Search

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

The MARS project (Monitoring Agriculture with Remote Sensing) was initiated by the European Commission (EC) in 1988 as a research programme in which three Directorates were involved: DG-Agriculture, Eurostat and the Joint Research Centre (JRC)

Het artikel beschrijft de voornaamste ontwikkelingen in gewasgroeimodellen (WOFOST), hun gebruik in CGMS en geïntegreerde modellering

Over 30 million people in East Africa depend on East African highland bananas for food and income. The bananas are grown with limited additions of nutrients and no irrigation, despite widespread poor soil fertility and regular dry seasons. This thesis describes the effect of increasing rainfall and application of potassium and nitrogen fertilizers on banana growth and yields. In areas that receive less than 1100 mm of rainfall per year, additional rainfall increases yields by 65%. Application of potassium increases yields by 88%, while nitrogen is not required. A framework for computing banana growth and yield in response to the amount of water stored in the soil is described. Where the soil water storage capacity is low, mulching increases yields by 10% but it has no effect in areas where the soil water storage is high. This framework is envisaged to guide improvements in banana management and productivity in East Africa.

Over 30 million people in East Africa depend on East African highland bananas for food and income. The bananas are grown with limited additions of nutrients and no irrigation, despite widespread poor soil fertility and regular dry seasons. This thesis describes the effect of increasing rainfall and application of potassium and nitrogen fertilizers on banana growth and yields. In areas that receive less than 1100 mm of rainfall per year, additional rainfall increases yields by 65%. Application of potassium increases yields by 88%, while nitrogen is not required. A framework for computing banana growth and yield in response to the amount of water stored in the soil is described. Where the soil water storage capacity is low, mulching increases yields by 10% but it has no effect in areas where the soil water storage is high. This framework is envisaged to guide improvements in banana management and productivity in East Africa.

There is demand for a decision support tool to design greenhouse production systems in various climate zones and locations in Africa: the ‘Toolbox Adaptive Greenhouse Systems for Africa’. Selection of a limited number of climate zones that are representative for Africa limits the variation in designs to be evaluated and makes the approach more effective for the Netherlands supply industry. The toolbox will result in a number of greenhouse cultivation systems that are most suitable for a given location, weighing perspectives such as greenhouse type, greenhouse installation, climate, production and economic viability. The toolkit will follow the ‘adaptive greenhouse approach’ in which models for greenhouse, crop and finances are combined. This report provides a basis for the toolbox. An overview of African climates is given, followed by options for greenhouse design which are placed in the context of a number of production systems. The expected future developments and the transitions from current situations to likely future situations are briefly described. Anticipating likely future developments enables an analysis of the potential of a certain farm type, the requirements for further development, and the options for Netherlands involvement. Er is behoefte aan beslissingsondersteuning bij het ontwerpen van kasproductiesystemen in verschillende klimaatzones en locaties in Afrika: de ‘Toolbox Adaptive Greenhouse Systems for Africa’. Selectie van een beperkt aantal klimaatzones die karakteristiek zijn voor Afrika beperkt de variatie in het aantal ontwerpen dat moet worden geëvalueerd en geeft een meer effectieve benadering voor de Nederlandse toeleverende industrie. De toolbox zal resulteren in een aantal kasproductiesystemen die voor een bepaalde locatie het meest geschikt zijn, waarbij verschillende elementen worden gewogen: kastype, kasinstallatie, klimaat, productie en economische levensvatbaarheid. De toolbox zal de ‘adaptive greenhouse approach’ volgen, waarin modellen voor kas, gewas en financiën worden gecombineerd. Dit rapport levert een basis voor de toolbox. Het geeft een overzicht van Afrikaanse klimaten, gevolgd door opties voor kasontwerp die in de context van een aantal productiesystemen geplaatst. De verwachte toekomstige ontwikkelingen en transities van de huidige naar mogelijke toekomstige situaties worden kort beschreven. Anticiperen op verwachte toekomstige ontwikkelingen maakt analyse van een bepaald bedrijfstype, de voorwaarden voor ontwikkeling en de opties voor Nederlandse betrokkenheid mogelijk.

In het kader van IJkakker worden gewassensoren ingezet als monitor voor gewasontwikkeling. IJkakker heeft tot doel sensorgestuurde teeltmaatregelen te ontwikkelen. Het deelproject IJkakkerbemesting onderzoekt de mogelijkheden in de teelt van aardappelpootgoed en zetmeelaardappel. Uit een opname met gewassensoren kan een schatting worden gemaakt van de hoeveelheid stikstof die in een gewas zetmeelaardappel is opgenomen. Op basis van een eenvoudig groeimodel dat uit de temperatuursom de totale N-opname door het gewas berekent vanaf opkomst, kan de streefwaarde voor de N-opname op elke willekeurig tijdstip worden vastgesteld. Als de geschatte N-opname uit de sensordata lager is dan de streefopname op het tijdstip van sensing, wordt een stikstofbijmestgift berekend. Deze proef heeft als doel een stikstofbijmestsysteem voor zetmeelaardappel op basis van gewassensing te ontwikkelen en te vergelijken met een standaard praktijkbemesting en een bestaand N-bijmestsysteem gebaseerd op monstername van bodem en gewas.

