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Spatial Statistics of Poultry Production in Anambra State of Nigeria: a Preliminary for Bio-Energy Plant Location Modelling

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Spatial Statistics of Poultry Production in Anambra State of Nigeria: a Preliminary for Bio-Energy Plant Location Modelling Nigerian Journal of Technology (NIJOTECH) Vol. 35, No. 4, October 2016, pp. 940 – 948 Copyright© Faculty of Engineering, University of Nigeria, Nsukka, Print ISSN: 0331-8443, Electronic ISSN: 2467-8821 www.nijotech.com http://dx.doi.org/10.4314/njt.v35i4.32 SPATIAL STATISTICS OF POULTRY PRODUCTION IN ANAMBRA STATE OF NIGERIA: A PRELIMINARY FOR BIO-ENERGY PLANT LOCATION MODELLING E. C. Chukwuma1,*, L. C. Orakwe2, D. C. Anizoba3 and A. I. Manumehe4 1,2,3 DEPT. OF AGRIC. & BIORESOURCES ENGINEERING NNAMDI AZIKIWE UNIVERSITY, AWKA, ANAMBRA STATE, NIGERIA 4 NATIONAL ENVIRONMENTAL STANDARDS & REGULATIONS ENFORCEMENT AGENCY, AWKA, ANAMBRA STATE, NIGERIA E-mail addresses: 1 [email protected], 2 [email protected], 3 [email protected], 4 [email protected] ABSTRACT Consequent on the need to utilize bio-wastes for energy generation, a preliminary study for bio-energy plant location modelling was carried out in this work using spatial statistics technique in Anambra State of Nigeria as a case study. Spatial statistics toolbox in ArcGIS was used to generate point density map which reveal the regional patterns of biomass distribution and to generate hotspot analysis of zones of poultry production sites in the study area. The result of the study indicates that the central regions of the state is characterised with high point density poultry production sites, some of the locations with high point density poultry production values include: Ogbaru town which has the highest point density value of above 4,362,480kg of poultry droppings. This is followed by Umuchu, Onitsha, Nise, Nibo and Amawbia, with point density value that ranges from 2,210,250kg to 4,362,480kg. Several hotspots were identified in the central region of the study area, and in the South west region of the study area (where Umuodu, Ogbakuba and Ossomala were located), while the Southern region of the study area is characterized with several coldspots. The hotspots zones and high point density production sites identified in this study should serve as suitable locations for siting bio-energy plants in the study area. Key Word: Spatial Statistics, Poultry Production, Bio-energy Plant, Livestock Waste, Anambra State. 1. INTRODUCTION non-spatial (traditional) statistics in terms of concepts With the rapid advancement of computer-based and objectives, spatial statistics are unique in that techniques for exploring, visualizing, and analyzing they were developed specifically for use with spatial features, it has become expedient that proper geographic data. Unlike traditional non-spatial site suitability analysis should be undertaking using statistical methods, they incorporate space (proximity, GIS technology in plant siting location decisions. area, connectivity, and/or other spatial relationships) Research indicates that certain areas are more prone directly into their mathematics. Spatial statistics also to higher concentrations of biomass[1, 2,3].Such helps to determine if features are random, clustered, areas are widely labelled as ‘hot spots’, and are often or evenly dispersed across any study area such as targets for siting bio-energy plants for electric power biomass generating sites. It helps in making decisions generation and utilization [4, 5]. Since biomass with higher level of confidence. Spatial statistics resources are essentially transported from certain comprises of a set of techniques for describing and points of generation to processing point, the modeling spatial data. In many ways they extend what identification of hot spots would be helpful in the mind and eyes do, intuitively, to assess spatial reduction of transportation cost and hence patterns, distributions, trends, processes and production cost. relationships. Spatial statistical techniques actually Spatial Statistics is a method of analyzing spatial use space – area, length, proximity, orientation, or distributions, patterns, processes, and relationships. spatial relationships – directly in their mathematics While there may be similarities between spatial and [6]. * Corresponding author, tel: +234-803-841-0820 SPATIAL STATISTICS OF POULTRY PRODUCTION IN ANAMBRA STATE OF NIGERIA… E. C. Chukwuma, et al Spatial statistics method has been used for various plants in the study area. purposes such as groundwater potential quality mapping and determination of spatial distribution of 2. MATERIALS AND METHODOLOGY major and minor ions present in the water and has 2.1 The Study Area been applied also in diversified applications in Anambra State is one of the 36 states of Nigeria, medical diagnosis, geology and other fields. Maps and is located in the South East geopolitical zone of generated are used as preliminary screening tools for Nigeria. Anambra State occupies a land mass of policy and decision-making in groundwater about 4,844 square