A universal agro-hydrological model for water and nitrogen cycles in the soil-crop system SMCR_N: critical update and further validation

Agro-hydrological models have widely been used for optimizing resources use and minimizing environmental consequences in agriculture. SMCRN is a recently developed sophisticated model which simulates crop response to nitrogen fertilizer for a wide range of crops, and the associated leaching of nitrate from arable soils. In this paper, we describe the improvements of this model by replacing the existing approximate hydrological cascade algorithm with a new simple and explicit algorithm for the basic soil water flow equation, which not only enhanced the model performance in hydrological simulation, but also was essential to extend the model application to the situations where the capillary flow is important. As a result, the updated SMCRN model could be used for more accurate study of water dynamics in the soil-crop system. The success of the model update was demonstrated by the simulated results that the updated model consistently out-performed the original model in drainage simulations and in predicting time course soil water content in different layers in the soil-wheat system. Tests of the updated SMCRN model against data from 4 field crop experiments showed that crop nitrogen offtakes and soil mineral nitrogen in the top 90 cm were in a good agreement with the measured values, indicating that the model could make more reliable predictions of nitrogen fate in the crop-soil system, and thus provides a useful platform to assess the impacts of nitrogen fertilizer on crop yield and nitrogen leaching from different production systems. (C) 2010 Elsevier B.V. All rights reserved.

A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved.

Improving wheat simulation capabilities in Australia from a cropping systems perspective - III. The integrated wheat model (I_WHEAT)

Previous work has identified several short-comings in the ability of four spring wheat and one barley model to simulate crop processes and resource utilization. This can have important implications when such models are used within systems models where final soil water and nitrogen conditions of one crop define the starting conditions of the following crop. In an attempt to overcome these limitations and to reconcile a range of modelling approaches, existing model components that worked demonstrably well were combined with new components for aspects where existing capabilities were inadequate. This resulted in the Integrated Wheat Model (I_WHEAT), which was developed as a module of the cropping systems model APSIM. To increase predictive capability of the model, process detail was reduced, where possible, by replacing groups of processes with conservative, biologically meaningful parameters. I_WHEAT does not contain a soil water or soil nitrogen balance. These are present as other modules of APSIM. In I_WHEAT, yield is simulated using a linear increase in harvest index whereby nitrogen or water limitations can lead to early termination of grainfilling and hence cessation of harvest index increase. Dry matter increase is calculated either from the amount of intercepted radiation and radiation conversion efficiency or from the amount of water transpired and transpiration efficiency, depending on the most limiting resource. Leaf area and tiller formation are calculated from thermal time and a cultivar specific phyllochron interval. Nitrogen limitation first reduces leaf area and then affects radiation conversion efficiency as it becomes more severe. Water or nitrogen limitations result in reduced leaf expansion, accelerated leaf senescence or tiller death. This reduces the radiation load on the crop canopy (i.e. demand for water) and can make nitrogen available for translocation to other organs. Sensitive feedbacks between light interception and dry matter accumulation are avoided by having environmental effects acting directly on leaf area development, rather than via biomass production. This makes the model more stable across environments without losing the interactions between the different external influences. When comparing model output with models tested previously using data from a wide range of agro-climatic conditions, yield and biomass predictions were equal to the best of those models, but improvements could be demonstrated for simulating leaf area dynamics in response to water and nitrogen supply, kernel nitrogen content, and total water and nitrogen use. I_WHEAT does not require calibration for any of the environments tested. Further model improvement should concentrate on improving phenology simulations, a more thorough derivation of coefficients to describe leaf area development and a better quantification of some processes related to nitrogen dynamics. (C) 1998 Elsevier Science B.V.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Nino Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975-93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April-May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean-atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