There is demand for a decision support tool to design greenhouse production systems in various climate zones and locations in Africa: the ‘Toolbox Adaptive Greenhouse Systems for Africa’. Selection of a limited number of climate zones that are representative for Africa limits the variation in designs to be evaluated and makes the approach more effective for the Netherlands supply industry. The toolbox will result in a number of greenhouse cultivation systems that are most suitable for a given location, weighing perspectives such as greenhouse type, greenhouse installation, climate, production and economic viability. The toolkit will follow the ‘adaptive greenhouse approach’ in which models for greenhouse, crop and finances are combined. This report provides a basis for the toolbox. An overview of African climates is given, followed by options for greenhouse design which are placed in the context of a number of production systems. The expected future developments and the transitions from current situations to likely future situations are briefly described. Anticipating likely future developments enables an analysis of the potential of a certain farm type, the requirements for further development, and the options for Netherlands involvement.

In het kader van IJkakker worden gewassensoren ingezet als monitor voor gewasontwikkeling. IJkakker heeft tot doel sensorgestuurde teeltmaatregelen te ontwikkelen. Het deelproject IJkakkerbemesting onderzoekt de mogelijkheden in de teelt van aardappelpootgoed en zetmeelaardappel. Uit een opname met gewassensoren kan een schatting worden gemaakt van de hoeveelheid stikstof die in een gewas zetmeelaardappel is opgenomen. Op basis van een eenvoudig groeimodel dat uit de temperatuursom de totale N-opname door het gewas berekent vanaf opkomst, kan de streefwaarde voor de N-opname op elke willekeurig tijdstip worden vastgesteld. Als de geschatte N-opname uit de sensordata lager is dan de streefopname op het tijdstip van sensing, wordt een stikstofbijmestgift berekend. Deze proef heeft als doel een stikstofbijmestsysteem voor zetmeelaardappel op basis van gewassensing te ontwikkelen en te vergelijken met een standaard praktijkbemesting en een bestaand N-bijmestsysteem gebaseerd op monstername van bodem en gewas.

CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas.

The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System.

Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

The demand for spatially-explicit predictions of regional crop-yield patterns is increasing. An approach to assess a priori and/or future ranges of alternative scenarios spatial yield patterns at the regional scale is the application of mechanistic crop growth simulation models (CGSMs) (e.g. Launary, 2002). However, two main problems emerge in the application of field-level CGSMs at regional scales. Firstly, the required input data on weather, soils, and management are often not available; and secondly, if they are, generally not at the required level of detail. There are two possible approaches to address the identified problems. One is replacing the CGSM by a metamodel (Kleijnen and Sargent, 2000). The second approach is a simple empirical model (e.g. Lobell et al., 2008). The modelling-approach choices and performances are context dependent. The context conditions that determine the best approach are input data requirements, problem definition, study sub-objective, the scale at which output results are expected, model end-users, and utilization of the output. The selection of the modelling approaches can be considered as one of the most difficult, and often ignored, steps to model crop yield at the regional level. However, a structured, systematic way of modelling-approach selection is lacking. In order to address this issue this thesis aimed to develop a framework for recommendable practices to model regional patterns of crop yield. We reviewed literature for existing approaches that have been used to overcome the problem of data availability for the application of CGSMs at the regional level. Then we used the review to formulate decision rules as to what approach to take under different circumstances. Which of the approaches should be used depends on the following questions: (i) do observations of the input variable allow to estimate semivariograms?; (ii) are there auxiliary data correlated to the target variable?; (iii) do the input variables exhibit spatial correlation?; and (iv) is there spatial correlation in the residuals of the regression that related auxiliary data to the target variable?. Summarized, the selection of possible approaches depends on the data availability, the spatial variability, the temporal variability, the correlations with other variables, the data acquisition methods, the expected accuracy from a particular approach used to describe spatial variability, and the sensitivity of the CGSM to the variable. We also evaluated different procedures (interpolate first, calculate later; calculate first, interpolate later) to simulate regional patterns of crop yields in the Carchi province in Northern Ecuador with field-level CGSMs. We also examined scaling effects that arise from spatial variability in input data by using different supports. Results demonstrated that the order of calculation and interpolation was of major importance, while aggregation had a minor effect on the regional patterns of potato yield. From an uncertainty propagation and variability point of view it is in general preferable to calculate first before interpolation. We compared and evaluated the performance of three different modelling approaches for their capacity to model regional patterns of crop yield for two different cases: potato yields in Carchi and wheat yields in Western Germany. Based on these findings, various criteria for selecting a modelling approach are defined including credibility, relevance, sensitivity, and user friendliness. The empirical model and the metamodel are very easy to use and transparent. However, their application domain is limited to the case study area. The application of the CGSM remains complex and the model functions as a black box. The strength of the CGSMs is that impacts over a wider range of conditions can be simulated, taking into account many factors in a way that would not be possible using empirical models and metamodels (Lobell and Burke, 2010). It can be concluded that the various modelling approaches each have their unique merit. Hence, the different modelling approaches are therefore complementary for the interpretation of the observed patterns. There is not a single optimal solution to modelling agricultural systems to model, e.g. regional yield patterns. Moreover, we analysed the effect of spatial aggregation on the performance of the modelling approaches. The results showed that aggregation of calculated data leads to less variability and increasing linear fits at higher aggregation levels. The spatial variability in the case study area determines how strong this effect is. Finally we conclude: Regional crop yield modelling is very sensitive to the choice of model-type and data used; This sensitivity is usually not specifically addressed and not properly and systematically documented in many studies; The outcomes of such modelling exercises cannot be properly used when the underlying decisions on model and data type and sensitivities are unknown; Without this crucial knowledge regional crop simulation models can be easily misused by non-specialists; Standard decision rules are proposed to document these choices in a standard format allowing cross comparisons of different approaches despite the often strong context dependency of the results.

