Robust parameter settings of evolutionary algorithms for the optimisation of agricultural systems models

Numerical optimisation methods are being more commonly applied to agricultural systems models, to identify the most profitable management strategies. The available optimisation algorithms are reviewed and compared, with literature and our studies identifying evolutionary algorithms (including genetic algorithms) as superior in this regard to simulated annealing, tabu search, hill-climbing, and direct-search methods. Results of a complex beef property optimisation, using a real-value genetic algorithm, are presented. The relative contributions of the range of operational options and parameters of this method are discussed, and general recommendations listed to assist practitioners applying evolutionary algorithms to the solution of agricultural systems. (C) 2001 Elsevier Science Ltd. All rights reserved.

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems. In this paper, we outline the basis for climate prediction, with emphasis on the El Nino-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction. In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary Knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based oil simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction. We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential. (C) 2001 Elsevier Science Ltd. All rights reserved.

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

Mannose-binding lectin gene polymorphism predicts hospital admissions for COPD infections

Infection frequently causes exacerbations of chronic obstructive pulmonary disease (COPD). Mannose-binding lectin (MBL) is a pattern-recognition receptor that assists in clearing microorganisms. Polymorphisms in the MBL2 gene reduce serum MBL levels and are associated with risk of infection. We studied whether the MBL2 codon 54 B allele affected serum MBL levels, admissions for infective exacerbation in COPD and disease susceptibility. Polymorphism frequency was determined by PCR-RFLP in 200 COPD patients and 104 smokers with normal lung function. Serum MBL was measured as mannan-binding activity in a subgroup of 82 stable COPD patients. Frequency of COPD admissions for infective exacerbation was ascertained for a 2-year period. The MBL2 codon 54 B allele reduced serum MBL in COPD patients. In keeping, patients carrying the low MBL-producing B allele had increased risk of admission for infective exacerbation (OR 4.9, P-corrected = 0.011). No association of MBL2 genotype with susceptibility to COPD was detected. In COPD, serum MBL is regulated by polymorphism at codon 54 in its encoding gene. Low MBL-producing genotypes were associated with more frequent admissions to hospital with respiratory infection, suggesting that the MBL2 gene is disease-modifying in COPD. MBL2 genotype should be explored prospectively as a prognostic marker for infection risk in COPD.

Cofilin interacts with ClC-5 and regulates albumin uptake in proximal tubule cell lines

Receptor-mediated endocytosis is a constitutive high capacity pathway for the reabsorption of proteins from the glomerular filtrate by the renal proximal tubule. ClC-5 is a voltage-gated chloride channel found in the proximal tubule where it has been shown to be essential for protein uptake, based on evidence from patients with Dent's disease and studies in ClC-5 knockout mice. To further delineate the role of ClC-5 in albumin uptake, we performed a yeast two-hybrid screen with the C-terminal tail of ClC-5 to identify any interactions of the channel with proteins involved in endocytosis. We found that the C-terminal tail of ClC-5 bound the actin depolymerizing protein, cofilin, a result that was confirmed by GST-fusion pulldown assays. In cultured proximal tubule cells, cofilin was distributed in nuclear, cytoplasmic, and microsomal fractions and co-localized with ClC-5. Phosphorylation of cofilin by overexpressing LIM kinase 1 resulted in a stabilization of the actin cytoskeleton. Phosphorylation of cofilin in two proximal tubule cell models (porcine renal proximal tubule and opossum kidney) was also accompanied by a pronounced inhibition of albumin uptake. This study identifies a novel interaction between the C-terminal tail of ClC-5 and cofilin, an actin-associated protein that is crucial in the regulation of albumin uptake by the proximal tubule.

Low level microsatellite instability may be associated with reduced cancer specific survival in sporadic stage C colorectal carcinoma

Background: Colorectal cancers (CRCs) may be categorised according to the degree of microsatellite instability (MSI) exhibited, as MSI-high (MSI-H), MSI-low (MSI-L), or microsatellite stable (MSS). MSI-H status confers a survival advantage to patients with sporadic CRC. Aims: To determine if low levels of MSI are related to the clinicopathological features and prognosis of sporadic stage C CRC. Patients: A total of 255 patients who underwent resection for sporadic stage C CRC were studied. No patient received chemotherapy. Minimum follow up was five years. Methods: DNA extracted from archival malignant and non-malignant tissue was amplified by polymerase chain reaction using a panel of 11 microsatellites. MSI-H was defined as instability at greater than or equal to40% of markers, MSS as no instability, and MSI-L as instability at >0% but,40% of markers. Patients with MSI-H CRC were excluded from analysis as they have previously been shown to have better survival. Results: Thirty three MSI-L and 176 MSS CRCs were identified. There was no difference in biological characteristics or overall survival of MSI-L compared with MSS CRC but MSI-L was associated with poorer cancer specific survival (hazard ratio 2.0 (95% confidence interval 1.1-3.6)). Conclusions: Sporadic MSI-L and MSS CRCs have comparable clinicopathological features. Further studies are required to assess the impact of MSI-L on prognosis.

CIC-5: A chloride channel with multiple roles in renal tubular albumin uptake

CIC-5 is a chloride (Cl-) channel expressed in renal tubules and is critical for normal tubular function. Loss of function nonsense or missense mutations in CIC-5 are associated with Dent's disease, a condition in which patients present with low molecular weight (LMW) proteinuria (including albuminuria), hypercalciuria and nephrolithiasis. Several key studies in CIC-5 knockout mice have shown that the proteinuria results from defective tubular reabsorption of proteins. CIC-5 is typically regarded as an intracellular Cl- channel and thus the defect in this receptor-mediated uptake pathway was initially attributed to the failure of the early endosomes to acidify correctly. CIC-5 was postulated to play a key role in transporting the Cl- ions required to compensate for the movement of H+ during endosomal acidification. However, more recent studies suggest additional roles for CIC-5 in the endocytosis of albumin. CIC-5 is now known to be expressed at low levels at the cell surface and appears to be a key component in the assembly of the macromolecular complex involved in protein endocytosis. Furthermore, mutations in CIC-5 affect the trafficking of v-H+-ATPase and result in decreased expression of the albumin receptor megalin/cubulin. Thus, the expression of CIC-5 at the cell surface as well as its presence in endosomes appears to be essential for normal protein uptake by the renal proximal tubule. (c) 2005 Elsevier Ltd. All rights reserved.

Validation of multicriteria analysis models

Validation procedures play an important role in establishing the credibility of models, improving their relevance and acceptability. This article reviews the testing of models relevant to environmental and natural resource management with particular emphasis on models used in multicriteria analysis (MCA). Validation efforts for a model used in a MCA catchment management study in North Queensland, Australia, are presented. Determination of face validity is found to be a useful approach in evaluating this model, and sensitivity analysis is useful in checking the stability of the model. (C) 2000 Elsevier Science Ltd. All rights reserved.