives. There is also need to increase fertilizer use so that land expansion for farming is curbed and yields are improved. In addition to that, population growth can be controlled through increase use of family planning. Encouraging children to go to school to avoid early marriages might also help to reduce poverty which will help to avoid cutting down of trees careless. Deliberate programs should be instituted by the government to curb further effects of climate variability such as droughts and floods. Such programs may include good agricultural practices that conserve soil and protect it from water erosion, discourage river bank cultivation, intensify afforestation programs, and ban the burning of charcoal. Findings in this study and the combination of methods used (application of GIS, remote sensing, and analysis of socioeconomic factors) can possibly be applied in areas where similar environmental problems have occurred. It is preferable to include a conclusion(s) section which will summarize the content of the book chapter.
We thank the State Key Laboratory of Estuarine and Coastal Research and Graduate School of East China Normal University (ECNU) for supporting this study. We also appreciate the valuable comments provided by Professor Christo C.P. Van der Westhuizen of North West University (South Africa), Professor Fang Shen of East China Normal University (China), Dr. Mavuto Tembo of Mzuzu University (Malawi), Ms. Lostina S. Chapola of Catholic University (Malawi), Mr. Tanazio Kwenda from the Department of Surveys (Malawi), Mr. Patrick Jambo from Forestry Department of Mzuzu University (Malawi), Mr. Samuel Limbu of the University of Dar es Salaam (Tanzania), Dr. Naziha Mokadem of North West University (South Africa), and the anonymous reviewers who helped us to polish this manuscript.
No potential conflict of interest was reported by the authors.
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