The demand for spatially-explicit predictions of regional crop-yield patterns is increasing. An approach to assess a priori and/or future ranges of alternative scenarios spatial yield patterns at the regional scale is the application of mechanistic crop growth simulation models (CGSMs) (e.g. Launary, 2002). However, two main problems emerge in the application of field-level CGSMs at regional scales. Firstly, the required input data on weather, soils, and management are often not available; and secondly, if they are, generally not at the required level of detail. There are two possible approaches to address the identified problems. One is replacing the CGSM by a metamodel (Kleijnen and Sargent, 2000). The second approach is a simple empirical model (e.g. Lobell et al., 2008). The modelling-approach choices and performances are context dependent. The context conditions that determine the best approach are input data requirements, problem definition, study sub-objective, the scale at which output results are expected, model end-users, and utilization of the output. The selection of the modelling approaches can be considered as one of the most difficult, and often ignored, steps to model crop yield at the regional level. However, a structured, systematic way of modelling-approach selection is lacking. In order to address this issue this thesis aimed to develop a framework for recommendable practices to model regional patterns of crop yield. We reviewed literature for existing approaches that have been used to overcome the problem of data availability for the application of CGSMs at the regional level. Then we used the review to formulate decision rules as to what approach to take under different circumstances. Which of the approaches should be used depends on the following questions: (i) do observations of the input variable allow to estimate semivariograms?; (ii) are there auxiliary data correlated to the target variable?; (iii) do the input variables exhibit spatial correlation?; and (iv) is there spatial correlation in the residuals of the regression that related auxiliary data to the target variable?. Summarized, the selection of possible approaches depends on the data availability, the spatial variability, the temporal variability, the correlations with other variables, the data acquisition methods, the expected accuracy from a particular approach used to describe spatial variability, and the sensitivity of the CGSM to the variable. We also evaluated different procedures (interpolate first, calculate later; calculate first, interpolate later) to simulate regional patterns of crop yields in the Carchi province in Northern Ecuador with field-level CGSMs. We also examined scaling effects that arise from spatial variability in input data by using different supports. Results demonstrated that the order of calculation and interpolation was of major importance, while aggregation had a minor effect on the regional patterns of potato yield. From an uncertainty propagation and variability point of view it is in general preferable to calculate first before interpolation. We compared and evaluated the performance of three different modelling approaches for their capacity to model regional patterns of crop yield for two different cases: potato yields in Carchi and wheat yields in Western Germany. Based on these findings, various criteria for selecting a modelling approach are defined including credibility, relevance, sensitivity, and user friendliness. The empirical model and the metamodel are very easy to use and transparent. However, their application domain is limited to the case study area. The application of the CGSM remains complex and the model functions as a black box. The strength of the CGSMs is that impacts over a wider range of conditions can be simulated, taking into account many factors in a way that would not be possible using empirical models and metamodels (Lobell and Burke, 2010). It can be concluded that the various modelling approaches each have their unique merit. Hence, the different modelling approaches are therefore complementary for the interpretation of the observed patterns. There is not a single optimal solution to modelling agricultural systems to model, e.g. regional yield patterns. Moreover, we analysed the effect of spatial aggregation on the performance of the modelling approaches. The results showed that aggregation of calculated data leads to less variability and increasing linear fits at higher aggregation levels. The spatial variability in the case study area determines how strong this effect is. Finally we conclude: Regional crop yield modelling is very sensitive to the choice of model-type and data used; This sensitivity is usually not specifically addressed and not properly and systematically documented in many studies; The outcomes of such modelling exercises cannot be properly used when the underlying decisions on model and data type and sensitivities are unknown; Without this crucial knowledge regional crop simulation models can be easily misused by non-specialists; Standard decision rules are proposed to document these choices in a standard format allowing cross comparisons of different approaches despite the often strong context dependency of the results.

