Conceptualization of Info- Crop Oil Palm Nutrient Modeling In Nigerian Institute for Oil Palm Research

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1 Greener Journal of Agricultural Sciences ISSN: ; ICV: 6.15 Vol. 2(8), pp , December, 2012 Copyright 2017, the copyright of this article is retained by the author(s) Research Article Conceptualization of Info- Crop Oil Palm Nutrient Modeling In Nigerian Institute for Oil Palm Research *Okpamen U. S., Uwumarongie-Ilori E.G., Okere R.A., Imoisi O.B. and Okolo E. C. Nigeria Institute for Oil Palm Research, NIFOR, P.M.B Benin City, Edo State ARTICLE INFO ABSTRACT Article No.: DOI: /GJAS Submitted: 09/11/2012 Accepted: 21/11/2012 Published: 30/12/2012 *Corresponding Author Okpamen U. S. Keywords: Info Crop, Info Nutrient -modeling, Regression coefficients, efficiency, agronomic experiment and validation Following the expensive cost in running fertilizer trials, it is increasingly difficult to fund most agronomical projects and in order to reduce cost, save available time and improve on existing data information pool, the idea of info crop-oil palm nutrient sourcing, processing and transformation into a require models is now an issue in oil palm industry. This work review areas of interest done by Universities and Research institutions in Nigeria on the growth and development of oil palm. It represents advancement in the Info-Crop oil palm nutrient modeling and its application to oil palm (Elaeis guineensis L.) growing in diverse tropical environments. The model is based on the regression analysis of oil palm nutrient data computed to predict various crop nutrients, especially the oil palm in tropical and subtropical regions. The Info Crop-oil palm nutrient models were validated with data compiled from approved studies, comprising agronomical and nutritional experiments conducted in Nigeria (NIFOR) and Malaysia (MPOB) between 2001 and 2008 in diverse locations. The treatments included nutrients and varieties of oil palm. Time of first harvest varied between 3 and 4 years, leaf production varied from 8 to 15 fronds year -1 and yield ranged from 9000 to 18,465kg wt ha -1 year -1. The regression coefficients used for validation were generated from field experiments conducted during the same period. Model validation performance was by using SPSS statistical package with known conventional procedures. Parameters evaluated were soil nutrients and climatic elements of the oil palm belts, its zoning and yield output performances across fields of the main station. It was established that field measurements had an experimental error of 10-15% and wide variation existed within treatments. The models reported in the final results adequately explained the effects of nutrients applied and agro-climatic conditions at varying periods for only the agro-climatic zone investigated, simulated potential yields varied from 16 to 24,000kg wt ha -1 year -1 and between 4 to16 bunches per palm stand (Nigeria) and 6 to 19 bunches per palm stand (Malaysia), depending on environmental and managerial influences. We established therefore that info Crop oil palm nutrient modeling could be used to increase the performances of agronomic experiments for rapid prediction of oil palm nutrient requirements and yields across the zones when properly developed and adopted in fields and plantations.