Predicted soil organic carbon stocks and changes in Kenya between 1990 and 2030

Under the United Nations Framework Convention on Climate Change (UNFCCC), Non-Annex 1 countries such as Kenya are obliged to report green house gas (GHG) emissions from all sources where possible, including those from soils as a result of changes in land use or land management. At present, the convention encourages countries to estimate emissions using the most advanced methods possible, given the country circumstances and resources. Estimates of soil organic carbon (SOC) stocks and changes were made for Kenya using the Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. The tool conducts analysis using three methods: (1) the Century general ecosystem model; (2) the RothC soil C decomposition model; and (3) the Intergovernmental Panel on Climate Change (IPCC) method for assessing soil C at regional scales. The required datasets included: land use history, monthly mean precipitation, monthly mean minimum and maximum temperatures for all the agro-climatic zones of Kenya and historical vegetation cover. Soil C stocks of 1.4-2.0 Pg (0-20 cm), compared well with a Soil and Terrain (SOTER) based approach that estimated similar to .8-2.0 Pg (0-30 cm). In 1990 48% of the country had SOC stocks of < 18 t C ha(-1) and 20% of the country had SOC stocks of 18-30 t C ha(-1), whereas in 2000 56% of the country had SOC stocks of < 18 t C ha(-1) and 31% of the country had SOC stocks of 18-30 t C ha(-1). Conversion of natural vegetation to annual crops led to the greatest soil C losses. Simulations suggest that soil C losses remain substantial throughout the modelling period of 1990-2030. All three methods involved in the GEFSOC System estimated that there would be a net loss of soil C between 2000 and 2030 in Kenya. The decline was more marked with RothC than with Century or the IPCC method. In non-hydric soils the SOC change rates were more pronounced in high sandy soils compared to high clay soils in most land use systems. (C) 2007 Elsevier B.V. All rights reserved.

A biophysical model of Sugarcane growth

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in crop-based biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process-based sugarcane model (included as a module within the Agro-IBIS dynamic agro-ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/overestimated the daily sensible/latent heat flux (by -10.5% and 14.8%, H and E, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between -10.1% and 12.5%) when compared against an agro-meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of Sao Paulo, Brazil, over a period of 16 years, with a yield relative bias of -0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (-2.67%), but exhibited a lower interannual variability than the observed yields.