Dit rapport beschrijft een verkennende studie voor een methodiek waarin de bodem-nutriëntenhuishouding op dynamische wijze is gekoppeld aan gewasgroei. Voor deze studie is gewasgroei gesimuleerd met het model WOFOST, gekoppeld aan het hydrologisch model SWAP. Dit is uitgebreid met een dynamische koppeling met het biogeochemisch model ANIMO waardoor het mogelijk is terugkoppelingen tussen nutriënten, water en vegetatie te bestuderen. In een casestudie en verschillende modelexperimenten is nagegaan wat het effect is van een beperking in bodem-nutriëntenaanbod op gewasgroei en verdamping. De modelresultaten laten zien dat het veronderstelde effect van stikstoftekort op gewasgroei en verdamping aantoonbaar is en gekwantificeerd kan worden met het huidige modelinstrumentarium. Hierbij is het aanbod van water en nutriënten in detail gemodelleerd. De vraagkant vanuit het gewas is relatief eenvoudig gemodelleerd en vereist nadere studie om operationele toepassingen (bijvoorbeeld schade-berekeningen) mogelijk te maken.

Een 3D-model dat belichtingssituaties in de kas nabootst biedt uitgebreide mogelijkheden om allerlei variaties door te rekenen op effectiviteit en zo de teelt te optimaliseren. Of om praktijkonderzoek gericht te ondersteunen. Binnen het programma Kas als Energiebron heeft Wageningen UR Glastuinbouw, in een door PT en EL&I gefinancierd project, onderzocht welke mogelijkheden een driedimensionaal model van gewassen kan bieden bij tomaat en roos.

De systeembiologie, de wetenschapstak die computermodellen combineert met biologische waarnemingen, groeit wereldwijd onstuimig. Wageningen liep achter, maar speelt nu mee, zegt wiskundige Jaap Molenaar van Wageningen University, onderdeel van Wageningen UR.

De systeembiologie, de wetenschapstak die computermodellen combineert met biologische waarnemingen, groeit wereldwijd onstuimig. Wageningen liep achter, maar speelt nu mee, zegt wiskundige Jaap Molenaar van Wageningen University, onderdeel van Wageningen UR.

De systeembiologie, de wetenschapstak die computermodellen combineert met biologische waarnemingen, groeit wereldwijd onstuimig. Wageningen liep achter, maar speelt nu mee, zegt wiskundige Jaap Molenaar van Wageningen University, onderdeel van Wageningen UR.

Een 3D-model dat belichtingssituaties in de kas nabootst biedt uitgebreide mogelijkheden om allerlei variaties door te rekenen op effectiviteit en zo de teelt te optimaliseren. Of om praktijkonderzoek gericht te ondersteunen. Binnen het programma Kas als Energiebron heeft Wageningen UR Glastuinbouw, in een door PT en EL&I gefinancierd project, onderzocht welke mogelijkheden een driedimensionaal model van gewassen kan bieden bij tomaat en roos.

Er is behoefte om de groeimodellen voor gerbera te verbeteren door de assimilatenverdeling binnen het gewas in relatie tot omgevingsfactoren en blad- en bloemafsplitsing in beeld te brengen. Dit is nodig om te kunnen sturen op maximale bloemproductie met behoud van kwaliteit. Het is niet bekend wat de optimale verhouding is tussen daglengte, tempera - tuur en lichtniveau. Hierbij is de dynamiek van wisselende klimaatcondities in de tijd en de steeds wijzigende status van het gewas een extra complicerende factor. Met een model dat de processen integreert, kan dit beter in beeld gebracht worden. Zo kan bijvoorbeeld het dichttrekken van een scherm nodig zijn voor het korte dag effect, maar nadelig zijn voor planttemperatuur en/of drogestofproductie en op termijn een dunner gewas geven. Dit rapport omhelst deel 1A van het project, een literatuurstudie, waarin een overzicht gemaakt is van bestaande kennis over het effect van klimaatfactoren op groei, fotosynthese, ademhaling, afsplitsing, droge stof verdeling en bladoppervlak. De dynamiek tussen de verschillende klimaatfactoren en de toepassing van bestaand onderzoek voor het model is ook aan de orde gesteld.

Key words: tropical lowland climate, tropical lowland greenhouse, plastic greenhouse, near infrared radiation (NIR) reflecting plastic, greenhouse climate model, determinate tomato, crop growth, development, truss appearance rate, crop simulation model, INTKAM. The goal of this research was to improve greenhouse crop production under tropical lowland conditions. The dynamics of greenhouse climate were analyzed using a simple greenhouse climate model (GCM), while the growth and development of a determinate tomato crop were quantified using the INTKAM greenhouse crop simulation model. By combining the GCM and INTKAM models, ways to improve tomato production under tropical lowland greenhouse were investigated. Model calculations were calibrated and validated with experiments in six prototype greenhouses with three different near infrared (NIR) transmissivities during three periods with different tropical lowland climate characteristics. The greenhouses having high natural ventilation capacity showed a climate closely coupled to the outdoor climate. Greenhouse air temperature TAir was affected more by variation in ventilation and leaf area index than by the applied NIR transmission. Simulation shows that lowering TAir can be achieved by: (i) reducing near infrared radiation (NIR) transmission especially for bigger greenhouses and humid conditions, (ii) increasing ventilation openings and (iii) transpiration cooling, especially under hot and dry conditions. GCM study indicated that naturally ventilated model greenhouses of up to size of 14400 m2 were capable to create TAir close to or lower than outdoor air temperature Tout when the greenhouse crops had leaf area index of higher than 0.5. Crops with low number of trusses produced substantially lower fruit weight than crops with high number of trusses. Determinate tomato clearly exhibit high fruit abortion, with the number of fruits per truss decreasing as truss number increases. This partly can be explained by low source – sink strength ratio during the productive period. Effort to increase tomato production might require adequate crop management aimed at finding the appropriate source – sink balance. Scenario studies revealed that fruit production by a determinate tomato crop can be increased slightly by using zero NIR transmittance plastic film and by planting three crops per year (which is current practice). However, when the number of fruits can be maintained constant through appropriate crop management measures, the production would increase with increasing fruit load and the lengthening production period through fewer plantings per year.

