Effect of Deficit Irrigation on Growth and Yield of Okro (Abelmoscus Esculentus)

The study was conducted to determine the effect of deficit irrigation on the growth and yield of the Dwarf Green Long Pod variety of okro (Abelmoschus esculentus). The location of the study was the School of Agriculture Research and Teaching farm of the University of Cape Coast,Cape Coast. Experimental design adopted for the study was the Randomised Complete Block Design and there were four (4) treatments which were replicated three times. Treatments one, two, three and four were the application of 100%, 80%, 70% and 60% of the amount of waterlost through evapotranspiration respectively. A daily irrigation water application was used. The study was conducted throughout the four growth stages of okro. The leaf area, number of pods per plant, pod weight, pod length and pod circumference were all measured at the various growth stages. Soil samples from the various treatment plots were analysed before and after 60 days of planting to determine the amount of nitrogen, phosphorus and potassium (N, P, and K). Similarly, moisture contents were determined before planting, at the developmental and mid stage of growth. It was observed that treatment two which was the 80% application of ETc performed better than the others. It was also observed that the 60% application of ETC gave the poorest results. It can be concluded that irrigating with 80% of estimated water requirement, is the best application for okro.Keywords: Deficit irrigation, okro, water, yield, evapotranspiratio

Using the past to constrain the future: how the palaeorecord can improve estimates of global warming

Climate sensitivity is defined as the change in global mean equilibrium temperature after a doubling of atmospheric CO2 concentration and provides a simple measure of global warming. An early estimate of climate sensitivity, 1.5-4.5{\deg}C, has changed little subsequently, including the latest assessment by the Intergovernmental Panel on Climate Change. The persistence of such large uncertainties in this simple measure casts doubt on our understanding of the mechanisms of climate change and our ability to predict the response of the climate system to future perturbations. This has motivated continued attempts to constrain the range with climate data, alone or in conjunction with models. The majority of studies use data from the instrumental period (post-1850) but recent work has made use of information about the large climate changes experienced in the geological past. In this review, we first outline approaches that estimate climate sensitivity using instrumental climate observations and then summarise attempts to use the record of climate change on geological timescales. We examine the limitations of these studies and suggest ways in which the power of the palaeoclimate record could be better used to reduce uncertainties in our predictions of climate sensitivity.Comment: The final, definitive version of this paper has been published in Progress in Physical Geography, 31(5), 2007 by SAGE Publications Ltd, All rights reserved. \c{opyright} 2007 Edwards, Crucifix and Harriso

Oracle-based optimization applied to climate model calibration

In this paper, we show how oracle-based optimization can be effectively used for the calibration of an intermediate complexity climate model. In a fully developed example, we estimate the 12 principal parameters of the C-GOLDSTEIN climate model by using an oracle- based optimization tool, Proximal-ACCPM. The oracle is a procedure that finds, for each query point, a value for the goodness-of-fit function and an evaluation of its gradient. The difficulty in the model calibration problem stems from the need to undertake costly calculations for each simulation and also from the fact that the error function used to assess the goodness-of-fit is not convex. The method converges to a Fbest fit_ estimate over 10 times faster than a comparable test using the ensemble Kalman filter. The approach is simple to implement and potentially useful in calibrating computationally demanding models based on temporal integration (simulation), for which functional derivative information is not readily available

The complexities of the coefficients of the Tutte polynomial

The complexity of calculating the coefficients of the Tutte polynomial of a graph is considered. The calculation of some coefficients is shown to be #P-complete, whereas some other coefficients can be computed in polynomial time. However, even for a hard coefficient, it can be decided in polynomial time whether it is less than a fixed constant.</p

Parameter estimation in an intermediate complexity earth system model using an ensemble Kalman filter

We describe the development of an efficient method for parameter estimation and ensemble forecasting in climate modelling. The technique is based on the ensemble Kalman filter and is several orders of magnitude more efficient than many others which have been previously used to address this problem. As well as being theoretically (near-)optimal, the method does not suffer from the 'curse of dimensionality' and can comfortably handle multivariate parameter estimation. We demonstrate the potential of this method in identical twin testing with an intermediate complexity coupled AOGCM. The model's climatology is successfully tuned via the simultaneous estimation of 12 parameters. Several minor modifications arc described by which the method was adapted to a steady state (temporally averaged) case. The method is relatively simple to implement, and with only O(50) model runs required, we believe that optimal parameter estimation is now accessible even to computationally demanding models