Impacto del cambio climático sobre los usos del agua en Europa

Esta Tesis realiza una contribución metodológica al estudio del impacto del cambio climático sobre los usos del agua, centrándose particularmente en la agricultura. Tomando en consideración su naturaleza distinta, la metodología aborda de forma integral los impactos sobre la agricultura de secano y la agricultura de regadío. Para ello incorpora diferentes modelos agrícolas y de agua que conjuntamente con las simulaciones de los escenarios climáticos permiten determinar indicadores de impacto basados en la productividad de los cultivos, para el caso de la agricultura de secano, e indicadores de impacto basados en la disponibilidad de agua para irrigación, para el caso de la agricultura de regadío. La metodología toma en consideración el efecto de la variabilidad climática en la agricultura, evaluando las necesidades de adaptación y gestión asociadas a los impactos medios y a la variabilidad en la productividad de los cultivos y el efecto de la variabilidad hidrológica en la disponibilidad de agua para regadío. Considerando la gran cantidad de información proporcionada por las salidas de las simulaciones de los escenarios climáticos y su complejidad para procesarla, se ha desarrollado una herramienta de cálculo automatizada que integra diferentes escenarios climáticos, métodos y modelos que permiten abordar el impacto del cambio climático sobre la agricultura, a escala de grandes extensiones. El procedimiento metodológico parte del análisis de los escenarios climáticos en situación actual (1961-1990) y futura (2071-2100) para determinar su fiabilidad y conocer qué dicen exactamente las proyecciones climáticas a cerca de los impactos esperados en las principales variables que intervienen en el ciclo hidrológico. El análisis hidrológico se desarrolla en los ámbitos territoriales de la planificación hidrológica en España, considerando la disponibilidad de información para validar los resultados en escenario de control. Se utilizan como datos observados las series de escorrentía en régimen natural estimadas el modelo hidrológico SIMPA que está calibrado en la totalidad del territorio español. Al trabajar a escala de grandes extensiones, la limitada disponibilidad de datos o la falta de modelos hidrológicos correctamente calibrados para obtener los valores de escorrentía, muchas veces dificulta el proceso de evaluación, por tanto, en este estudio se plantea una metodología que compara diferentes métodos de interpolación y alternativas para generar series anuales de escorrentía que minimicen el sesgo con respecto a los valores observados. Así, en base a la alternativa que genera los mejores resultados, se obtienen series mensuales corregidas a partir de las simulaciones de los modelos climáticos regionales (MCR). Se comparan cuatro métodos de interpolación para obtener los valores de las variables a escala de cuenca hidrográfica, haciendo énfasis en la capacidad de cada método para reproducir los valores observados. Las alternativas utilizadas consideran la utilización de la escorrentía directa simulada por los MCR y la escorrentía media anual calculada utilizando cinco fórmulas climatológicas basadas en el índice de aridez. Los resultados se comparan además con la escorrentía global de referencia proporcionada por la UNH/GRDC que en la actualidad es el “mejor estimador” de la escorrentía actual a gran escala. El impacto del cambio climático en la agricultura de secano se evalúa considerando el efecto combinado de los riesgos asociados a las anomalías dadas por los cambios en la media y la variabilidad de la productividad de los cultivos en las regiones agroclimáticas de Europa. Este procedimiento facilita la determinación de las necesidades de adaptación y la identificación de los impactos regionales que deben ser abordados con mayor urgencia en función de los riesgos y oportunidades identificadas. Para ello se utilizan funciones regionales de productividad que han sido desarrolladas y calibradas en estudios previos en el ámbito europeo. Para el caso de la agricultura de regadío, se utiliza la disponibilidad de agua para irrigación como un indicador del impacto bajo escenarios de cambio climático. Considerando que la mayoría de estudios se han centrado en evaluar la disponibilidad de agua en régimen natural, en este trabajo se incorpora el efecto de las infraestructuras hidráulicas al momento de calcular el recurso disponible bajo escenarios de cambio climático Este análisis se desarrolla en el ámbito español considerando la disponibilidad de información, tanto de las aportaciones como de los modelos de explotación de los sistemas hidráulicos. Para ello se utiliza el modelo de gestión de recursos hídricos WAAPA (Water Availability and Adaptation Policy Assessment) que permite calcular la máxima demanda que puede atenderse bajo determinados criterios de garantía. Se utiliza las series mensuales de escorrentía observadas y las series mensuales de escorrentía corregidas por la metodología previamente planteada con el objeto de evaluar la disponibilidad de agua en escenario de control. Se construyen proyecciones climáticas utilizando los cambios en los valores medios y la variabilidad de las aportaciones simuladas por los MCR y también utilizando una fórmula climatológica basada en el índice de aridez. Se evalúan las necesidades de gestión en términos de la satisfacción de las demandas de agua para irrigación a través de la comparación entre la disponibilidad de agua en situación actual y la disponibilidad de agua bajo escenarios de cambio climático. Finalmente, mediante el desarrollo de una herramienta de cálculo que facilita el manejo y automatización de una gran cantidad de información compleja obtenida de las simulaciones de los MCR se obtiene un proceso metodológico que evalúa de forma integral el impacto del cambio climático sobre la agricultura a escala de grandes extensiones, y a la vez permite determinar las necesidades de adaptación y gestión en función de las prioridades identificadas. ABSTRACT This thesis presents a methodological contribution for studying the impact of climate change on water use, focusing particularly on agriculture. Taking into account the different nature of the agriculture, this methodology addresses the impacts on rainfed and irrigated agriculture, integrating agricultural and water planning models with climate change simulations scenarios in order to determine impact indicators based on crop productivity and water availability for irrigation, respectively. The methodology incorporates the effect of climate variability on agriculture, assessing adaptation and management needs associated with mean impacts, variability in crop productivity and the effect of hydrologic variability on water availability for irrigation. Considering the vast amount of information provided by the outputs of the regional climate model (RCM) simulations and also its complexity for processing it, a tool has been developed to integrate different climate scenarios, methods and models to address the impact of climate change on agriculture at large scale. Firstly, a hydrological analysis of the climate change scenarios is performed under current (1961-1990) and future (2071-2100) situation in order to know exactly what the models projections say about the expected impact on the main variables involved in the hydrological cycle. Due to the availability of information for validating the results in current situation, the hydrological analysis is developed in the territorial areas of water planning in Spain, where the values of naturalized runoff have been estimated by the hydrological model SIMPA, which are used as observed data. By working in large-scale studies, the limited availability of data or lack of properly calibrated hydrological model makes difficult to obtain runoff time series. So as, a methodology is proposed to compare different interpolation methods and alternatives to generate annual times series that minimize the bias with respect to observed values. Thus, the best alternative is selected in order to obtain bias-corrected monthly time series from the RCM simulations. Four interpolation methods for downscaling runoff to the basin scale from different RCM are compared with emphasis on the ability of each method to reproduce the observed behavior of this variable. The alternatives consider the use of the direct runoff of the RCMs and the mean annual runoff calculated using five functional forms of the aridity index. The results are also compared with the global runoff reference provided by the UNH/GRDC dataset, as a contrast of the “best estimator” of current runoff on a large scale. Secondly, the impact of climate change on rainfed agriculture is assessed considering the combined effect of the risks associated with anomalies given by changes in the mean and variability of crop productivity in the agro-climatic regions of Europe. This procedure allows determining adaptation needs based on the regional impacts that must be addressed with greater urgency in light of the risks and opportunities identified. Statistical models of productivity response are used for this purpose which have been developed and calibrated in previous European study. Thirdly, the impact of climate change on irrigated agriculture is evaluated considering the water availability for irrigation as an indicator of the impact. Given that most studies have focused on assessing water availability in natural regime, the effect of regulation is incorporated in this approach. The analysis is developed in the Spanish territory considering the available information of the observed stream flows and the regulation system. The Water Availability and Adaptation Policy Assessment (WAAPA) model is used in this study, which allows obtaining the maximum demand that could be supplied under certain conditions (demand seasonal distribution, water supply system management, and reliability criteria) for different policy alternatives. The monthly bias corrected time series obtained by previous methodology are used in order to assess water availability in current situation. Climate change projections are constructed taking into account the variation in mean and coefficient of variation simulated by the RCM. The management needs are determined by the agricultural demands satisfaction through the comparison between water availability under current conditions and under climate change projections. Therefore, the methodology allows evaluating the impact of climate change on agriculture to large scale, using a tool that facilitates the process of a large amount of complex information provided by the RCM simulations, in order to determine the adaptation and management needs in accordance with the priorities of the indentified impacts.