The Netherlands are an important producer and exporter of agricultural products. Changes in climate, markets and policies may have a large impact on the agricultural sector and farmers will need to adapt to these changes. Sector and policy documents have, so far, insufficiently considered the impacts of climate change and increased climate variability on the sector.

The Netherlands are an important producer and exporter of agricultural products. Changes in climate, markets and policies may have a large impact on the agricultural sector and farmers will need to adapt to these changes. Sector and policy documents have, so far, insufficiently considered the impacts of climate change and increased climate variability on the sector.

Key words: tropical lowland climate, tropical lowland greenhouse, plastic greenhouse, near infrared radiation (NIR) reflecting plastic, greenhouse climate model, determinate tomato, crop growth, development, truss appearance rate, crop simulation model, INTKAM. The goal of this research was to improve greenhouse crop production under tropical lowland conditions. The dynamics of greenhouse climate were analyzed using a simple greenhouse climate model (GCM), while the growth and development of a determinate tomato crop were quantified using the INTKAM greenhouse crop simulation model. By combining the GCM and INTKAM models, ways to improve tomato production under tropical lowland greenhouse were investigated. Model calculations were calibrated and validated with experiments in six prototype greenhouses with three different near infrared (NIR) transmissivities during three periods with different tropical lowland climate characteristics. The greenhouses having high natural ventilation capacity showed a climate closely coupled to the outdoor climate. Greenhouse air temperature TAir was affected more by variation in ventilation and leaf area index than by the applied NIR transmission. Simulation shows that lowering TAir can be achieved by: (i) reducing near infrared radiation (NIR) transmission especially for bigger greenhouses and humid conditions, (ii) increasing ventilation openings and (iii) transpiration cooling, especially under hot and dry conditions. GCM study indicated that naturally ventilated model greenhouses of up to size of 14400 m2 were capable to create TAir close to or lower than outdoor air temperature Tout when the greenhouse crops had leaf area index of higher than 0.5. Crops with low number of trusses produced substantially lower fruit weight than crops with high number of trusses. Determinate tomato clearly exhibit high fruit abortion, with the number of fruits per truss decreasing as truss number increases. This partly can be explained by low source – sink strength ratio during the productive period. Effort to increase tomato production might require adequate crop management aimed at finding the appropriate source – sink balance. Scenario studies revealed that fruit production by a determinate tomato crop can be increased slightly by using zero NIR transmittance plastic film and by planting three crops per year (which is current practice). However, when the number of fruits can be maintained constant through appropriate crop management measures, the production would increase with increasing fruit load and the lengthening production period through fewer plantings per year.

Recently, the scale of interest for application of crop growth models has extended to the region or even globe with time frames of 50-100 years. The application at larger scales of a crop growth model originally developed for a small scale without any adaptation might lead to errors and inaccuracies. Moreover, application of crop growth models at large scales usually gives problems with respect to missing data. Knowledge about the required level of modelling detail to accurately represent crop growth processes in crop growth models to be applied at large scales is scarce. In this thesis we analysed simulated potential yields, which resulted from models which apply different levels of detail to represent important crop growth processes. Our results indicated that, after location-specific calibration, models in which the same processes were represented with different levels of detail may perform similarly. Model performance was in general best for models which represented leaf area dynamics with the lowest level of detail. Additionally, the results indicated that the use of a different description of light interception significantly changes model outcomes. Especially the representation of leaf senescence was found to be critical for model performance. Global crop growth models are often used with monthly weather data, while crop growth models were originally developed for daily weather data. We examined the effects of replacing daily weather data with monthly data. Results showed that using monthly weather data may result in higher simulated amounts of biomass. In addition, we found increasing detail in a modelling approach to give higher sensitivity to aggregation of input data. Next, we investigated the impact of the use of spatially aggregated sowing dates and temperatures on the simulated phenology of winter wheat in Germany. We found simulated winter wheat phenology in Germany to be rather similar using either non-aggregated input data or aggregated input data with a 100 km × 100 km resolution. Generation or simulation of input data for crop growth models is often necessary if the model is applied at large scales. We simulated sowing dates of several rainfed crops by assuming farmers to sow either when temperature exceeds a crop-specific threshold or at the onset of the wet season. For a large part of the globe our methodology is capable of simulating reasonable sowing dates. To simulate the end of the cropping period (i.e. harvesting dates) we developed simple algorithms to generate unknown crop- and location-specific phenological parameters. In the main cropping regions of wheat the simulated lengths corresponded well with the observations; our methodology worked less well for maize (over- and underestimations of 0.5 to 1.5 month). Importantly, our evaluation of possible consequences for simulated yields related to uncertainties in simulated sowing and harvesting dates showed that simulated yields are rather similar using either simulated or observed sowing and harvesting dates (a maximum difference of 20%), indicating the applicability of our methodology in crop productivity assessments. The thesis concludes with a discussion on a proposed structure of a global crop growth model which is expected to simulate reasonable potential yields at the global scale if only monthly aggregates of climate data at a 0.5° × 0.5° grid are available. The proposed model consists of a forcing function, defined in terms of sigmoidal and quadratic functions to represent light interception, combined with the radiation use efficiency approach, and phenology determining the allocation of biomass to the organs of the crop. Within the model sowing dates and phenological cultivar characteristics are simulated. Based on the proposed model the thesis finally derives directions for future research to further enhance global crop growth modelling.