2 Okpamen et al / Greener Journal of Agricultural Sciences 425 INTRODUCTION Info Crop oil palm nutrient modeling, involves modeling nutrient demand, nutrient status of soils and losses in an altogether bases in order to generate a nutrient recovery or recycling model in the oil palm industry (Henson, 2007). It is a mathematical crop nutrient technology in oil palm nutrition and development. The major factors influencing growth and yield of oil palm in fields depends on the rate and content of nutrient uptake by the palm and the amount of fertilizers that is being applied to the soil. Recommended rate of application of fertilizers is pivotal to sustainable growth of oil palm and to guarantee optimal productivity of the crop. These factors are assumed to be responsible for the high productivity of Oil palm world over, apart from the environmental variations. There had not been any straightforward factor(s) that is solely responsible for both growth and yield performance in Oil palm rather a conglomeration of factors. Also, complex interaction of which results in the plant response to nutrient uptake, utilization and output differences cross -established fields. However, it was agreed that, most responses by plants especially oil palm are restrictive and site specific (Kee et al., 2000). In an oil palm plantation where output have been noticeably high, it was observed that such plantation was established based on assessment of fertilizer requirement of the palms, the relationship between field trials and fertilizer application and yield, this form an index to most oil palm fields and plantations in Nigeria were the cultivation of the crop is solely rain-fed. This justifies the impact of soil and climatic effects on the nutrient adequacy and inadequacy that affects nutrient recycling within the oil palm fields in Nigeria wherein NIFOR is a case study. Most trials are site-specific also, because oil palm growth and yield from history have been greatly tailored to climatic influences on the crop. And as such fertilizer trials are becoming very expensive to run, projects are hardly funded due to these reasons, hence data sourcing for transformation into require models become a better option in the industry. This approach will assist in simulating effects of specific nutrient in relation to the crop output performances and enhanced effective field /plantation management. The need to consider the impact of climatic elements on soils supporting growth and yield of oil palm is born out of the present dependence of the crop on weather elements and critical records of recent declining yields being reported by oil palm growers in Nigeria (Omoti et al.,1983), due to leaching as deep drainage DD. Foong (1993) reported an irregular pattern of these factors with palm age in soils of Malaysia, and also compared deep drainage DD losses as a percentage of rainfall at different sites and plantations adding that the impact of climate change is likely to become the major factor of nutrient loss in oil palm management. METHODOLOGY AND DATA TRANSFORMATION This study is concerned with sourcing, assembling, correlating and transforming of data from previous and current agronomical-nutritional experiments as it affects oil palm that were done in NIFOR Main Station, Universities and other centers of research in Nigeria. Concept of Factors and Variables in Modeling Experiments There are two major classes of models which are talked about and have common similarities. These are experimental designed model and regression models. The similarities in these models are that all the models assumed data representing output variables for observed yield. Yield can be expressed as the sum of the systematic component and the random component, the systematic component represents the detailed of a major variation between yields or any other dependent variable. The random component represents the uncontrolled inherent variation between observed experimental units see below; Systematic random Y = bi + tj + eij Y = b0 + b1x1 + b2x2 + b3x3 + eij Other similarities included in the systematic component are composed of several subcomponents added together. It also consists of an error term that is normally distributed and the random variation of y about the systematic component value that does not affect the systematic component of the model with respect to experimental data. The identified differences are that in experimentally designed models, the systematic variation is denoted as factors which may assume different levels or set of possible levels like those to be discussed latter. This implies a case where y observation included one level from each factor, the block factor will consist of blocks such as 1, 2, 3 ) with each observation occurring in one block. Therefore if a particular block is being considered for unstructured set of treatments, the treatment factor would comprise treatment 1, treatment 2 ) this will all contribute one level to the systematic component of each observation. If the effect of each treatment is described in terms of several factors, their combine effects is represented by a

