An overview of APSIM, a model designed for farming systems simulation

The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

Soil carbon and nitrogen stocks in traditional agricultural and agroforestry systems in the semiarid region of Brazil

In the semiarid region of Brazil, inadequate management of cropping systems and low plant biomass production can contribute to reduce soil carbon (C) and nitrogen (N) stocks; therefore, management systems that preserve C and N must be adopted. This study aimed to evaluate the changes in soil C and N stocks that were promoted by agroforestry (agrosilvopastoral and silvopastoral) and traditional agricultural systems (slash-and-burn clearing and cultivation for two and three years) and to compare these systems with the natural Caatinga vegetation after 13 years of cultivation. The experiment was carried out on a typical Ortic Chromic Luvisol in the municipality of Sobral, Ceará, Brazil. Soil samples were collected (layers 0-6, 6-12, 12-20, 20-40 and 40-60 cm) with four replications. The plain, convex and concave landforms in each study situation were analyzed, and the total organic C, total N and densities of the soil samples were assessed. The silvopastoral system promoted the greatest long-term reductions in C and N stocks, while the agrosilvopastoral system promoted the smallest losses and therefore represents a sustainable alternative for soil C and N sequestration in these semiarid conditions. The traditional agricultural system produced reductions of 58.87 and 9.57 Mg ha-1 in the organic C and total N stocks, respectively, which suggests that this system is inadequate for these semiarid conditions. The organic C stocks were largest in the concave landform in the agrosilvopastoral system and in the plain landform in the silvopastoral system, while the total N values were highest in the concave landform in the native, agrosilvopastoral and silvopastoral systems.

