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Monitoring and Analysing the Impact of Industry on the Environment
Monitoring and Analysing the Impact of Industry on the Environment
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The economic, social and environmental circumstances of the 1970s and 1980s have compelled the American Soil Society to critically look for a way to measure soil’s suitability for sustainable environmental management. Awareness about assessment of soil quality has been raised since 1946, first as the Soil and Health Foundation and later by the Rodale Institute.
Until the current research that necessitated this article, the challenge had been how to directly quantify soil quality so that scientists would have a harmonised standard for assessing the quality of soils for different defined purposes or uses. All hands have been on deck to solve this problem, but most solutions have come from the USDA / Soil Science Society of America. Because of advancement in the precision of soil management, other soil scientists and societies (the British Society of Soil, the Italian Society of Soil, Indian Society of Soil and the Nigerian Society of Soil, etc) have made their own valuable contributions towards having a harmonised soil quality standard.
Research by the USDA on soil quality is currently underway even as the Environmental Protection Agency (EPA) has come up with a generally accepted standard for air and water quality respectively for sustainable management of the environment. Soil is a very dynamic resource that developing such a generalised standard poses a challenge, as we always have to deal with its spatio-temporal variability. Soil variability results from differences in use or management to which the soil had been put. Because of this, many researchers have different perspectives as to what will constitute a Minimum Data Set (MDS) for defining soil quality for a particular use. Apart from having no consensus on what constitutes a MDS, another problem we have faced is in defining a scoring function that will truly quantify the soil’s quality with exactitude.
In the study that was conducted, soil quality of minimally disturbed areas was assessed for their respective quality standards in judging which soil could be more fertile than the other. The research also included some qualitative terrain characteristics in defining soil quality for some predefined uses such as use of the soils for sanitary landfill area, land application of food processing wastes and municipal sewage sludge using a comparative means of assessment.
If we assess disturbed areas (urban soils and industrial soils), we cannot use the same standard for minimally disturbed areas to make judgments about their quality, because the soils may contain hydrocarbons or heavy metals that are harmful to human health. We could have used a comparative assessment for all the studies (fertility and urban) but soil quality index (SQI) was better used for assessment of soil quality for fertility purposes.
Soil quality assessment is necessary for growers and governments to easily garner information about what a soil needs to perform. Soil quality has been defined as “the capacity of a soil to function within ecosystem and land use boundaries, to sustain biological productivity, maintain environmental quality, and promote plant and animal health”. A quantitative assessment of soil quality is needed to determine the sustainability of land management systems as related to agricultural production practices, and to assist government agencies in formulating and evaluating sustainable agricultural and land use policies. Soil quality cannot be measured directly, but must be inferred from soil quality indicators.
The objective of our study was to determine changes in the quality of soils of southeastern Nigeria as a result of differences in land use. Further discussions were done for a dynamic assessment of soil quality. Soils from thick forest land, crop land and secondary forest were sampled and analysed for the important parameters collected into a MDS. Twelve parameters were chosen as the minimum data set (MDS) for defining the soil quality for the Nigerian agricultural production systems. The parameters include: nitrogen, phosphorous, potassium, calcium, magnesium, organic carbon, Cation Exchange Capacity (CEC), percentage base saturation, soil pH, Coefficient of Linear Extensibility (COLE), bulk density and hydraulic conductivity.
These were minimally chosen because of the need for easy water movement and availability of nutrients in the soil. Micronutrients were not included because availability of macronutrients implies the availability of other micronutrients that are associated with them. These micronutrients obey Liebig’s Law of minimum and as such were not necessarily included in what constituted the minimum data set for defining soil quality.
Linear function for assessing the quality of some Southeastern Nigerian soils was deduced using a total of 249 soil samples collected from three Southeastern Nigerian soils, namely Imo State soils, Abia State soils and Akwa Ibom State soils. Surface soil samples were collected (sampling at least 70 points) from each of the three states using a Dutch auger and taking note that their parent materials differed naturally. Soils of these three states cut across different parent materials including: coastal plain soils, false bedded sandstones, clay-shales, upper coal measure and lower coal measures.