kilometre and is bounded in the management strategies on a regional scale [7]. East by Enugu State, in the North by Kogi State, in the Höhn et al., [5] used spatial statistics to generate South by Rivers and Imo States, and in the West by kernel density maps to point out the regional patterns Delta State. The national population census of 2006 of biomass distribution in Southern Finland, the gave the population of the State as 4.06 million with researchers also used hotspot analysis to identify a population density of 1,500 to 2,000 persons living areas with high availability of agricultural resources in within every square kilometer. The State is divided the study area which were located majorly in the rural into 21 local government areas with Awka serving as areas. Based on these preliminary spatial analyses, the its state capital. It has equatorial type of climate with suitable candidate sites for bio-energy plant were two main seasons viz: the rainy season which is often derived by the researchers. Hence the objective of this characterized by heavy storm and lasts from April to study is to use spatial statistics to generate maps that October and dry season that last from November to could be used as preliminary screening tools for March. Figure 1 below shows map of Nigeria, and the decision-making in location modelling of bio-energy study area in hash mark. Figure 1: Map of Nigeria showing Anambra State. Nigerian Journal of Technology Vol. 35, No. 4, October 2016 941 SPATIAL STATISTICS OF POULTRY PRODUCTION IN ANAMBRA STATE OF NIGERIA… E. C. Chukwuma, et al 2.2 Data Collection, Processing and Analysis Data of poultry production in the State was collected ∑ ∑ − ) ∑ ∑ ) 1) from Veterinary Department, Anambra State Ministry of Agriculture and Rural Development Awka. In (1), the numerator is the covariance term with i and Estimates of the manure excreted by 1000 birds per j (two areal units), x is the data value in each unit, day (based on average daily live weights during the the overall value of x, and Wij is the proximity of birds’ production cycle) are approximately 120 kg for location between point i and j. By calculating the layer chickens, 80 kg for meat chickens, 200 to 350 kg product of the unit’s differences from x, the extent to for turkeys (grower females and grower heavy males, which they vary together is determined. The product respectively), and 150 kg for ducks [8]. is positive, if both xi and xj lie on the same side (above Extrapolations were calculated to give general or below) of the mean. It is negative, if the sides they estimates for the manure generated with the are positioned are different, and the value depends on corresponding number of birds for the various farm the difference from the overall value to the unit’s in the state. About 2000 poultry sites located in the values. These covariance terms are multiplied with Wij state were analysed. The data of the yearly bio-wastes which switches each possible covariance on or off depending on generated in various towns and villages of the study area and the geographical coordinate point is shown 1 ∑ − ) 2) in the Appendix. Global Positioning System (GPS) receiver was used in field survey to determine the geographical co-ordinate of these poultry farms for 2.5 Local Moran I data analysis in the study. The Spatial Statistics The other type of statistics tools used in analyzing toolbox in ArcGIS version 10 was leveraged on in patterns are categorized as Local Calculations. These spatial analysis and operations in the study. The calculations identify the extent and locations of spatial statistics used in this study are outlined clustering. The Local Moran’s I index [11] can be below: expressed as: − 2.3 Spatial Autocorrelation Analyses ) ∑ [ − )] ) o CMRC [9] study indicated that a good approach for , detecting hot spots is tests of spatial Where is the mean value of z with the sample autocorrelation. Moran’s I is a commonly used number of n; is the value of the variable at location i; indicator of spatial autocorrelation. In this study, is the value at other locations where j≠i) o is the global Moran’s I [10] was used as the first measure of variance of z; and is a distance weighting between spatial autocorrelation. Its values range from −1 to 1. and , which can be defined as the inverse of the The value “1” means perfect positive spatial distance. The weight can also be determined using autocorrelation (high values or low values cluster a distance band: samples within a distance band are together), while “−1” suggests perfect negative given the same weight, while those outside the spatial autocorrelation (a checkerboard pattern), and distance band are given the weight of 0 [12]. “0” implies perfect spatial randomness [11]. 2.6 Hot Spot Spatial Statistic Analysis 2.4 Global Moran I One of the tools in the Mapping Clusters toolset is This is the first approach in spatial auto correction, called the Hot Spot Analysis Getis Ord Gi* statistic, and global calculations identifies the overall patterns or it can be used to delineate clusters of features with trends in the data.
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