3 426 Okpamen et al / Greener Journal of Agricultural Sciences combination of main effect factors and interaction factors. A fertilizer trial involving K and Mg application in six levels and repeated for five years on an oil palm field is sited as an instance, the numbers of observations will be 30 (thirty). It is a 2x6 factorial block designed experiment with its observation modeled as follows; Blocks = five(b1, b2, b3, b4, b5 ) Fertilizers = two = K and Mg = 2F Levels = six = f1, f2, f3, f4, f5, f6 Interaction effect = (2F x f1, f2, f3, f4, f5, f6) = (Ff) where Ff = fertilizer x levels, Model applicable would be 1) Y1 = b1+ F1 + f1 + (Ff) ij + eij 2) Y2 = b2 +F2 +f2 + (Ff) ij +eij 3) Y3 = b3 +F3 +f3 + (Ff) ij +eij N Yn = bn +Fn +fn + (Ff) ij +eij Where N = numbers of observations or experimental units. From each observation the model applies to all the experimental units, the systematic component of the model includes four terms, using a total of 31 factors which are also parameters to describe the systematic components of the model. Now for tables drawn below indicating the influences of fertilizers on yield performance of oil palm in NIFOR will be assessed with coefficients of correlation in latter discourse with y as the dependent variable and expected yield from palm(s) per stand or per hectare yr -1, it could also mean numbers of expected harvest of FFB per stand or per hectare yr- 1 (Table 1). Concept of modeling Several Factors and Variables This concept accommodates the use of factors and variables wherein the systematic components are factors and variables. This implying that we can investigate a particular factor as treatment along with others as variables such as study on land reclamation management investigated along with rainfall intensity, altitude and temperature rise. For this type of experiment, a model could be postulated to represents all components. Y ij = a + b1r ij + b2t ij + b3a ij + eij model. Where we assume the Yij to represents different management method of land reclamation. The sum effect of all the components of the above model could be a representation of this complete model. From the model we can assess the impact of individual factors on a major factor such as irrigation management, growth rate, and yield performances on either unit variable or variables. Other factors being variables fitted into the model as subset of the main term or factor. In Table 1, the use of a single sets of data as factor x and y were used for the linear regression model. In most field experiments, it is usually uncommon to have only a single set of data used in fertilizer trial experiment, what is common is the comparison of two or more fertilizers being tested on a main factor. The emphasis here is fitting several factors relationship to simulate a research finding or a predictive model which is achievable when there are evidences of replication or repeatability which is among the aims of additional components in linear regression model. The tables below are used to evaluate conceptual linear regression model. RESULTS AND DISCUSSION TABLE 1: Influence of Mg and K - fertilizer on fresh fruit bunch numbers and weights in NIFOR (main station, 148palms/ha. Ages 6-15yrs) No. of K-level Mg-level (Ave FFB No/ha) Single Ave FFB Ave. FFB Wt/ha Obs. Wt/palm , , , ,122 Source; NIFOR Annual Report, Nigeria; Obs - observations, Ave - average, Wt - weight; ha - hectare, FFB - fresh fruit bunch

4 Okpamen et al / Greener Journal of Agricultural Sciences 427 The Table (1) represents experiment conducted in the institute main station between 2001and It was on the influence of Mg fertilizer application and K and Mg application on fresh fruit bunch Numbers per palm and bunch weight per hectare of oil palm. A total of 148 palms per hectare were planted. Magnesium fertilizer was applied in kilograms at four (4) levels and each was replicated three times for a period of six years making a total of twelve (12) treatments and the mean or average weight of the fresh fruit bunches and numbers were determined and recorded as fresh fruit bunch weight and single average palm bunch number per hectare. Table 1 above is a condensed data for biometric parameters and record of computed results below. The correlation of single fertilizer treatment with oil palm bunch number was a positive one at α = 0.05, r = 0.982* with bunch numbers rising from between 4 to 24 with average bunch weight of kg wt palm -1 with age ranging from 6 to 15yrs (Table 1). FFB yield (kg/hectare) y = 0.427x R 2 = Amount of Mg (kg) Fig 1: Effect of single Mg fertilizer application on FFB yield/ hectare In Fig 1, there is a significant positive correlation of numbers of FFB harvested per palm with the magnesium fertilizer application rate of 0.0, 0.8, 1.7, 2.6 and 3.0 kg. The result implies that there is a linear comparison between Mg levels applied singly to the soil and FFB numbers harvested justifying the possibility of the regression model as a predictor or simulator for Fresh fruit bunch numbers. Similarly, the combined used of K +Mg (Fig 2) wherein magnesium was applied at the same rate as in the single application and K at 0.0, 1.5, 2.5, 3.5 and 4.5 respectively produced a positively response at α = 0.05, r = 0.989** under the same ecological condition y = 1376x R 2 = 1 FFB yield (kg/hectare) Amount of Mg + K (kg) Fig 2: Effect of combined fertilizer application of Mg & K on FFB yield/hectare