Recently, the scale of interest for application of crop growth models has extended to the region or even globe with time frames of 50-100 years. The application at larger scales of a crop growth model originally developed for a small scale without any adaptation might lead to errors and inaccuracies. Moreover, application of crop growth models at large scales usually gives problems with respect to missing data. Knowledge about the required level of modelling detail to accurately represent crop growth processes in crop growth models to be applied at large scales is scarce. In this thesis we analysed simulated potential yields, which resulted from models which apply different levels of detail to represent important crop growth processes. Our results indicated that, after location-specific calibration, models in which the same processes were represented with different levels of detail may perform similarly. Model performance was in general best for models which represented leaf area dynamics with the lowest level of detail. Additionally, the results indicated that the use of a different description of light interception significantly changes model outcomes. Especially the representation of leaf senescence was found to be critical for model performance. Global crop growth models are often used with monthly weather data, while crop growth models were originally developed for daily weather data. We examined the effects of replacing daily weather data with monthly data. Results showed that using monthly weather data may result in higher simulated amounts of biomass. In addition, we found increasing detail in a modelling approach to give higher sensitivity to aggregation of input data. Next, we investigated the impact of the use of spatially aggregated sowing dates and temperatures on the simulated phenology of winter wheat in Germany. We found simulated winter wheat phenology in Germany to be rather similar using either non-aggregated input data or aggregated input data with a 100 km × 100 km resolution. Generation or simulation of input data for crop growth models is often necessary if the model is applied at large scales. We simulated sowing dates of several rainfed crops by assuming farmers to sow either when temperature exceeds a crop-specific threshold or at the onset of the wet season. For a large part of the globe our methodology is capable of simulating reasonable sowing dates. To simulate the end of the cropping period (i.e. harvesting dates) we developed simple algorithms to generate unknown crop- and location-specific phenological parameters. In the main cropping regions of wheat the simulated lengths corresponded well with the observations; our methodology worked less well for maize (over- and underestimations of 0.5 to 1.5 month). Importantly, our evaluation of possible consequences for simulated yields related to uncertainties in simulated sowing and harvesting dates showed that simulated yields are rather similar using either simulated or observed sowing and harvesting dates (a maximum difference of 20%), indicating the applicability of our methodology in crop productivity assessments. The thesis concludes with a discussion on a proposed structure of a global crop growth model which is expected to simulate reasonable potential yields at the global scale if only monthly aggregates of climate data at a 0.5° × 0.5° grid are available. The proposed model consists of a forcing function, defined in terms of sigmoidal and quadratic functions to represent light interception, combined with the radiation use efficiency approach, and phenology determining the allocation of biomass to the organs of the crop. Within the model sowing dates and phenological cultivar characteristics are simulated. Based on the proposed model the thesis finally derives directions for future research to further enhance global crop growth modelling.

Er is behoefte om de groeimodellen voor gerbera te verbeteren door de assimilatenverdeling binnen het gewas in relatie tot omgevingsfactoren en blad- en bloemafsplitsing in beeld te brengen. Dit is nodig om te kunnen sturen op maximale bloemproductie met behoud van kwaliteit. Het is niet bekend wat de optimale verhouding is tussen daglengte, tempera - tuur en lichtniveau. Hierbij is de dynamiek van wisselende klimaatcondities in de tijd en de steeds wijzigende status van het gewas een extra complicerende factor. Met een model dat de processen integreert, kan dit beter in beeld gebracht worden. Zo kan bijvoorbeeld het dichttrekken van een scherm nodig zijn voor het korte dag effect, maar nadelig zijn voor planttemperatuur en/of drogestofproductie en op termijn een dunner gewas geven. Dit rapport omhelst deel 1A van het project, een literatuurstudie, waarin een overzicht gemaakt is van bestaande kennis over het effect van klimaatfactoren op groei, fotosynthese, ademhaling, afsplitsing, droge stof verdeling en bladoppervlak. De dynamiek tussen de verschillende klimaatfactoren en de toepassing van bestaand onderzoek voor het model is ook aan de orde gesteld.

A key objective of the AgriAdapt project is the development of methodologies to assess climatic change impacts on agriculture including adaptation at regional and farm type level in combination with market changes. More specifically, the methodologies should enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions including increasing climate variability but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale.