On measuring quality of a probabilistic commodity forecast for a system that incorporates seasonal climate forecasts

Regional commodity forecasts are being used increasingly in agricultural industries to enhance their risk management and decision-making processes. These commodity forecasts are probabilistic in nature and are often integrated with a seasonal climate forecast system. The climate forecast system is based on a subset of analogue years drawn from the full climatological distribution. In this study we sought to measure forecast quality for such an integrated system. We investigated the quality of a commodity (i.e. wheat and sugar) forecast based on a subset of analogue years in relation to a standard reference forecast based on the full climatological set. We derived three key dimensions of forecast quality for such probabilistic forecasts: reliability, distribution shift, and change in dispersion. A measure of reliability was required to ensure no bias in the forecast distribution. This was assessed via the slope of the reliability plot, which was derived from examination of probability levels of forecasts and associated frequencies of realizations. The other two dimensions related to changes in features of the forecast distribution relative to the reference distribution. The relationship of 13 published accuracy/skill measures to these dimensions of forecast quality was assessed using principal component analysis in case studies of commodity forecasting using seasonal climate forecasting for the wheat and sugar industries in Australia. There were two orthogonal dimensions of forecast quality: one associated with distribution shift relative to the reference distribution and the other associated with relative distribution dispersion. Although the conventional quality measures aligned with these dimensions, none measured both adequately. We conclude that a multi-dimensional approach to assessment of forecast quality is required and that simple measures of reliability, distribution shift, and change in dispersion provide a means for such assessment. The analysis presented was also relevant to measuring quality of probabilistic seasonal climate forecasting systems. The importance of retaining a focus on the probabilistic nature of the forecast and avoiding simplifying, but erroneous, distortions was discussed in relation to applying this new forecast quality assessment paradigm to seasonal climate forecasts. Copyright (K) 2003 Royal Meteorological Society.

Second differential of the entropy as a criterion for the stability in low-dimensional climate models

The second differential of the entropy is used for analysing the stability of a thermodynamic climatic model. A delay time for the heat flux is introduced whereby it becomes an independent variable. Two different expressions for the second differential of the entropy are used: one follows classical irreversible thermodynamics theory; the second is related to the introduction of response time and is due to the extended irreversible thermodynamics theory. the second differential of the classical entropy leads to unstable solutions for high values of delay times. the extended expression always implies stable states for an ice-free earth. When the ice-albedo feedback is included, a discontinuous distribution of stable states is found for high response times. Following the thermodynamic analysis of the model, the maximum rates of entropy production at the steady state are obtained. A latitudinally isothermal earth produces the extremum in global entropy production. the material contribution to entropy production (by which we mean the production of entropy by material transport of heat) is a maximum when the latitudinal distribution of temperatures becomes less homogeneous than present values