5 428 Okpamen et al / Greener Journal of Agricultural Sciences Concept of Non Linear Regression When the relationship among variables under consideration is not linear, the linear regression analysis procedures would become inadequate and a researcher must have to turn to non -linear regression analysis procedures. According to Alika (1997), there are numerous functional procedures or forms that can be used to describe a linear relationship between two variables and choice of the appropriate regression and correlation techniques, depending on the functional form involved. The non- linear form can be linearized through variables transformation or by creation of new variables. Use of the Model and Simulation: Table 1 represents the influence of Mg fertilizer on Single fresh fruit bunch in NIFOR main station. The early application of Mg fertilizers in the experiment did not contribute effectively to increased number of bunches harvested in the first two year of application. Oil palm bunch number and weight did not correlate significantly with fertilizer application at 5% probability, r = , wherein the mean FFB weight was 15,857kg wt/ha and standard deviation of and mean Mg application per hectare was 1.667kg/ha. The third session of application indicated some increase effect by continuous application of the element to the fourth, fifth and sixth session respectively; this gave a responsive effect on the palms as numbers of bunches increased tremendously with an annual application of 1,66kg Mg/ha increasing the fresh fruit bunch (FFB) yield number as depicted by the computed regression equation. The modeling of Mg rate and K jointly was applicable to effective increases in bunch numbers only when the rate of application reaches approximately to1.66kg (Mg + K) /ha for magnesium and 4.5kg for Potassium. This is with respect to Table 1 results wherein magnesium and K was applied as joint application required for correcting a deficiency symptom and to effects an increase in oil palm bunch numbers. This model is useful to the extent that it could be use to predict future increases in oil palm plantation given the condition that a bench-mark of (1.66kg Mg + 4.5kg K) ha -1 for magnesium element is not exceeded for optimal fertilizer rate for satisfactory increase number of oil palm bunch per hectare provided environmental and managerial practices are maintained. TABLE 2: Physical and Chemical Factors of Soils of Coastal Plain Sand Parent Material (Gene Pool 1, uncultivated portion of the field). Depth Sand Silt Clay ph Org. C Tot. N BS Av.P Ea Na K Mg Ca ECEC C; N cm gkg-1 (H2O)1;1 gkg-1 % mgkg-1 cmolkg I MEAN ECEC - effective cation exchangeable capacity. BS - base saturation %, Org. C - organic carbon, Av. P - Available phosphorous Ea - exchangeable acidity, (Courtesy; Okpamen, et al. 2011). Predicting outcomes of fertilizer response in any given field has not been without conducting series of fertilizer trials, the question is: are these trials all the time necessary? The amount of any single or compound fertilizer required to correct a deficiency problem depends on basic environmental factors that are fundamentally soil related and climate specific. (Henson, 2007) To be able to ascertain how much or less the individual palm takes in or losses within a field per season would demand on the knowledge of the right quantification of the applied nutrients. In a similar trial K application to the soil gave a tremendous increase in both numbers of bunches and weight of FFB yield, kg wt per palm to kg wt per palm and an hectare increment of 16 tons to 23 tons. This increment could neither be correlated with magnesium nor potassium at the first and second session of application because numbers and weights recorded were poorly significant at ( (Okomu-trials, ), while in latter session of application there was high significant correlation between K application against Mg application (r =0.989** α=0.05) which had been reflected in the increase above. From Table 2 the computed parameters indicating the physico-chemical properties of soil of NIFOR main station were subjected to a regression analysis. The silt fraction was correlated with ECEC and showed no significant statistical correlation, implying that the amount of cation held in the highly silted regions of the pit analyzed was not dependent on silt fraction hence could not be related (r = , α =0.05). The predictor used in the statistics might not be a good simulator hence silt proportion can be an index for the determination of ECEC; the Organic carbon was also correlated with the ECEC (r= α = 0.05). It could be said that the amount of nutrient element held in the

