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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

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.

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.

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

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.

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

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.

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 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

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. Het artikel geeft een inleiding tot en een overzicht van de verschillende onderdelen van het MCYFS en met name het CGMS en aanverwante tools en viewers. Het hoofddoel van het CGMS is om een schatting te maken van de invloed van de weersomstandigheden op de gewasgroei en oogst op een regionale schaal (provincies, landen, continenten)

Een belangrijk onderdeel van het MARS oogstvoorspellingssysteem is het zogenaamde CGMS (crop growth monitoring system). CGMS gebruikt een gewasgroeimodel om het effect van bodem, weer en teeltmaatregelen op de groei van het gewas te bepalen. Hiervoor worden relevante gegevens verzameld over Europa. Op basis van deze gegevens simuleert het model WOFOST de gewasgroei. In dit proefschrift wordt op praktische en theoretische gronden beargumenteerd dat de onzekerheid in het weer de bepalende factor is voor onzekerheid in de WOFOST simulaties. Dit komt omdat de weersgegevens afkomstig zijn van een beperkt aantal weerstations over Europa en met deze reeks is het niet mogelijk om de daadwerkelijke ruimtelijke en temporele variabiliteit in het weer te beschrijven

Een belangrijk onderdeel van het MARS oogstvoorspellingssysteem is het zogenaamde CGMS (crop growth monitoring system). CGMS gebruikt een gewasgroeimodel om het effect van bodem, weer en teeltmaatregelen op de groei van het gewas te bepalen. Hiervoor worden relevante gegevens verzameld over Europa. Op basis van deze gegevens simuleert het model WOFOST de gewasgroei. In dit proefschrift wordt op praktische en theoretische gronden beargumenteerd dat de onzekerheid in het weer de bepalende factor is voor onzekerheid in de WOFOST simulaties. Dit komt omdat de weersgegevens afkomstig zijn van een beperkt aantal weerstations over Europa en met deze reeks is het niet mogelijk om de daadwerkelijke ruimtelijke en temporele variabiliteit in het weer te beschrijven