From field to globe: upscaling of 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. </p

Can Bangladesh produce enough cereals to meet future demand?

Bangladesh faces huge challenges in achieving food security due to its high population, diet changes, and limited room for expanding cropland and cropping intensity. The objective of this study is to assess the degree to which Bangladesh can be self-sufficient in terms of domestic maize, rice and wheat production by the years 2030 and 2050 by closing the existing gap (Yg) between yield potential (Yp) and actual farm yield (Ya), accounting for possible changes in cropland area. Yield potential and yield gaps were calculated for the three crops using well-validated crop models and site-specific weather,management and soil data, and upscaled to the whole country.We assessed potential grain production in the years 2030 and 2050 for six land use change scenarios (general decrease in arable land; declining ground water tables in the north; cropping of fallow areas in the south; effect of sea level rise; increased cropping intensity; and larger share of cash crops) and three levels of Yg closure (1: no yield increase; 2: Yg closure at a level equivalent to 50% (50% Yg closure); 3: Yg closure to a level of 85% of Yp (irrigated crops) and 80% of water-limited yield potential or Yw (rainfed crops) (full Yg closure)). In addition, changes in demand with low and high population growth rates, and substitution of rice by maize in future diets were also examined. Total aggregated demand of the three cereals (in milled rice equivalents) in 2030 and 2050, based on the UN median population variant, is projected to be 21 and 24% higher than in 2010. Current Yg represent 50% (irrigated rice), 48–63% (rainfed rice), 49% (irrigated wheat), 40% (rainfed wheat), 46% (irrigated maize), and 44% (rainfed maize) of their Yp or Yw. With 50% Yg closure and for various land use changes, self-sufficiency ratio will be N1 for rice in 2030 and about one in 2050 but well below one for maize and wheat in both 2030 and 2050. With full Yg closure, self-sufficiency ratios will be well above one for rice and all three cereals jointly but below one for maize and wheat for all scenarios, except for the scenario with drastic decrease in boro rice area to allow for area expansion for cash crops. Full Yg closure of all cereals is needed to compensate for area decreases and demand increases, and then even some maize and large amounts of wheat imports will be required to satisfy demand in future. The results of this analysis have important implications for Bangladesh and other countries with high population growth rate, shrinking arable land due to rapid urbanization, and highly vulnerable to climate change

Community-based governance: implications for ecosystem service supply in Berg en Dal, the Netherlands

Governance is an essential element in land-use decision-making and ecosystem management choices and thus for ecosystem service provisioning. Although a community-based approach, i.e. governance involving actors from all spheres of society (the state, market and civil society), is considered most appropriate for natural resource management, there is a lack of knowledge about its actual effects on environmental outcomes and ecosystem service supply in particular. To obtain insight in the effect of governance on ecosystem service provision in our study region (Berg en Dal, the Netherlands), we constructed ecosystem service maps for the period 1995 to 2012 using land-use maps. Also an inventory of the implemented governance models was created, based on interviews with stakeholders, supplemented with literature research. Our results show that 1) governance in Berg en Dal changed from top-down to more community-based models during the studied period; and 2) that the potential and actual supply of the majority of the investigated regulating, cultural and habitat ecosystem services increased during the studied period, at the expense of agricultural production. The interviewed local stakeholders also indicated that they have the perception that the landscape has improved during the last two decades. Although there is a clear connection between governance and improved ecosystem service supply, more research is needed to further develop causal relationships explaining the indirect effects and non-linear behavior within ecosystem service governance systems.FWN – Publicaties zonder aanstelling Universiteit Leide