In Imo, different locations for the study included Emeabiam and Mbato, Egwe and Amuro; and prevalent land uses in areas sampled included crop land, secondary and thick forest land, respectively. Because soil quality indicators are majorly sensitive to changes in land use, management or conservation practices, higher priority was given to them for site selection than to the differing parent materials or soil classes. Imo State is located approximately between longitudes 6 °50’E and 7 °25’E and latitudes 4 °45’N and 7 °15’N. In Abia State, different locations for the study include; Isuochi, Uturu and Arondizuogu and the same land uses (crop land, secondary forest and thick forest soils, respectively) were identified. Abia State is located between longitudes 7 °10’E and 8 °25’E and latitudes 4 °40’N and 6 °14’N. Again, three areas (Uyo, Abak and Etinam) in Akwa Ibom State were used for the study where same land uses were prevalent. Akwa Ibom State is located approximately between longitudes 7 °25’E and 8 °25’E and latitudes 4 °32’N and 5 °33’N.
The three states lie within a tropical climate characterised by rainy season (February/March – November) and dry season (November – February/March). Annual rainfall in the three states ranges from 3000 mm along Atlantic coast to 2000 mm in the hinterland. Average annual temperature of the three states ranges from 25 to 27 °C. Most of the crop lands in the locations were non-irrigated to cash crops such as oil palm plantation, and annual and perennial crops.
Statistically representative samples of at least 70 points were selected within each of the states. Points were selected at random without regard to differing soil parent materials or soil classes. In Imo State, 72 soil samples were collected in different locations of the state. In each of the sampling areas identified by differences in the land use such as crop land (for Emebiam and Mbato), secondary forest (Egwe) and thick forest land (Amuro), six replicate samples were collected (with the aid of a Dutch auger) perpendicular to the direction of the upper slope position, middle and lower positions respectively, making a total of 72 soil samples [(6 × 3) + (6 × 3) + (6 × 3) + (6 × 3)].
In Abia State, a total of 75 soil samples were collected in three different locations in the state including; Isuochi, Uturu and Arondizuogu. Surface soil samples were collected from each of the areas by using a Dutch auger to collect 25 soil samples [(8 × 3) + (9 × 3) + (8× 3)] in each of the areas respectively. Land uses identified in the sampled areas were as well crop land, secondary forest and thick forest, respectively. In Akwa Ibom State, 102 soil samples were collected at different locations including, Uyo, Abak and Etinam. Surface soils were sampled from each of the landforms with the aid of the Dutch auger.
In each of the sampling areas, nine samples were collected perpendicular to the direction of the upper slope, 16 from the middle slope and nine again were collected from the bottom slope. The same sampling method were repeated for the other two landforms making a total of 102 samples [(9 × 3) + (16 × 3) + (9 × 3)] collected from the state. The same aforementioned land uses were identified in each of these areas.
A total of 249 soil samples were processed, and laboratory analysis of the selected 12 quality indicators collected into Minimum Data Set (bulk density, hydraulic conductivity, coefficient of linear extensibility, pH, Ca, Mg, K, cation exchange capacity, base saturation, total organic carbon, total nitrogen and available phosphorus) were carried out for the 249 samples. Bulk density was determined using the core method. Hydraulic conductivity was determined using the constant head permeameter method.
The coefficient of linear extensibility (COLE) was calculated as the difference in bulk density of undisturbed core samples when moist (33kPa or 10kPa if coarse sandy soil) and when oven dried. Exchangeable base cations (Ca, Mg, K, and Na) were extracted with 1 N NH4OAc (pH 7). Exchangeable calcium and magnesium were determined by ETDA complexio-metric titration while exchangeable potassium and sodium were determined by flame photometry. Cation Exchange Capacity (CEC) was determined by ammonium saturation (NH4OAc) displacement method conducted at pH 7.0 as was explained in the Laboratory Manual for Agronomic Studies in Soil, Plant and Microbiology, University of Ibadan.
