Search

Agricultural land use and land cover change(Agri-LUCC) is one of the key issues among global change and sustainability studies.Year-on-year progress makes "agricultural land change" to be an emerging interdisciplinary science.As an effective tool for understanding the driver,process and consequence of Agri-LUCC,spatially-explicit land change models have successfully applied in representing agricultural landscapes and its possible developments across scales.Although several breakthroughs have been achieved by traditional land change modeling,there are still many crucial issues remain unsolved,especially the insufficient cognition on the complexity and dynamics of agricultural land systems.Recently,some researchers begin to combine agent-based models(ABM,one of the key tools for complex system studies) with land change models,bringing a new emergence of model series in the agricultural land change modeling community,which are called as Agri-ABM/LUCCs.Progress in this field can be summarized as:(1) Based on the complexity system theory,most of these models bring theoretical and methodological innovations in analyzing the complexity of agricultural land systems.(2) These models innovatively take land use decisions at individual level into consideration,based on which to recognize the role of decision makers bringing about changes,through their choices,on regional level landscapes.Such "modeling with stakeholders" underlines the role of farmers in agricultural transformation,facilitating the expression of diversified decisions on agricultural land use from heterogeneous farmers.(3) Agri-ABM/LUCC links "land change driving forces" with "land use consequences" as an endogenous feedback loop in agricultural land change processes.This tightly coupled method describes a better feature of agricultural land dynamics,which is essential for analyzing the vulnerabilities,impacts,and adaptation in agricultural land change context.(4) From the recent literature,a wild range of issues related to farmer′s decisions on their land were discussed,including deforestation,agricultural expansion,crop allocation,resource management,and settlement and livelihood decisions.In these studies,various methods and approaches were used in representing farmer′s decisions.Methods include linear programming model,optimization model,heuristic imitative and innovative decision-making algorithms,utility function,decision tree,evolutionary programming,probabilistic method,participatory modeling,role playing game,bounded-rational approach,spatial multi-nominal logistic functions,among others.(5) This new perspective provides a way to dynamically link agricultural land change assessments for integrated human-natural studies.On one hand,consequence of agricultural land change can be used to forecast crop production then to develop food security scenarios;on the other hand,the same land change result is valuable for predicting carbon-nitrogen cycling processes,consequently for projecting carbon sequestration within large scale agricultural landscapes.Scenarios of food and ecological security provide feedbacks to individual farmers to alter their decisions of land use in turn.Beside the progress,however,problems of current Agri-ABM/LUCCs still exist,such as "theory divorced from practice",deficiency in cross-site comparison,and difficulties in carrying out large-scale modeling.The most critical problem is that other than the common characteristics of complex adaptive systems,some of the special features of agricultural land systems exit in their spatial-temporal dynamics,scaling effects,coupled human and natural issues,and multi-dimension feedbacks.These features are still not well examined in the current studies,which require further in-depth discussions in the future.

This paper studies the classification of the International Commission on Illumination (CIE) Standard General Skies using vertical sky component ( VSC ) defined as the procedures for identifying the Standard General Skies are proposed. The performance of the recommended sky classification process was evaluated against measured data in terms of percentage error (PE) and root-mean-square error (RMSE). The results revealed that the method produced the RMSE of not more than 20% for most of classified Standard General Skies. The proposed approach provides an alternative for defining CIE Standard General Skies when the measured vertical and horizontal daylight illuminance data are available.

This paper presents a scenario-based assessment of global future food security. To do that, the socio-economic and climate change scenarios were defined for the future and were linked to an integrated modeling framework. The crop yields simulated by the GIS-based Environmental Policy Integrated Climate (EPIC) model and crop areas simulated by the crop choice decision model were combined to calculate the total food production and per capita food availability, which was used to represent the status of food availability and stability. The per capita Gross Domestic Product (GDP) simulated by IFPSIM model was used to reflect the situation of food accessibility and affordability. Based on these two indicators, the future food security status was assessed at a global scale over a period of approximately 20 years, starting from the year 2000. The results show that certain regions such as South Asia and most African countries will likely remain hotspots of food insecurity in the future as both the per capita food availability and the capacity of being able to import food will decrease between 2000 and 2020. Low food production associated with poverty is the determining factor to starvation in these regions, and more efforts are needed to combat hunger in terms of future actions. Other regions such as China, most Eastern European countries and most South American countries where there is an increase in per capita food availability or an increase in the capacity to import food between 2000 and 2020 might be able to improve their food security situation.