Optimization and Management of Materials in Earthwork Construction, TR-501, 2010

As a result of forensic investigations of problems across Iowa, a research study was developed aimed at providing solutions to identified problems through better management and optimization of the available pavement geotechnical materials and through ground improvement, soil reinforcement, and other soil treatment techniques. The overall goal was worked out through simple laboratory experiments, such as particle size analysis, plasticity tests, compaction tests, permeability tests, and strength tests. A review of the problems suggested three areas of study: pavement cracking due to improper management of pavement geotechnical materials, permeability of mixed-subgrade soils, and settlement of soil above the pipe due to improper compaction of the backfill. This resulted in the following three areas of study: (1) The optimization and management of earthwork materials through general soil mixing of various select and unsuitable soils and a specific example of optimization of materials in earthwork construction by soil mixing; (2) An investigation of the saturated permeability of compacted glacial till in relation to validation and prediction with the Enhanced Integrated Climatic Model (EICM); and (3) A field investigation and numerical modeling of culvert settlement. For each area of study, a literature review was conducted, research data were collected and analyzed, and important findings and conclusions were drawn. It was found that optimum mixtures of select and unsuitable soils can be defined that allow the use of unsuitable materials in embankment and subgrade locations. An improved model of saturated hydraulic conductivity was proposed for use with glacial soils from Iowa. The use of proper trench backfill compaction or the use of flowable mortar will reduce the potential for developing a bump above culverts.

Design, implementation and application of a generic framework for integrated regional land-use modeling

Land use is a crucial link between human activities and the natural environment and one of the main driving forces of global environmental change. Large parts of the terrestrial land surface are used for agriculture, forestry, settlements and infrastructure. Given the importance of land use, it is essential to understand the multitude of influential factors and resulting land use patterns. An essential methodology to study and quantify such interactions is provided by the adoption of land-use models. By the application of land-use models, it is possible to analyze the complex structure of linkages and feedbacks and to also determine the relevance of driving forces. Modeling land use and land use changes has a long-term tradition. In particular on the regional scale, a variety of models for different regions and research questions has been created. Modeling capabilities grow with steady advances in computer technology, which on the one hand are driven by increasing computing power on the other hand by new methods in software development, e.g. object- and component-oriented architectures. In this thesis, SITE (Simulation of Terrestrial Environments), a novel framework for integrated regional sland-use modeling, will be introduced and discussed. Particular features of SITE are the notably extended capability to integrate models and the strict separation of application and implementation. These features enable efficient development, test and usage of integrated land-use models. On its system side, SITE provides generic data structures (grid, grid cells, attributes etc.) and takes over the responsibility for their administration. By means of a scripting language (Python) that has been extended by language features specific for land-use modeling, these data structures can be utilized and manipulated by modeling applications. The scripting language interpreter is embedded in SITE. The integration of sub models can be achieved via the scripting language or by usage of a generic interface provided by SITE. Furthermore, functionalities important for land-use modeling like model calibration, model tests and analysis support of simulation results have been integrated into the generic framework. During the implementation of SITE, specific emphasis was laid on expandability, maintainability and usability. Along with the modeling framework a land use model for the analysis of the stability of tropical rainforest margins was developed in the context of the collaborative research project STORMA (SFB 552). In a research area in Central Sulawesi, Indonesia, socio-environmental impacts of land-use changes were examined. SITE was used to simulate land-use dynamics in the historical period of 1981 to 2002. Analogous to that, a scenario that did not consider migration in the population dynamics, was analyzed. For the calculation of crop yields and trace gas emissions, the DAYCENT agro-ecosystem model was integrated. In this case study, it could be shown that land-use changes in the Indonesian research area could mainly be characterized by the expansion of agricultural areas at the expense of natural forest. For this reason, the situation had to be interpreted as unsustainable even though increased agricultural use implied economic improvements and higher farmers' incomes. Due to the importance of model calibration, it was explicitly addressed in the SITE architecture through the introduction of a specific component. The calibration functionality can be used by all SITE applications and enables largely automated model calibration. Calibration in SITE is understood as a process that finds an optimal or at least adequate solution for a set of arbitrarily selectable model parameters with respect to an objective function. In SITE, an objective function typically is a map comparison algorithm capable of comparing a simulation result to a reference map. Several map optimization and map comparison methodologies are available and can be combined. The STORMA land-use model was calibrated using a genetic algorithm for optimization and the figure of merit map comparison measure as objective function. The time period for the calibration ranged from 1981 to 2002. For this period, respective reference land-use maps were compiled. It could be shown, that an efficient automated model calibration with SITE is possible. Nevertheless, the selection of the calibration parameters required detailed knowledge about the underlying land-use model and cannot be automated. In another case study decreases in crop yields and resulting losses in income from coffee cultivation were analyzed and quantified under the assumption of four different deforestation scenarios. For this task, an empirical model, describing the dependence of bee pollination and resulting coffee fruit set from the distance to the closest natural forest, was integrated. Land-use simulations showed, that depending on the magnitude and location of ongoing forest conversion, pollination services are expected to decline continuously. This results in a reduction of coffee yields of up to 18% and a loss of net revenues per hectare of up to 14%. However, the study also showed that ecological and economic values can be preserved if patches of natural vegetation are conservated in the agricultural landscape. -----------------------------------------------------------------------