Base saturation was calculated as a percentage of the value of the summation of exchangeable bases over cation exchange capacity. Soil organic carbon was analysed by Walkley and Black wet digestion method. Soil pH was measured potentiometrically in both water and 0.1 N KCl at the soil- liquid ratio of 1:2.5. Total nitrogen was determined by micro Kjedahl digestion method and available phosphorous was determined by Bray II method.
The mean of each of the 12 indicators of the MDS or parameters were used to calculate the soil quality index. Scales for scoring were set at either the upper or lower limit of the “moderate” standards, depending on the range of values of the MDS in the three States. These were weighed by using the scales as the denominator and the values of the MDS as numerators.
According to Liebig, scores ranging from 0 to 1 were then assigned applying more is better or less is better function (if the best soil functionality is associated with high or low values, respectively) to the indicators included in the MDS. The more is better function was applied to hydraulic conductivity, organic carbon and cation exchange capacity for their roles in water retention and availability, structural stability and fertility as well as to total nitrogen, available K, Ca and Mg content and base saturation. On the contrary, the less is better function was applied to bulk density, because its high value has an inhibitory effect on growth of plant roots and soil organisms and to coefficient of linear extensibility because its high value will result in shrink-swell activity of the soils.
Regarding pH and available P concentration, optimal range was identified as 7 for pH Liebig and 10-20 mg kg-1 for available P concentration, thus scores were assigned by considering the more is better function.
Lastly, indicator scores were integrated in an additive index according to Andrews, calculated by summing scores of each indicator and dividing by the total number of indicators:
SQI = Soil quality index Si = the score assigned to each indicator n = the number of indicators included in the MDS.
Soil organic carbon scored low in most of the states yet their scores were inconspicuous in determining the overall trend of the index. It followed a direct trend only in Abia State. Cation exchange capacity as well as Ca, Mg and K all scored low in the assessment while total Nitrogen and Available P scored low especially in Abia and Akwa Ibom States respectively. The results showed that soil quality was highest in thick forest land use in Imo State. Soil quality index of all the land uses in the states were highest in Imo state having a high (SQI > 0.70) quality index according to the rating of Marzaioli in the thick forest soil than do other states. According to Rutigliano, in shrubland soils, the presence of a dense herbaceous layer due to re-colonisation by spontaneous plants, after soils were laid fallow, caused a soil quality approaching that of undisturbed soil, suggesting a recovery of soil quality.
Without considering the land uses in the states and making comparison on soils of the different states, there were significant differences (LSD, p < 0.05) in the soil quality of the different states. According to the rating of Marzaioli, the soils sampled from Imo had an intermediate quality (SQI = 0.65), and soils from Abia (SQI = 0.50) and Akwa Ibom (SQI = 0.41) States both had a low soil quality. These differences in soil quality index could be reflected from both pedogenetic and land use, management and conservation practice differences in the states. Most researchers (Bredja; Rutigliano; Zornoza and more recently Marzaioli) concentrated efforts on discussing soil quality based on dynamic soil differences such as land use, management and conservation. The SQI of the land uses showed there were no significant differences among them indicating that the differences in land use had not influenced the quality of the soils. It can be argued that these stipulated land uses were not very different from each other, because effect of continued use of land for cropping will depend on the number of years it has been put to use and on management practices. Although data on the crop land use (period of use) was not considered in the course of this study, it is true that increasing/including more land uses in the states will be able to show clearly the differences when a one-way ANOVA is performed. Other researchers have only discussed SQI based on its influence on different land uses without considering soil qualities of near same land uses. This will in fact show significant differences due to inherent soil property differences.
Published: 06th Sep 2016 in AWE International
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