Climatic variables are frequently used as weighting factors to indicate the degree of clearness for interpreting sky patterns. However, such important parameters are not always widely available and their criteria to define a sky condition are not clear-cut. In addition, certain variables may be more effective than the others in terms of sky identification. This paper studies the capability of various daylight parameters, namely zenith luminance, global, direct-beam and sky-diffuse illuminance, and solar altitude for categorizing the 15 International Commission on Illumination (CIE) standard skies. A new form of artificial neural networks called probabilistic neural network (PNN) which is a powerful technique for pattern recognition was used for the analysis. The findings suggested that the PNN is an appropriate tool when a number of climatic parameters of various criteria for differentiating sky standards are employed, and the ratio of zenith luminance to diffuse illuminance (Lz/Dv) and solar altitude (αs) are respectively the most and the least significant input parameters for discriminating between the 15 CIE skies.

Designing a building to integrate daylight requires an accurate estimation of the amount of available outdoor illuminance. The common method for predicting daylight has been the derivation of illuminance from the more widely measured solar irradiance using the luminous efficacy approach. Recently, the International Commission on Illumination (CIE) has adopted a range of 15 standard skies which cover the whole probable spectrum of skies in the world. This paper presents the work to model the luminous efficacy of diffuse component under the 15 CIE standard skies. Sky luminance distributions measured between 1999 and 2005 were used for the standard sky classifications. An approach to develop luminous efficacy for inclined surfaces was proposed. The predicted vertical outdoor illuminance data for the four cardinal planes (i.e., N, E, S and W) using the proposed luminous efficacy were evaluated against data measured in 2004. Statistical analysis indicated that the estimated daylight illuminance data give acceptable agreements with measured data for all vertical planes.

The correlation between the clearness index and sunshine duration is useful in the estimation of the solar radiation for areas where measured solar radiation data are unavailable. Regression techniques and artificial neural networks were used to investigate the correlations between daily global solar radiation (GSR) and sunshine duration for different climates in China. Measurements made during the 30-year period (1971–2000) from 41 measuring stations covering 9 thermal and 7 solar climate zones and sub-zones across China were gathered and analysed. The performance of the regression and the ANN models in the thermal and solar zones was analysed and compared. The coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (NSEC), mean bias error (MBE) and root-mean-square error (RMSE) were determined. It was found that the regression models in both the thermal and the solar climate zones showed a strong correlation between the clearness index and sunshine duration (R2=0.79–88). There appeared to be an increasing trend of larger MBE and RMSE from colder climates in the north to warmer climates in the south. In terms of the thermal and solar climate zone models, there was very little to choose between the two models.

This paper studies the classification of the International Commission on Illumination (CIE) standard skies using sky luminance and other climatic parameters measured in Hong Kong. An approach to identify the standard skies is proposed. The performance of the proposed approach and the Bartzokas et al. method was evaluated against the measured data in terms of root-mean-square error (RMSE). The statistical analysis revealed that using the Bartzokas et al. method, around 50% of the data were rejected for sky classification and the overall RMSE was calculated of around 34%. The proposed approach produced an overall RMSE of just less than 33% without data reduction.

Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is presented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users’ decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions, while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001–2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover product in 2001. Both validation approaches indicated reliability of the model for addressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally, the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline. The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

We present the energy use situation in Hong Kong from 1979 to 2006. The primary energy requirement (PER) nearly tripled during the 28-year period, rising from 195,405 to 566,685 TJ, about two-third of which was used for electricity generation. The residential and commercial sectors are the two largest electricity end-users with an average annual growth rate of 5.9% and 7.4%, respectively. The monthly consumption in these two sectors shows distinct seasonal variations mainly due to changes in the air-conditioning requirements, which are affected by the prevailing weather conditions. Principal component analysis of five major climatic variables—dry-bulb temperature, wet-bulb temperature, global solar radiation, clearness index and wind speed—was conducted. Measured sector-wide electricity consumption was correlated with the corresponding two principal components determined using multiple regression technique. The regression models could give a very good indication of the annual electricity use (largely within a few percents), but individual monthly estimation could differ by up to 24%. It was also found that the climatic indicators determined appeared to show a slight increasing trend during the 28-year period indicating a subtle, but gradual change of climatic conditions that might affect future air-conditioning requirements.

The intensive cropping system of “four-crops four-harvests” or “five-crops five-harvests” per year developed in Fugou County, Henan Province is a rotation system with very high productivity. The yield reached 15,825 kg/ha, which is 2.5 times of the yield per unit area in the traditional cropping systems of two crops per annum practiced in Henan Province. However, the concern has been whether these highly intensive agricultural practices are environmentally sustainable. This study attempts to address this concern by investigating the environmental, impact of the highly intensive farming system in Fugou County. Based on the production and economic results and analysis of key environmental indicators (soil fertility change, groundwater level change and content of NO3-N in groundwater) over 12 years, we concluded that there is no significant resource depletion nor environmental degradation that occurred on the farm. However, more research should be oriented to the existing major problems and constrai...