A technological innovation for capsicums production in Peru: the mechatronics arms

Peru agricultural exports have increased in recent years due to (i) free trade agreements with many countries (United States, Canada, European Union, China, Thailand, Singapore, Japan, Chile, among others), (ii) an increasing international demand for healthy products, (iii) country´s economic development and (iv) more private investments in this sector (Velazco 2012). Also, if we can compare among Peru three main regions (Coast, Andean highlands and the Jungle), It is the Coast (western region) that has a developed agricultural production due to unique weather conditions, private investments, public infrastructure, transport costs and quality of land (Gomez, 2008). This country development is also related to the production of non-traditional products for export like asparagus, artichokes, capsicums, bananas, grapes, among others; produced by agro industrial companies and small farmers and that are mainly labor intensive (Gomez, 2008 and Velazco, 2012). This very successful export diversification and self-discovery process was the result of a combination of strong natural comparative advantages (mainly excellent agro climatic conditions) and a significant innovation effort. It meant the introduction and expansion of new products and markets, the entry of new firms, and experimental research and the adoption of new techniques and process technologies developed abroad (in irrigation, crop management, post-harvesting, sanitary control, storage and packing) to produce high-quality, niche (gourmet) and higher value-added products, in line with consumer trends in sophisticated food markets. In products such as asparagus, mango, organic coffee and capsicums, Peru has become a leading world exporter (OECD). For this reason one of the government main tasks for the next years is to meet urgent agriculture producer’s needs in the areas of technological Innovation and business management (MINAG). In this context, this thesis analyzes the applicability of a new technology – the mechatronic arms – specifically to capsicums production sector in Peru. We chose Capsicums production sector (paprika, chilli pepper) because is mainly labor intensive and is the sector where my family company (DIROSE SAC) operates. This innovation consists in a 40 arms mechatronic combine, and it was first created in order to improve the efficiency on the labor intensive phase of harvest for this kind of agriculture products. It is estimated that a laborer with brief training operating the machine would be equivalent to 40 people that not only would work during daytime, but also on the night shift as well. Also, using this new technology can allow a company to make additional crops that would increase their yields and annual revenues. This thesis was developed as a business plan to make this new product available for other agriculture companies that operates in the capsicums production sector in Peru; however, this new technology has the potential to be modified in order to be available to other kind of agriculture products, in Peru and other countries.

Épocas de plantio de milho em função das deficiências hídricas no solo em Cambará-PR

The aim of this study was to try the forecast of corn (Zea mays L.) sowing dates, the understanding of the quantitative effect of water deficits on that crop and crop yield decrease on a basis of a climatic model of water deficit forecast. This model was applied at Cambara (lat. 23 degrees 00'S, long. 50 circle 02'WGr, altitude 450 m), PR, Brazil. The model estimates yield decrease, in relation to potential values, as a function of the sowing dates which determine flowering and grain filling dates, highly critical times in relation to water deficit. The estimates were done from expected values of water deficit, at the 80% probability level and accumulated degrees-days, using several climatological data. Results show that the first ten days of November are the best corn sowing date under dry or irrigated conditions. Under these same conditions, the worst time showed to be August. Estimates of total needs of supplementar irrigation get values of 126 e 226 mm, respectively to the corn sowed at the first ten days of November and August.

Development of technological capabilities in an extremely volatile economy. The industrial sector in Argentina

Includes bibliography

Mecanismos fisiológicos e bioquímicos da cana-de-açúcar sob estresses induzidos por deficiência hídrica e paraquat

Pós-graduação em Agronomia (Agricultura) - FCA