A key objective of the AgriAdapt project is the development of methodologies to assess climatic change impacts on agriculture including adaptation at regional and farm type level in combination with market changes. More specifically, the methodologies should enable (a) the assessment of impacts, risks and resiliencies for agriculture under changes in climatic conditions including increasing climate variability but also under changes of other drivers (market, technology, policy, etc.) and (b) the evaluation of adaptation strategies at farm type and regional scale.

One third of the land surface is covered with natural and cultivated grasslands. Most of these grasslands are intensively or extensively exploited by humans to feed animals. With growing wealth, causing an increase of meat consumption, there is a need to better understand the processes that influence the grass production of these ecosystems. The project aims to improve the knowledge basis regarding grassland productivity and the relationship between management of grasslands and productivity. The research will led to modification of the Dynamic global vegetation model with natural and managed land (LPJmL, version 3.4.018, 2010) for the simulation of grassland and grassland management. Crop growth models such as LPJmL can help to clarify and understand grass production processes. A checked and calibrated model gives useful insights in the carrying capacity of grasslands and enables us to estimate the risk for environmental damage with increase of grass and/or meat production.

One third of the land surface is covered with natural and cultivated grasslands. Most of these grasslands are intensively or extensively exploited by humans to feed animals. With growing wealth, causing an increase of meat consumption, there is a need to better understand the processes that influence the grass production of these ecosystems. The project aims to improve the knowledge basis regarding grassland productivity and the relationship between management of grasslands and productivity. The research will led to modification of the Dynamic global vegetation model with natural and managed land (LPJmL, version 3.4.018, 2010) for the simulation of grassland and grassland management. Crop growth models such as LPJmL can help to clarify and understand grass production processes. A checked and calibrated model gives useful insights in the carrying capacity of grasslands and enables us to estimate the risk for environmental damage with increase of grass and/or meat production.

Door de groei van het gewas te analyseren kan de teler veel opsteken voor de toekomst. Om cirkelredeneringen te voorkomen is het nodig steeds factoren uit te splitsen. Voorwaarde is dat deze factoren gezamenlijk de verklaring opleveren en elkaar niet overlappen. Zo is een eerlijke vergelijking met de prestaties van voorgaande jaren of met die van collega's mogelijk. In het verhaal zijn voorbeelden uitgewerkt van de manier van denken

This study aims to quantify the gap between current and potential yields of major crops in the world, and the production constraints that contribute to this yield gap. Using an expert-based evaluation of yield gaps and the literature, global and regional yields and yield trends of major crops are quantified, yield gaps evaluated by crop experts, current yield progress by breeding estimated, and different yield projections compared. Results show decreasing yield growth for wheat and rice, but still high growth rates for maize. The yield gap analysis provides quantitative estimates of the production constraints for a number of crops and regions and reveals the difficulty to measure and compare yield potentials and actual yields consistently under a range of environmental conditions, and it shows the difficulty to disentangle interacting production constraints. FAO yield growth projections are generally lower than what possibly could be gained by closing current yield gaps.

Het artikel beschrijft de voornaamste ontwikkelingen in gewasgroeimodellen (WOFOST), hun gebruik in CGMS en geïntegreerde modellering

Door de groei van het gewas te analyseren kan de teler veel opsteken voor de toekomst. Om cirkelredeneringen te voorkomen is het nodig steeds factoren uit te splitsen. Voorwaarde is dat deze factoren gezamenlijk de verklaring opleveren en elkaar niet overlappen. Zo is een eerlijke vergelijking met de prestaties van voorgaande jaren of met die van collega's mogelijk. In het verhaal zijn voorbeelden uitgewerkt van de manier van denken

The main purpose of Crop Growth Monitoring System CGMS is to estimate the influence of weather conditions on crop growth and yield on regional scale (provinces, countries, continents). Therefore, CGMS combines aspects of both weather data processing and collection as well as modelling crop growth and development.

Supportive irrigation is practiced in the Netherlands to overcome drought spells in the summer season. In the south-west delta mainly surface water is used of which the salinity is likely to increase. This study investigates the effects of saline irrigation on potatoes, sugar beet, grass, and tulips for different soils using a modeling approach. A comparison was made with a previous study showing the importance of climate and soils on crop reaction when applying supportive irrigation with variable salinities. It was found that the internationally accepted concept of Maas and Hoffman to estimate crop damage due to salts is not sufficiently reliable to establish salinity norms under conditions prevailing in the Netherlands. Recommendations are given for trade-offs between drought and salt damage, modeling improvements, and experimental field research.