Effects of data aggregation on simulations of crop phenology

Policy decisions are often taken at the regional scale, while crop models, supporting these decisions, have been developed for individual locations, expecting location-specific, spatially homogeneous input data. Crop models are able to account for the variation in climatic conditions and management activities and their effects on crop productivity. However, regional applications require consideration of spatial variability in these factors. Several studies have analyzed effects of using spatially aggregated climate data on model outcomes. The effects of spatially aggregated sowing dates on simulations of crop phenological development have not been studied, however. We investigated the impact of spatial aggregation of sowing dates and temperatures on the simulated occurrence of ear emergence and physiological maturity of winter wheat in Germany, using the phenological model of AFRCWHEAT2. We observed time ranges for crop emergence exceeding 90 d, whereas for harvesting this was more than 75 d. Spatial aggregation to 100 km × 100 km reduced this range to less than 30 and 20 d for emergence and harvest, respectively. Differences among selected regions were relatively small, suggesting that non-climatic factors largely determined the spatial variability in sowing dates and consecutive phenological stages. Application of the AFRCWHEAT2 phenology model using location-specific weather data and emergence dates, and an identical crop parameter set across Germany gave similar deviations in all studied regions, suggesting that varietal differences were less important among regions than within regions. Importantly, bias in model outcomes as a result of using aggregated input data was small. Increase in resolution from 100 km to 50 km resulted in slight improvements in the simulations. We conclude that using spatially aggregated weather data and emergence dates to simulate the length of the growing season for winter wheat in Germany is justified for grid cells with a maximum area of 100 km × 100 km and for the model considered here. As spatial variability of sowing dates within a region or country can be large, it is important to obtain information about the representative sowing date for the region

Effects of data aggregation on simulations of crop phenology

Policy decisions are often taken at the regional scale, while crop models, supporting these decisions, have been developed for individual locations, expecting location-specific, spatially homogeneous input data. Crop models are able to account for the variation in climatic conditions and management activities and their effects on crop productivity. However, regional applications require consideration of spatial variability in these factors. Several studies have analyzed effects of using spatially aggregated climate data on model outcomes. The effects of spatially aggregated sowing dates on simulations of crop phenological development have not been studied, however. We investigated the impact of spatial aggregation of sowing dates and temperatures on the simulated occurrence of ear emergence and physiological maturity of winter wheat in Germany, using the phenological model of AFRCWHEAT2. We observed time ranges for crop emergence exceeding 90 d, whereas for harvesting this was more than 75 d. Spatial aggregation to 100 km × 100 km reduced this range to less than 30 and 20 d for emergence and harvest, respectively. Differences among selected regions were relatively small, suggesting that non-climatic factors largely determined the spatial variability in sowing dates and consecutive phenological stages. Application of the AFRCWHEAT2 phenology model using location-specific weather data and emergence dates, and an identical crop parameter set across Germany gave similar deviations in all studied regions, suggesting that varietal differences were less important among regions than within regions. Importantly, bias in model outcomes as a result of using aggregated input data was small. Increase in resolution from 100 km to 50 km resulted in slight improvements in the simulations. We conclude that using spatially aggregated weather data and emergence dates to simulate the length of the growing season for winter wheat in Germany is justified for grid cells with a maximum area of 100 km × 100 km and for the model considered here. As spatial variability of sowing dates within a region or country can be large, it is important to obtain information about the representative sowing date for the region

Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha-1, whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization

Modelling greenhouse gas emissions of cacao production in the Republic of Côte d’Ivoire

The current expansion of cacao cultivation in the Republic of Côte d’Ivoire is associated with deforestation, forest degradation, biodiversity loss and high greenhouse gas (GHG) emissions. Global concerns about emissions that are associated with tropical commodity production are increasing. Consequently, there is a need to change the present cacao-growing practice into a more climate-friendly cultivation system. A more climate-friendly system causes lower GHG emissions, stores a high amount of carbon in its standing biomass and produces high cacao yields. GHG emissions and carbon stocks associated with the present cacao production, as assessed in 509 farmers’ fields, were estimated by using the Perennial GHG model and the Cool Farm Tool. On average, the production of 1 kg cacao beans is associated with an emission of 1.47 kg CO2e. Deforestation contributed largely to GHG emissions, while tree biomass and residue management contributed mainly to carbon storage. The collected data combined with the model simulations revealed that it is feasible to produce relatively high yields while at the same time storing a high amount of carbon in the standing biomass and causing low GHG emissions. The climate-friendliness of cacao production is strongly related to farm management, especially the number of shade trees and management of residues. Calculated emissions related to good agricultural practices were 2.29 kg CO2e per kg cacao beans. The higher emissions due to the use of more agro-inputs and other residue management practices such as recommended burning of residues for sanitary reasons were not compensated for by higher yields. This indicates a need to revisit recommended practices with respect to climate change mitigation objectives