EVALUATING THE EFFECTIVENESS OF FOUR SELECTED METHODS OF INVESTIGATING SOIL EROSION EFFECT ON SOIL PRODUCTIVITY IN NSUKKA, ENUGU STATE, NIGERIA

ABSTRACT

This  study was  conducted  in  2012  at  the  University  of  Nigeria,  Nsukka  Teaching  and Research  Farm (UNN),  and  Ekwegbe,  both in Nsukka  agricultural  zone;  to  evaluate  the effectiveness of four selected methods of quantifying erosion effect on soil productivity at Nsukka, southeastern Nigeria. The four methods were (1)  desurfacing technique (DT), (2) Neill’s (1983) productivity index, (3) modified  productivity index (MPI) and (4) Riquier’s productivity index (RI). Soils were sampled at 0-30, 30-60, and 60-90 cm depth zones at each location prior to planting and after  harvest. Incremental depths (0, 2, and 4 cm) of topsoil

layers were manually removed to simulate erosion at the two sites. Poultry manure (10 t ha-1)

was applied  two  weeks before planting as a soil amendment.  Correlation  and  regression analyses revealed that RI was significantly (p < 0.05) correlated (positively) with plant height at 10 WAP (r = 0.75*), LAI at 14 WAP (r = 0.76*) and pod yield (r = 0.72*) at UNN, and was ranked first in effectiveness, followed by DT, which had a significant (p < 0.05) negative correlation with plant height at 6 WAP (r = -0.45*), while PI and MPI were less effective. At Ekwegbe, DT showed significant (p < 0.05; p < 0.01) negative correlations with plant height at 10 (r = -0.42*) and 14 WAP (r = -0.66**), and LAI at 14 WAP (r = -0.52**), and was validated as the most effective index, whereas RI, PI and MPI were less useful. Based on RI, the soils at UNN and Ekwegbe had productivity index ratings of 15% and 8%, respectively, placing them in the  productivity class IV (poor productivity). Following the application of poultry manure  and tillage, the soils recorded potentiality index ratings of 22% and 13%, respectively,  raising the potentiality class of the UNN sandy clay loam soil to III (average potentiality),  while the Ekwegbe sandy loam soil remained in class IV (poor potentiality). The computed coefficients of improvement (Ci) were respectively 1.5 and 1.6 for the soils at UNN and Ekwegbe. The findings indicated that although the current productivity levels of the soils were poor, there was room for improvement, given necessary management practices.

1.0 INTRODUCTION

CHAPTER ONE

The value of a soil has traditionally been measured in terms of its productivity, defined as the capacity of the soil to produce a plant or sequence of plants under a physically defined set of management practices (Soil Survey Staff, 1951). Maintenance of soil productivity depends on management practices as well as on soil and site  characteristics,  the major ones being soil rooting  depth,  topsoil  thickness,  available  water  capacity,  plant  nutrient  storage,  surface runoff, soil tilth, and soil organic matter content (McCormack et al., 1982)

Soil  quality  has  historically  been  equated  with  agricultural  productivity.  Soil quality,  of which soil productivity is a vital integral component, is defined by Larson and Pierce (1991) as “the capacity of a soil to function, both within its ecosystem boundaries (such as soil map unit boundaries) and within the environment external to that ecosystem (particularly relative to air and water quality)”.  In other words, it is the capacity of a  specific  kind of soil to function,  within  natural  or  managed  ecosystem  boundaries,  to  sustain  plant  and  animal productivity,  maintain  or  enhance  water  and  air  quality,  and  support  human  health  and habitation.

Soil productivity includes two aspects: the inherent productivity of soil and its response to management.  Crop  yield has been considered  the best indicator of soil productivity  as it integrates the inherent and managed components of soil productivity (Pierce, 1994). This is precisely why land evaluation procedures or indices relate to the potential of land to produce food and fibre, and hence must correlate to crop yield (Pierce,  1994). Crop yields are an expression  of historical  production,  whereas  productivity  is  a measure  of potential  yield (Tengberg and Stocking, 1997).

The evaluation of soil productivity using crop yields as the only indicator is inadequate and inappropriate  because  in addition  to  soil  properties,  crop  yields  are  influenced  by  other factors extrinsic to soil such as climate, management, slope, and crop genetic make-up which are difficult to quantify. Crop yield as the measure of soil value is inappropriate because it “simplifies” the soil by treating it as a closed system in which an array of managed inputs produces a single output, crop yield (Pierce, 1994).