6 Okpamen et al / Greener Journal of Agricultural Sciences 429 soil is partly dependent on the value of ECEC of the soil. Another notable factor of the soil to be considered is the impact of silt to clay ratios of the soil; this was determined by the extent of clay micelles emerging from the weathered parent material of the soil in the location. Another is the correlation of depth with clay content of the soil, it revealed a significant positive correlation at α =0.05, r = 0.979, the implication of which is that clay content can be used to predict depth of the soil nutrient in the field examined if x should represent any value of clay y = 0.409x R 2 = 1 Depth (cm) Clay (g/kg) Fig 3: Comparison of Depth with Clay content of the Soil From the above (Fig 3), the modeling of depth and Clay content is actually a possible mathematical simulating model having been tested and validated from Table 2 above; the simple linear regression model has established a seemly linearity between Clay parameter and depths of profile as far as this work is concerned. TABLE 3: RELATIONSHIP OF SOME SOIL PROPERTIES WITH FFB OF OILPALM HARVESTED FROM FIELDS WITHIN THE INSTITUTE (Field 31, 35 and 14) Fields K/ECEC C : N Yield(kg wt/ha) Soil ph Exch. Ca Exch. K Tot N% Bunch No/palm , , , K/ECEC; Potassium - Effective cation ratio; C:N Carbon, Nitrogen ratios; Tot N% - total nitrogen % From Table 3, when yield of fresh fruit bunch of Oil palm was correlated with K : ECEC there was not significant correlation; this implies that the K:ECEC is not a reasonable predictor for yield determination in the respective fields examined at (α =0.05,r = 0.184). TABLE 4: COMPARISON OF CLIMATIC ELEMENTS WITH SOIL REACTION, NUTRIENT APPLICATION AND FFB YIELD OF OIL PALM IN SOME FIELDS IN NIFOR MAIN STATION. Soil ph K(kg) Mg (kg) FFB(Kg Wt/ha) Rainfall (mm) Sun/s R/H% Temp. 0 C Solar R , , , ,

7 430 Okpamen et al / Greener Journal of Agricultural Sciences K; potassium, Mg - magnesium, FFB - Fresh fruit bunch, Wt/ha - weight per hectare, /s shine, R/H -relative humidity, 0 C - degree Celsius, R - radiation. In a similar manner, yield also was correlated with C: N ratios across the various field investigated and was found to be positively significant at α = 0.05, r = 0.931, the implication is that C: N ratios of the selected fields examined could be use as a reasonable predictor of yield performance from the three fields on Table 3. In Table 4, there is the comparison of soil parameter, soil ph with several others that are mainly weather influences except the fertilizer application rates and yield records. This table has been carefully formulated and processed for a result it is four- year experimental trial result done in the Institute. Our regression modeling could not give an all- embracing model that could represent the variable on the above table but have only succeeded in individualizing each relationship as it affects the various parameters. Soil ph correlated with K above with r = 0.995* at 5% probability. It does so with FFB harvested r =0,165, with relative humidity negatively, r = , positively with solar radiation and rainfall r =0.645 and 0.899*. The relationship of this weather elements and soil reactions was assessed with the correlation efficient, so was the fertilizer and yield of FFB relationship. Yield harvested correlated poorly with fertilizers (Mg) and (K + Mg) r = and 0.268, temperature r= and relative humidity, r = with all the parameter significant at α = Three major parameters influenced yield of FFB harvested these were; rainfall r = , sunshine, r = 0.887, and solar radiation, r = at (α =0.05). Significance of Multiple Regressions Modeling in Research Experiments Due to errors that may arise from individual or single application and prediction, an attempt is made to expand the modeling into several non -linear or multiple application or prediction. This will help take care of the incidence of antagonism of one element with another. It will help solve problem of joint application which is usually the case in oil palm fields and plantations, especially for elements such as potassium and magnesium. Others are micronutrients and general problems of nutrient interaction in the soils. The successful application of multiple regression analysis for data statistically processed and transformed field results would graduate into a possible research experimental model adequate for prediction in plantations with similar environmental condition specific to it (soil and climatic factors). This first edition of this publication has considered fertilizer trials and crop response for experiments conducted in the NIFOR main station depicting the southern Nigeria soils where oil palm and other palms such as Coconut and Raphia are major economic crops cultivated. In kick starting an important project of this magnitude, it