Modelling of global crop production and resulting N2O emissions

Landwirtschaft spielt eine zentrale Rolle im Erdsystem. Sie trägt durch die Emission von CO2, CH4 und N2O zum Treibhauseffekt bei, kann Bodendegradation und Eutrophierung verursachen, regionale Wasserkreisläufe verändern und wird außerdem stark vom Klimawandel betroffen sein. Da all diese Prozesse durch die zugrunde liegenden Nährstoff- und Wasserflüsse eng miteinander verknüpft sind, sollten sie in einem konsistenten Modellansatz betrachtet werden. Dennoch haben Datenmangel und ungenügendes Prozessverständnis dies bis vor kurzem auf der globalen Skala verhindert. In dieser Arbeit wird die erste Version eines solchen konsistenten globalen Modellansatzes präsentiert, wobei der Schwerpunkt auf der Simulation landwirtschaftlicher Erträge und den resultierenden N2O-Emissionen liegt. Der Grund für diese Schwerpunktsetzung liegt darin, dass die korrekte Abbildung des Pflanzenwachstums eine essentielle Voraussetzung für die Simulation aller anderen Prozesse ist. Des weiteren sind aktuelle und potentielle landwirtschaftliche Erträge wichtige treibende Kräfte für Landnutzungsänderungen und werden stark vom Klimawandel betroffen sein. Den zweiten Schwerpunkt bildet die Abschätzung landwirtschaftlicher N2O-Emissionen, da bislang kein prozessbasiertes N2O-Modell auf der globalen Skala eingesetzt wurde. Als Grundlage für die globale Modellierung wurde das bestehende Agrarökosystemmodell Daycent gewählt. Neben der Schaffung der Simulationsumgebung wurden zunächst die benötigten globalen Datensätze für Bodenparameter, Klima und landwirtschaftliche Bewirtschaftung zusammengestellt. Da für Pflanzzeitpunkte bislang keine globale Datenbasis zur Verfügung steht, und diese sich mit dem Klimawandel ändern werden, wurde eine Routine zur Berechnung von Pflanzzeitpunkten entwickelt. Die Ergebnisse zeigen eine gute Übereinstimmung mit Anbaukalendern der FAO, die für einige Feldfrüchte und Länder verfügbar sind. Danach wurde das Daycent-Modell für die Ertragsberechnung von Weizen, Reis, Mais, Soja, Hirse, Hülsenfrüchten, Kartoffel, Cassava und Baumwolle parametrisiert und kalibriert. Die Simulationsergebnisse zeigen, dass Daycent die wichtigsten Klima-, Boden- und Bewirtschaftungseffekte auf die Ertragsbildung korrekt abbildet. Berechnete Länderdurchschnitte stimmen gut mit Daten der FAO überein (R2 = 0.66 für Weizen, Reis und Mais; R2 = 0.32 für Soja), und räumliche Ertragsmuster entsprechen weitgehend der beobachteten Verteilung von Feldfrüchten und subnationalen Statistiken. Vor der Modellierung landwirtschaftlicher N2O-Emissionen mit dem Daycent-Modell stand eine statistische Analyse von N2O-und NO-Emissionsmessungen aus natürlichen und landwirtschaftlichen Ökosystemen. Die als signifikant identifizierten Parameter für N2O (Düngemenge, Bodenkohlenstoffgehalt, Boden-pH, Textur, Feldfrucht, Düngersorte) und NO (Düngemenge, Bodenstickstoffgehalt, Klima) entsprechen weitgehend den Ergebnissen einer früheren Analyse. Für Emissionen aus Böden unter natürlicher Vegetation, für die es bislang keine solche statistische Untersuchung gab, haben Bodenkohlenstoffgehalt, Boden-pH, Lagerungsdichte, Drainierung und Vegetationstyp einen signifikanten Einfluss auf die N2O-Emissionen, während NO-Emissionen signifikant von Bodenkohlenstoffgehalt und Vegetationstyp abhängen. Basierend auf den daraus entwickelten statistischen Modellen betragen die globalen Emissionen aus Ackerböden 3.3 Tg N/y für N2O, und 1.4 Tg N/y für NO. Solche statistischen Modelle sind nützlich, um Abschätzungen und Unsicherheitsbereiche von N2O- und NO-Emissionen basierend auf einer Vielzahl von Messungen zu berechnen. Die Dynamik des Bodenstickstoffs, insbesondere beeinflusst durch Pflanzenwachstum, Klimawandel und Landnutzungsänderung, kann allerdings nur durch die Anwendung von prozessorientierten Modellen berücksichtigt werden. Zur Modellierung von N2O-Emissionen mit dem Daycent-Modell wurde zunächst dessen Spurengasmodul durch eine detailliertere Berechnung von Nitrifikation und Denitrifikation und die Berücksichtigung von Frost-Auftau-Emissionen weiterentwickelt. Diese überarbeitete Modellversion wurde dann an N2O-Emissionsmessungen unter verschiedenen Klimaten und Feldfrüchten getestet. Sowohl die Dynamik als auch die Gesamtsummen der N2O-Emissionen werden befriedigend abgebildet, wobei die Modelleffizienz für monatliche Mittelwerte zwischen 0.1 und 0.66 für die meisten Standorte liegt. Basierend auf der überarbeiteten Modellversion wurden die N2O-Emissionen für die zuvor parametrisierten Feldfrüchte berechnet. Emissionsraten und feldfruchtspezifische Unterschiede stimmen weitgehend mit Literaturangaben überein. Düngemittelinduzierte Emissionen, die momentan vom IPCC mit 1.25 +/- 1% der eingesetzten Düngemenge abgeschätzt werden, reichen von 0.77% (Reis) bis 2.76% (Mais). Die Summe der berechneten Emissionen aus landwirtschaftlichen Böden beträgt für die Mitte der 1990er Jahre 2.1 Tg N2O-N/y, was mit den Abschätzungen aus anderen Studien übereinstimmt.

Evaluating the risk of non-point source pollution from biosolids: integrated modelling of nutrient losses at field and catchment scales

A semi-distributed model, INCA, has been developed to determine the fate and distribution of nutrients in terrestrial and aquatic systems. The model simulates nitrogen and phosphorus processes in soils, groundwaters and river systems and can be applied in a semi-distributed manner at a range of scales. In this study, the model has been applied at field to sub-catchment to whole catchment scale to evaluate the behaviour of biosolid-derived losses of P in agricultural systems. It is shown that process-based models such as INCA, applied at a wide range of scales, reproduce field and catchment behaviour satisfactorily. The INCA model can also be used to generate generic information for risk assessment. By adjusting three key variables: biosolid application rates, the hydrological connectivity of the catchment and the initial P-status of the soils within the model, a matrix of P loss rates can be generated to evaluate the behaviour of the model and, hence, of the catchment system. The results, which indicate the sensitivity of the catchment to flow paths, to application rates and to initial soil conditions, have been incorporated into a Nutrient Export Risk Matrix (NERM).