The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System. De weers- en gewasindicatoren en oogstvoorspellingen worden opgeslagen in een ORACLE spatial database. Om deze indicatorwaarden zowel ruimtelijk als temporeel te kunnen analyseren, wordt een geavanceerde viewer ontwikkeld die de huidige MARSOP website zal vervangen. Met de viewer kunnen weersomstandigheden en gewasproductie van het huidige jaar geanalyseerd worden in relatie met het langjarig gemiddelde of andere specifieke jaren. Deze viewer wordt samen met andere IT aspecten van het CGMS besproken

Nearly three decades ago Farquhar, von Caemmerer and Berry published a biochemical model for C 3 photosynthetic rates (the FvCB model). The model predicts net photosynthesis ( A ) as the minimum of the Rubisco-limited rate of CO 2 assimilation ( A c ) and the electron transport-limited rate of CO 2 assimilation ( A j ). Given its simplicity and the growing availability of the required enzyme kinetic constants, the FvCB model has been used for a wide range of studies, from analysing underlying C 3 leaf biochemistry to predicting photosynthetic fluxes of ecosystems in response to global warming. However, surprisingly, this model has seen limited use in existing crop growth models. Here we highlight the elegance, simplicity, and robustness of this model. In the light of some uncertainties with photosynthetic electron transport pathways, a recently extended FvCB model to calculate A j is summarized. Applying the FvCB-type model in crop growth models for predicting leaf photosynthesis requires a stomatal conductance ( g s ) model to be incorporated, so that intercellular CO 2 concentration ( C i ) can be estimated. In recent years great emphasis has been put on the significant drawdown of Rubisco carboxylation-site CO 2 concentration ( C c ) relative to C i . To account for this drawdown, mesophyll conductance ( g m ) for CO 2 transfer can be added. We present an analytical algorithm that incorporates a g s model and uses g m as a temperature-dependent parameter for calculating A under various environmental scenarios. Finally we discuss a C 4 -equivalent version of the FvCB model. In addition to the algorithms already elaborated for C 3 photosynthesis, most important algorithms for C 4 photosynthesis are those that capture the CO 2 concentrating mechanism and the extra ATP requirement by the C 4 cycle. Although the current estimation of the C 4 enzyme kinetic constants is less certain, applying FvCB-type models to both C 3 and C 4 crops is recommended to accurately predict the response of crop photosynthesis to multiple, interactive environmental variables.

Het artikel beschrijft de voornaamste ontwikkelingen in gewasgroeimodellen (WOFOST), hun gebruik in CGMS en geïntegreerde modellering

CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas.

Supportive irrigation is practiced in the Netherlands to overcome drought spells in the summer season. In the south-west delta mainly surface water is used of which the salinity is likely to increase. This study investigates the effects of saline irrigation on potatoes, sugar beet, grass, and tulips for different soils using a modeling approach. A comparison was made with a previous study showing the importance of climate and soils on crop reaction when applying supportive irrigation with variable salinities. It was found that the internationally accepted concept of Maas and Hoffman to estimate crop damage due to salts is not sufficiently reliable to establish salinity norms under conditions prevailing in the Netherlands. Recommendations are given for trade-offs between drought and salt damage, modeling improvements, and experimental field research.

CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas. Naast de operationele toepassing van het CGMS worden verschillende mogelijkheden tot verbetering bestudeerd en geïmplementeerd. Deze mogelijke verbeteringen variëren van uitbreiding naar andere continenten, het incorporeren van korte- en langetermijn-weersvoorspellingen, een verbeterde bodemwatermodule en het gebruik van remote sensing data. Op dit moment wordt er, met betrekking tot de remote sensing data, gekeken hoe de onzekerheid in het systeem door een probabilistische aanpak en assimilatie van remote sensing data geïncorporeerd kan worden

This study aims to quantify the gap between current and potential yields of major crops in the world, and the production constraints that contribute to this yield gap. Using an expert-based evaluation of yield gaps and the literature, global and regional yields and yield trends of major crops are quantified, yield gaps evaluated by crop experts, current yield progress by breeding estimated, and different yield projections compared. Results show decreasing yield growth for wheat and rice, but still high growth rates for maize. The yield gap analysis provides quantitative estimates of the production constraints for a number of crops and regions and reveals the difficulty to measure and compare yield potentials and actual yields consistently under a range of environmental conditions, and it shows the difficulty to disentangle interacting production constraints. FAO yield growth projections are generally lower than what possibly could be gained by closing current yield gaps.

The Crop Growth Monitoring System (CGMS) provides operational services and analysis tools to the Joint Research Centre of the European Commission (JRC) in the area of crop monitoring and crop yield forecast, as part the MARS Crop Yield Forecasting System.

The MARS project (Monitoring Agriculture with Remote Sensing) was initiated by the European Commission (EC) in 1988 as a research programme in which three Directorates were involved: DG-Agriculture, Eurostat and the Joint Research Centre (JRC)

The MARS project (Monitoring Agriculture with Remote Sensing) was initiated by the European Commission (EC) in 1988 as a research programme in which three Directorates were involved: DG-Agriculture, Eurostat and the Joint Research Centre (JRC) De geschiedenis van het CGMS in het MARS project wordt beschreven. Het MARS project is oorspronkelijk opgezet, omdat er steeds meer overproductie van de voornaamste gewassen plaatsvond (granen, oliezaden en suikerbieten), waarvoor op productiebasis subsidies betaald moesten worden aan de agrarische sector. Zodoende wilde de Europese Commissie een waarschuwingssysteem, dat schattingen kon geven van de regionale en nationale gewasproductie gedurende het groeiseizoen