Expert systems modeling for assessing climate change impacts and adaptation in agricultural systems at regional level

Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. In this context, this paper describes a research methodology for assessing potential climate change impacts on, and formulating adaptation options for, agriculture at regional level. The methodology was developed and applied in the analysis of climate change impacts on key horticultural commodities—pome fruits (apples and pears), stone fruits (peaches and nectarines) and wine grapes—in the Goulburn Broken catchment management region, State of Victoria, Australia. Core components of the methodology are mathematical models that enable to spatially represent the degree of biophysical land suitability for the growth of agricultural commodities in the region of interest given current and future climatic conditions. The methodology provides a sound analytic approach to 1) recognise regions under threat of declines in agricultural production due to unfolding climatic changes; 2) identify alternative agricultural systems better adapted to likely future climatic conditions and 3) investigate incremental and transformational adaptation actions to improve the problem situations that are being created by climate change.

A discrete simulation approach to spatial allocation of commodity production for revenue optimisation over a local region

A simulation approach is described for the spatial allocation of crops across a region in order to maximise total revenue. The model uses inputs from GIS-based land suitability analysis to provide data on yields for a range of commodities, where the land suitability for the crops can be determined by either biophysical models or multi-criteria analysis. The objective of the study was to gain some indication of the magnitude of improvement possible in revenue, based on the convergence results for the optimisation (subject to estimated production quantities and market prices). The basic structure of the model allows for scaling up to larger problems with additional inputs and finer cell resolution. The software produces a visualisation of crop spatial allocation across the region and is compatible with statistical uncertainty analysis. The results of model simulations revealed a significant increase in revenue is possible using this approach and, when projected over the full region, suggests the possibility of significant economic benefits.

Adaptation to climate change in regional Australia : a decision-making framework for modelling policy for rural production

A decision-making framework was developed and applied in regional Australia to identify adaptation issues arising in agricultural systems and rural production as a consequence of climate change. Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and rainfall projected. An advantage of the framework is that it provides a suite of tools to aid in the formulation of strategies for sustainable regional development and adaptation. The decision-making framework uses a participatory approach that integrates land suitability analysis with uncertainty analysis and spatial optimisation to determine optimal agricultural land use (at a regional scale) for current and possible future climatic conditions. It thus provides a robust analytic approach to (i) recognise regions under threat of productivity declines, (ii) identify alternative cropping systems better adapted to likely future climatic conditions and (iii) investigate policy actions to improve the sub-optimal situations created by climate change. The decision-making framework and its methods were applied in a case study of the South West Region of Victoria.

A modelling framework for optimisation of commodity production by minimising the impact of climate change

The agricultural sector is vulnerable to the impact of climate change due to decreasing rainfall, increasing temperature, and the frequency of extreme weather events. A modelling framework was developed and applied to identify issues, problems and opportunities arising in regional agricultural systems as a consequence of climate change. This integrated framework blends together land suitability analysis, uncertainty analysis and an optimisation approach to establish optimal agricultural land-use patterns on a regional scale for current and possible future climate scenarios. The framework can also be used to identify (i) regions under threat of productivity decline, and (ii) alternative crops and their locations that can cope better with changing climate. The methods and contents of the framework are presented by means of a case study developed in the South West Region of Victoria, Australia. The results can be used to assess land suitability in support of optimised crop allocations across a local region, and to underpin the development of a regional adaptation policy framework designed to reduce the vulnerability of the agriculture sector to the impacts of climate change.

Application of GIS-based computer modelling to planning for adaption to climate change in rural areas

A GIS-based computer modelling methodology was developed and applied to identify climate change adaptation issues arising in regional agricultural production systems (including forestry). Agricultural production in Australia is very susceptible to the adverse impacts of climate change due to projected shifts in rainfall and temperature. The methodology integrates land suitability analysis with uncertainty analysis and spatial (regional) optimisation to determine optimal agricultural land use at a regional scale for current and possible future climatic conditions. The approach can be used to recognise regions under threat of productivity decline, identify alternative cropping systems that may be better adapted to likely future conditions, and investigate implementation actions to improve the sub-optimal situations created by climate change. An example of how the methodology may be used is outlined through a case study involving the South West Region of Victoria, Australia. The case study provides information on the tools available to support the formulation of a regional adaptation strategy.

An integrated approach to supporting land-use decisions in site redevelopment for urban renewal in Hong Kong

Urban renewal is a significant issue in developed urban areas, with a particular problem for urban planners being redevelopment of land to meet demand whilst ensuring compatibility with existing land use. This paper presents a geographic information systems (GIS)-based decision support tool (called LUDS) to quantitatively assess land-use suitability for site redevelopment in urban renewal areas. This consists of a model for the suitability analysis and an affiliated land-information database for residential, commercial, industrial, G/I/C (government/institution/community) and open space land uses. Development has occurred with support from interviews with industry experts, focus group meetings and an experimental trial, combined with several advanced techniques and tools, including GIS data processing and spatial analysis, multi-criterion analysis, as well as the AHP method for constructing the model and database. As demonstrated in the trial, LUDS assists planners in making land-use decisions and supports the planning process in assessing urban land-use suitability for site redevelopment. Moreover, it facilitates public consultation (participatory planning) by providing stakeholders with an explicit understanding of planners' views.

