Alternative methods to determine plot sizes for tree crops. a case study from coconut data

Two methodologies are pooposed to determine the most efficient plot size for tree crops using data from experiments based on randomized complete block designs. Both methods can be generalized for data from any balanced design. The merits and demerits of these methods are discussed. The methods are illustrated using the data sets of long-term field ecperiments at the Coconut Research Institute, Sri Lanka. The results show that efficient plot size in field experiments for coconut for a wide range of agroecological regions is four or six palms

Use of statistical package SAS: A case study from coconut research institute of Sri Lanka

The CRI began using microcomputers for the analysis of their experimental data in 1986. Four case studies of typical sets of field experimental data from the CRI are used to illustrate the potential benefits and possible problems in moving from a manual system of data processing to one which uses a powerful statistical package

Assessment of the effects of environmental factors on yield of coconut (Cocos nucifera L.)

The long term nut yield data and climate data of eight variables (1976-1992) were analyzed to understand the effects of climate and weather on the yield variability between picks. The yield variation over the years had no systematic pattern. The order of contribution of the picks to total yield is not significantly consistent between years. Explanatory models were developed at monthly lag periods prior to harvest of each pick. The most and least influential picks in respect of climate variability are picks 5 and 2 respectively. The critical period with respect to climate and weather variability of picks 1-6 are February , June July, Septermber, December and February respectively. The climatic models fitted at these periods explain the yield variability between picks. The influence of climatic variables during these periods vary from pick to pick. Maximum air temperature and relative humidity in the afternoon are the two most significant environmental variables influencing yield irrespective of picks

Principal component regression for solving multicollinearity problem

Multicollinearity often causes a huge explanatory problem in multiple linear regression analysis. In presence of multicollinearity the ordinary least squares (OLS) estimators are inaccurately estimated. In this paper the multicollinearity was detected by using observing correlation matrix, variance influence factor (VIF), and eigenvalues of the correlation matrix. The simulation multicollinearity data were generated using MINITAB software and make comparison between methods of principal component regression (PCR) and the OLS methods. According to the results of this study, we found that PCR method facilitates to solve the multicollinearity problem

An alternative model to estimate solar radiation

Solar radiation is extremely useful in modelling many agricultural applications, but is hardly used due to the difficulty in obtaining data, and the time consuming process in estimating it by the angstrom (1924) formula which uses world geographical relationships. To estimate solar radiation at the Coconut Research institute, Lunuwila (7o 20'N;71o 53'E;30.5m) an alternative model was developed from measured sunshine hours data only. The model had good fit (R2=0.90,P0.001) and was found to have agreement with the estimates obtained from the Angstrom model. The alternative model is more flexible and useful in estimating crop evapotranspiration, and for crop-weather modelling. The mean daily solar radiation at Lunuwila was estimated to be 18.3 MJ m-2d-1 and the total annual solar radiation receipts is 6680 MJ m-2 (66.8 TJ ha-1). The monthly solar radiation was highest in March (21.7MJ m-2 d-1) and the estimated 75 per cent probability value was 22.5 June had the lowest (16.1 MJ m-2 d-1) value and the estimated 75 per cent probability value was 17.8