Granularity and disaggregation in compositional modelling with applications to ecological systems

In the past decade, compositional modelling (CM) has established itself as the predominant knowledge-based approach to construct mathematical (simulation) models automatically. Although it is mainly applied to physical systems, there is a growing interest in applying CM to other domains, such as ecological and socio-economic systems. Inspired by this observation, this paper presents a method for extending the conventional CM techniques to suit systems that are fundamentally presented by interacting populations of individuals instead of physical components or processes. The work supports building model repositories for such systems, especially in addressing the most critical outstanding issues of granularity and disaggregation in ecological systems modelling.

Modelling fungus dispersal scenarios using cellular automata

This study focused on representing spatio-temporal patterns of fungal dispersal using cellular automata. Square lattices were used, with each site representing a host for a hypothetical fungus population. Four possible host states were allowed: resistant, permissive, latent or infectious. In this model, the probability of infection for each of the healthy states (permissive or resistant) in a time step was determined as a function of the host's susceptibility, seasonality, and the number of infectious sites and the distance between them. It was also assumed that infected sites become infectious after a pre-specified latency period, and that recovery is not possible. Several scenarios were simulated to understand the contribution of the model's parameters and the spatial structure on the dynamic behaviour of the modelling system. The model showed good capability for representing the spatio-temporal pattern of fungus dispersal over planar surfaces. With a specific problem in mind, the model can be easily modified and used to describe field behaviour, which can contribute to the conservation and development of management strategies for both natural and agricultural systems. © 2012 Elsevier B.V.

Modelling and predictive control techniques for building heating systems

Model predictive control (MPC) has often been referred to in literature as a potential method for more efficient control of building heating systems. Though a significant performance improvement can be achieved with an MPC strategy, the complexity introduced to the commissioning of the system is often prohibitive. Models are required which can capture the thermodynamic properties of the building with sufficient accuracy for meaningful predictions to be made. Furthermore, a large number of tuning weights may need to be determined to achieve a desired performance. For MPC to become a practicable alternative, these issues must be addressed. Acknowledging the impact of the external environment as well as the interaction of occupants on the thermal behaviour of the building, in this work, techniques have been developed for deriving building models from data in which large, unmeasured disturbances are present. A spatio-temporal filtering process was introduced to determine estimates of the disturbances from measured data, which were then incorporated with metaheuristic search techniques to derive high-order simulation models, capable of replicating the thermal dynamics of a building. While a high-order simulation model allowed for control strategies to be analysed and compared, low-order models were required for use within the MPC strategy itself. The disturbance estimation techniques were adapted for use with system-identification methods to derive such models. MPC formulations were then derived to enable a more straightforward commissioning process and implemented in a validated simulation platform. A prioritised-objective strategy was developed which allowed for the tuning parameters typically associated with an MPC cost function to be omitted from the formulation by separation of the conflicting requirements of comfort satisfaction and energy reduction within a lexicographic framework. The improved ability of the formulation to be set-up and reconfigured in faulted conditions was shown.

Net greenhouse gas fluxes in Brazilian ethanol production systems

Biofuels are both a promising solution to global warming mitigation and a potential contributor to the problem. Several life cycle assessments of bioethanol have been conducted to address these questions. We performed a synthesis of the available data on Brazilian ethanol production focusing on greenhouse gas (GHG) emissions and carbon (C) sinks in the agricultural and industrial phases. Emissions of carbon dioxide (CO(2)) from fossil fuels, methane (CH(4)) and nitrous oxide (N(2)O) from sources commonly included in C footprints, such as fossil fuel usage, biomass burning, nitrogen fertilizer application, liming and litter decomposition were accounted for. In addition, black carbon (BC) emissions from burning biomass and soil C sequestration were included in the balance. Most of the annual emissions per hectare are in the agricultural phase, both in the burned system (2209 out of a total of 2398 kg C(eq)), and in the unburned system (559 out of 748 kg C(eq)). Although nitrogen fertilizer emissions are large, 111 kg C(eq) ha-1 yr-1, the largest single source of emissions is biomass burning in the manual harvest system, with a large amount of both GHG (196 kg C(eq) ha-1 yr-1). and BC (1536 kg C(eq) ha-1 yr-1). Besides avoiding emissions from biomass burning, harvesting sugarcane mechanically without burning tends to increase soil C stocks, providing a C sink of 1500 kg C ha-1 yr-1 in the 30 cm layer. The data show a C output: input ratio of 1.4 for ethanol produced under the conventionally burned and manual harvest compared with 6.5 for the mechanized harvest without burning, signifying the importance of conservation agricultural systems in bioethanol feedstock production.