Soil  degradation  (long-term  decline  in  soil’s  productive  and  environmental  regulatory capacity) by accelerated erosion is a serious global issue, especially in developing countries of the tropics. The soil and water resources of the tropics are under pressure and prone to degradation  because of harsh environments  and fragile soils in  ecologically-sensitive  eco- regions (Lal, 1994). Erosion ultimately reduces soil productivity. Relationship between soil properties and a soil’s capacity for producing plants or soil productivity has been the focus of a number of current research activities  (National Soil Erosion-Soil Productivity  Research Planning Committee, 1981). The effects of topsoil loss on soil productivity have been more widely researched in the temperate regions than in the tropical regions. At present, there are serious gaps in our  knowledge about the relationships between erosion and productivity in tropical soils  (Lal, 2012). Because of the many hectares of new land  being brought  into cultivation  in the wet and dry tropics,  it is imperative that research information be made available on the erosion-productivity relationship for these soils; such information is essential in planning development strategies and in selecting appropriate land use and  management practices for sustaining soil productivity (Lal, 2012).

The productive capacity of a soil can be evaluated directly or indirectly (Dengiz and Saglam, 2012). Direct evaluations are carried out in the field, greenhouses or laboratory by means of some  experiments  under  given climatic  and  management  conditions.  Indirect evaluations consist basically in developing and applying models of varying complexity. There is a need to conduct periodic soil erosion-productivity studies using soil productivity models of proven validity to ascertain the continued ability of a nation’s soil resources “to produce a plant or sequence of plants under a physically defined set  of management practices” and by direct implication feed the nation’s population. These indices are calculated based on selected soil properties that are easily measurable. Several models have been developed for predicting loss in soil productivity occasioned by erosion (Laflen et al., 1985; Pierce et al., 1983; Stocking and Pain, 1983; Timlin et  al., 1986; Williams  et al., 1983). Most methods for evaluating erosion impacts on productivity are based on the relationship of crop yield to soil thickness (or topsoil removed) or to productivity indices that depend upon the depth and quality of the surface  soil (Lal,  2012). The most commonly  used  methods  are (a) agronomic  methods, including natural erosion plots and yield records and desurfacing experiments; (b) geological measurements  and  rates  of weathering;  and  (c) modelling  and  productivity  indices  (Lal, 2012). The usefulness, effectiveness and application of an index of soil erosion will certainly be limited outside the region, crop, or general use for which the index was developed (László, 2008). For this reason, there is a need to carry out field trials in specific eco-regions in order to evaluate the validity and effectiveness of selected models in quantifying soil productivity with a view to establishing a scientific basis for inclusion and/or exclusion of certain factors in order to enhance their applicability to local soil conditions in specific areas.

Relatively few studies have been undertaken to validate the usefulness/reliability of methods of evaluating the effect of soil erosion on the productivity of Nigerian soils (Mbagwu et al., 1984a; Anikwe and Obi, 1999, Ngwu et al., 2007; Nwite and Obi, 2008; Agber and Anjembe, 2012). Most of these research works on soil erosion-soil productivity relationships carried out predominantly  in southeastern  Nigeria,  focused mainly on the use of a simple  numerical index model of Neill (PI) and its modifications in quantifying the relationship between plant growth and soil properties  which might be affected  by soil erosion,  using mostly cereals (maize  and  sorghum)  and  rarely  legumes  as  test  crops.  There  is,  therefore,  a  need  to investigate the effectiveness of a wide range of methods of predicting the impacts of erosion on soil productivity in southeastern Nigeria. Furthermore, a variety of local crops should be used as test crops in full cognizance of the fact that productivity is a relative term; each crop has its own soil productivity  scale,  which does not coincide with that of any other crop (Riquier  et  al.,  1970).  Soil  productivity  studies  should  also  be  carried  out  in  different locations  with  contrasting  climates  and soil types  in order to determine  the influence  of climate  and  inherent  soil  properties  on  soil  productivity  as  affected  by  soil  erosion. Information on the effects of varying degrees of topsoil removal is necessary to determine the relative importance of different topsoil thicknesses in relation to soil productivity. The main objective of this work was to evaluate the effectiveness of four methods of quantifying soil erosion-soil productivity relationship  in Nsukka and Ekwegbe,  southeastern Nigeria, using groundnut  as a test  crop.  The  methods  included  Neill’s  (1983)  productivity  index  (PI), Modified  productivity  index  (MPI),  Riquier’s  productivity  index  (RI)  and  Desurfacing technique (DT). Specific objectives of the study were to:

         determine the properties of the soils at the study locations;

            determine  the  productivity  and  potentiality  indices  of the  soils,  as  well  as  their productivity and potentiality classes;

         determine the soils’ coefficients of improvement.

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