is very important to explain the basic terms associated with the project. Evaluation of nutrient requirement of any crop plant would require knowledge of nutrient composition of such plant; sometimes this will include study on the mean tissue nutrient concentration and total increase in biomass of the plant (Corley and Tinker, 2003 Goh, 2005). In considering these two aspects in the oil palm, it will interest us to know that the nutrient requirement of oil palm is born out of the deficiency demand that is created by the growth demand exercised from the emerging growth parameters of the plant, such as the roots and the shoot. The growth of oil palm is multi-dimensional and restrictive to the formation of either new or preexisting biomass which develops from the reservoir of nutrient bank in both the plant and the soil. In the plant it is known as mean nutrient concentration of the plant while in the soil it exists in both exchange sites and in soil solution, data reviewed by Foster (2003) indicates that optimum nutrient concentration will differ depending on soil type from which areas the plant is assume to source its nutrients. The bio-physiological limitations existing between plant roots and soil nutrient will be discussed in the next issue of this publication. RECOMMENDATION This work had utilized a common statistics to validate most of the conceptualized model which have been well appropriated in the SPSS statistical packages version not withstanding. A linear regression modeling was applied in most of the experiments investigated. It is my advice that where results are not significantly represented, a conclusive model have not been arrived at, as such regression equation could not be assumed as appropriate for a prediction purpose. In this work therefore, a few prediction models have been worked out for further validation and improvement. CONCLUSION In summary, the study has applied to a reasonable extent the statistical packages on linear regression modeling in nutrients and yields of the oil palm relative to some basic climatic variables. To investigate this topic on info-crop nutrient data processing that will eventually graduate into possible experimental models in the future in the industry would require more literatures on infocrop modeling. However, the measures of growth parameters and the amount of nutrient gained in form of nutrient uptake and quantity lost in synthesis of new biomass and maintenance of old ones have not been covered. Hence individual and group consideration of data is basic for a satisfactory statistics in info crop and nutrient analysis in oil palm management. This study has therefore initiated an important aspect of nutrient data assemblage and transformation in oil palm fertilizer trials and also dealt with the processing of some results, modeling same with yield outputs in such experiments.

8 Okpamen et al / Greener Journal of Agricultural Sciences 431 ACKNOWLEDGEMENT We acknowledge the inputs of the following persons, Professor J.E. Alika, Dr. Mudugu both of the University of Benin, Faculty of Agriculture. We wish to also express our appreciation to Mr. D. Oseghe (Researcher) of the Nigeria Institute for Oil Palm Research Benin-city, Nigeria. REFERENCES Alika JE (1997). Statistics and Research Methods Ambik Press 1 st edition 59pp. Ataga DO (1976). Twelfth Annual Report. Nigerian Institute for Oil Palm Research, Corley RHV, Tinker PB (2003). The Oil Palm. 4 th edition.blackwell Science Oxford, U.K. 562pp. Corley RHV, Breure CJ (1981). Measurements in Oil Palm Experiments Internal Report Unilever Plantations Group, London. 21pp. Foong SF (1993). Potential evapo-transpiration, potential yield and leaching losses of Oil palm. Proc. of oil palm. Proc. of the 1991 PORIM International Palm Oil Conference - Agriculture Conference. PORIM, Bangi, p Forde, St CM (1968).The Dynamics of Soil Potassium in Relation to the nutrition of Oil palm(elaesis guineensis Jacq) PhD Thesis, University of Ibadan, 167p. Foster HL (2003). Assessment of oil palm fertilizer requirements. Oil palm Management for large and Sustainable yield (T. Fairhurst and R. Hardtereds). Potash and Phosphate Institute /Phosphate Institute of Canada and International Potash Institute, p Goh KJ (1992). A model of Nutrient Balance and Dry Matter Allocation of Oil Palm in Malaysia. M.Sc. Thesis University of York (Quoted by Ng et al., 1999). Henson IE (2007). Modeling oil pal yield based on source and Sink. Oil palm Bulletin No 54: Kee KK, Goh KL, Chew PS (2000). Water cycling and balance in mature oil palm Agro-ecosystem in Malaysia. Proc. of the International Planters Conference, May2000, Okpamen SU, Uzu FO, Aghimien AE (2011). Fractionation of Magnesium in Soils of Rhodic paluedult, Journal of Physical Sciences, 297 (2011). Africa Research Development Network,297/2011. Okpamen, S.U. (2009). Forms of Magnesium in Soils developed in four Parent Materials in Edo and Delta States of Nigeria, M. Sc. Thesis University of Benin, Benin City, Nigeria, 91p.