Using Lindenmayer systems to model morphogenesis in a tropical pasture legume Stylosanthes scabra

This paper summarizes the processes involved in designing a mathematical model of a growing pasture plant, Stylosanthes scabra Vog. cv. Fitzroy. The model is based on the mathematical formalism of Lindenmayer systems and yields realistic computer-generated images of progressive plant geometry through time. The processes involved in attaining growth data, retrieving useful growth rules, and constructing a virtual plant model are outlined. Progressive output morphological data proved useful for predicting total leaf area and allowed for easier quantification of plant canopy size in terms of biomass and total leaf area.

Improving wheat simulation capabilities in Australia from a cropping systems perspective: water and nitrogen effects on spring wheat in a semi-arid environment

Systems approaches can help to evaluate and improve the agronomic and economic viability of nitrogen application in the frequently water-limited environments. This requires a sound understanding of crop physiological processes and well tested simulation models. Thus, this experiment on spring wheat aimed to better quantify water x nitrogen effects on wheat by deriving some key crop physiological parameters that have proven useful in simulating crop growth. For spring wheat grown in Northern Australia under four levels of nitrogen (0 to 360 kg N ha(-1)) and either entirely on stored soil moisture or under full irrigation, kernel yields ranged from 343 to 719 g m(-2). Yield increases were strongly associated with increases in kernel number (9150-19950 kernels m(-2)), indicating the sensitivity of this parameter to water and N availability. Total water extraction under a rain shelter was 240 mm with a maximum extraction depth of 1.5 m. A substantial amount of mineral nitrogen available deep in the profile (below 0.9 m) was taken up by the crop. This was the source of nitrogen uptake observed after anthesis. Under dry conditions this late uptake accounted for approximately 50% of total nitrogen uptake and resulted in high (>2%) kernel nitrogen percentages even when no nitrogen was applied,Anthesis LAI values under sub-optimal water supply were reduced by 63% and under sub-optimal nitrogen supply by 50%. Radiation use efficiency (RUE) based on total incident short-wave radiation was 1.34 g MJ(-1) and did not differ among treatments. The conservative nature of RUE was the result of the crop reducing leaf area rather than leaf nitrogen content (which would have affected photosynthetic activity) under these moderate levels of nitrogen limitation. The transpiration efficiency coefficient was also conservative and averaged 4.7 Pa in the dry treatments. Kernel nitrogen percentage varied from 2.08 to 2.42%. The study provides a data set and a basis to consider ways to improve simulation capabilities of water and nitrogen effects on spring wheat. (C) 1997 Elsevier Science B.V.

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.

Low input tillage/cropping systems for limited resource areas

Agriculture in limited resource areas is characterized by small farms which an generally too small to adequately support the needs of an average farm family. The farming operation can be described as a low input cropping system with the main energy source being manual labor, draught animals and in some areas hand tractors. These farming systems are the most important contributor to the national economy of many developing countries. The role of tillage is similar in dryland agricultural systems in both the high input (HICS) and low input cropping systems (LICS), however, wet cultivation or puddling is unique to lowland rice-based systems in low input cropping systems. Evidence suggest that tillage may result in marginal increases in crop yield in the short term, however, in the longer term it may be neutral or give rise to yield decreases associated with soil structural degradation. On marginal soils, tillage may be required to prepare suitable seedbeds or to release adequate Nitrogen through mineralization, but in the longer term, however, tillage reduces soil organic matter content, increases soil erodibility and the emission of greenhouse gases. Tillage in low input cropping systems involves a very large proportion of the population and any changes: in current practices such as increased mechanization will have a large social impact such as increased unemployment and increasing feminization of poverty, as mechanization may actually reduce jobs for women. Rapid mechanization is likely to result in failures, but slower change, accompanied by measures to provide alternative rural employment, might be beneficial. Agriculture in limited resource areas must produce the food and fiber needs of their community, and its future depends on the development of sustainable tillage/cropping systems that are suitable for the soil and climatic conditions. These should be based on sound biophysical principles and meet the needs of and he acceptable to the farming communities. Some of the principle requirements for a sustainable system includes the maintenance of soil health, an increase in the rain water use efficiency of the system, increased use of fertilizer and the prevention of erosion. The maintenance of crop residues on the surface is paramount for meeting these requirements, and the competing use of crop residues must be met from other sources. These requirements can be met within a zonal tillage system combined with suitable agroforestry, which will reduce the need for crop residues. It is, however, essential that farmers participate in the development of any new technologies to ensure adoption of the new system. (C) 2001 Elsevier Science B.V. All rights reserved.