Analysis of spatio-temporal dynamic pattern and driving forces of urban land in China in 1990s using TM images and GIS

China has witnessed fast urban growth in the recent decade. This study analyzes spatio-temporal characteristics of urban expansion in China using satellite images and regionalization methods. Landsat TM images at three time periods, 1990/1991, 1995/1996, and 1999/2000, are interpreted to get 1:100000 vector land use datasets. The study calculates the urban land percentage and urban land expansion index of every 1 km(2) cell throughout China. The study divides China into 27 urban regions to conceive dynamic patterns of urban land changes. Urban development was achieving momentum in the western region, expanding more noticeably than in the previous five years, and seeing an increased growth percentage. Land use dynamic changes reflect the strong impacts of economic growth environments and macro-urban development policies. The paper helps to distinguish the influences of newly market-oriented forces from traditional administrative controls on China's urban expansion. (c) 2005 Elsevier Ltd. All rights reserved.

Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Will improved access to capital dampen the need for more agricultural land? A CGE analysis of agricultural capital markets and world-wide biofuel policies. Factor Markets Working Paper No. 48, May 2013

This paper analyses the consequences of enhanced biofuel production in regions and countries of the world that have announced plans to implement or expand on biofuel policies. The analysis considers biofuel policies implemented as binding blending targets for transportation fuels. The chosen quantitative modelling approach is two-fold: it combines the analysis of biofuel policies in a multi-sectoral economic model (MAGNET) with systematic variation of the functioning of capital and labour markets. This paper adds to existing research by considering biofuel policies in the EU, the US and various other countries with considerable agricultural production and trade, such as Brazil, India and China. Moreover, the application multi-sectoral modelling system with different assumptions on the mobility of factor markets allows for the observation of changes in economic indicators under different conditions of how factor markets work. Systematic variation of factor mobility indicates that the ‘burden’ of global biofuel policies is not equally distributed across different factors within agricultural production. Agricultural land, as the pre-dominant and sector-specific factor, is, regardless of different degrees of inter-sectoral or intra-sectoral factor mobility, the most important factor limiting the expansion of agricultural production. More capital and higher employment in agriculture will ease the pressure on additional land use – but only partly. To expand agricultural production at global scale requires both land and mobile factors adapted to increase total factor productivity in agriculture in the most efficient way.

Técnicas de geoprocessamento e de análise de multicritérios na adequação de uso das terras

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Ecologia da paisagem para avaliação da idoneidade de redes ecológicas, como subsídio para o planejamento territorial

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

LCA-GIS Integrated approach for a Sustainable Land Use Under Energy Crops

In genere, negli studi di vocazionalità delle colture, vengono presi in considerazione solo variabili ambientali pedo-climatiche. La coltivazione di una coltura comporta anche un impatto ambientale derivante dalle pratiche agronomiche ed il territorio può essere più o meno sensibile a questi impatti in base alla sua vulnerabilità. In questo studio si vuole sviluppare una metodologia per relazionare spazialmente l’impatto delle colture con le caratteristiche sito specifiche del territorio in modo da considerare anche questo aspetto nell’allocazione negli studi di vocazionalità. LCA è stato utilizzato per quantificare diversi impatti di alcune colture erbacee alimentari e da energia, relazionati a mappe di vulnerabilità costruite con l’utilizzo di GIS, attraverso il calcolo di coefficienti di rischio di allocazione per ogni combinazione coltura-area vulnerabile. Le colture energetiche sono state considerate come un uso alternativo del suolo per diminuire l’impatto ambientale. Il caso studio ha mostrato che l’allocazione delle colture può essere diversa in base al tipo e al numero di impatti considerati. Il risultato sono delle mappe in cui sono riportate le distribuzioni ottimali delle colture al fine di minimizzare gli impatti, rispetto a mais e grano, due colture alimentari importanti nell’area di studio. Le colture con l’impatto più alto dovrebbero essere coltivate nelle aree a vulnerabilità bassa, e viceversa. Se il rischio ambientale è la priorità, mais, colza, grano, girasole, e sorgo da fibra dovrebbero essere coltivate solo nelle aree a vulnerabilità bassa o moderata, mentre, le colture energetiche erbacee perenni, come il panico, potrebbero essere coltivate anche nelle aree a vulnerabilità alta, rappresentando cosi una opportunità per aumentare la sostenibilità di uso del suolo rurale. Lo strumento LCA-GIS inoltre, integrato con mappe di uso attuale del suolo, può aiutare a valutarne il suo grado di sostenibilità ambientale.