Modelling the Human Values Scale from Consumers Transactional Data Bases

The main objective of this paper aims at developing a methodology that takes into account the human factor extracted from the data base used by the recommender systems, and which allow to resolve the specific problems of prediction and recommendation. In this work, we propose to extract the user's human values scale from the data base of the users, to improve their suitability in open environments, such as the recommender systems. For this purpose, the methodology is applied with the data of the user after interacting with the system. The methodology is exemplified with a case study

Physical properties of dystrophic Red Latosol (Oxisol) under different agricultural uses

Obtaining information about soil properties under different agricultural uses to plan soil management is very important with a view to sustainability in the different agricultural systems. The aim of this study was to evaluate changes in certain indicators of the physical quality of a dystrophic Red Latosol (Oxisol) under different agricultural uses. The study was conducted in an agricultural area located in northern Paraná State. Dystrophic Red Latosol samples were taken from four sites featuring different types of land use typical of the region: pasture of Brachiaria decumbens (P); sugarcane (CN); annual crops under no-tillage (CAPD); and native forest (permanent conservation area) (control (C)). For each land use, 20 completely randomized, disturbed and undisturbed soil samples were collected from the 0-20 cm soil layer, to determine soil texture, volume of water-dispersible clay, soil flocculation (FD), particle density, quantity of organic matter (OM), soil bulk density (Ds), soil macroporosity (Ma) and microporosity (Mi), total soil porosity (TSP), mean geometric diameter of soil aggregates (MGD), and penetration resistance (PR). The results showed differences in OM, FD, MGD, Ds, PR, and Ma between the control (soil under forest) and the areas used for agriculture (P, CN and CAPD). The soils of the lowest physical quality were those used for CN and CAPD, although only the former presented a Ma level very close to that representing unfavorable conditions for plant growth. For the purposes of this study, the physical properties studied were found to perform well as indicators of soil quality.

Microbial biomass and soil chemical properties under different land use systems in northeastern Pará

The increase in agricultural production in the Brazilian Amazon region is mostly a result of the agricultural frontier expansion, into areas previously influenced by humans or of native vegetation. At the same time, burning is still used to clear areas in small-scale agricultural systems, leading to a loss of the soil productive capacity shortly after, forcing the opening of new areas. This study had the objective of evaluating the effect of soil preparation methods that involve plant residue shredding, left on the surface or incorporated to the soil, with or without chemical fertilization, on the soil chemical and biological properties. The experiment was conducted in 1995, in an experimental field of Yellow Latosol (Oxisol) of the Embrapa Amazônia Oriental, northeastern Pará (Brazil). The experiment was arranged in randomized blocks, in a 2x6 factorial design, with two management systems and six treatments evaluated twice. The management systems consisted of rice (Oriza sativa), followed by cowpea (Vigna unguiculata) with manioc (Manihot esculenta). In the first system the crops were planted in two consecutive cycles, followed by a three-year fallow period (natural regrowth); the second system consisted of one cultivation cycle and was left fallow for three years. The following treatments were applied to the secondary forest vegetation: slash and burn, fertilized with NPK (Q+NPK); slash and burn, without fertilizer NPK (Q-NPK); cutting and shredding, leaving the residues on the soil surface, fertilized with NPK (C+NPK); cutting and shredding, leaving residues on the soil surface, without fertilizer (C-NPK); cutting and shredding, with residue incorporation and fertilized with NPK (I+NPK); cutting and shredding, with residue incorporation and without NPK fertilizer (I-NPK). The soil was sampled in the rainier season (April 2006) and in the drier season (September 2006), in the 0-0.1 m layer. From each plot, 10 simple samples were collected in order to generate a composite sample. In the more intensive management system the contents of microbial C (Cmic) and microbial N (Nmic) were higher, while the C (Corg) level was higher in the less intensive system. The treatments with highest Cmic and Nmic levels were those with cutting, shredding and distribution of biomass on the soil surface. Under both management systems, the chemical characteristics were in ranges that classify the soil as little fertile, although P and K (in the rainy season) were higher in the less intensive management system.