Plant Physiological Models Research Articles

Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.

This study investigates the influence of cadmium (Cd) stress on the micropropagation of Goji Berry (Lycium barbarum L.) across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Gaussian Process (GP), and Extreme Gradient Boosting (XGBoost). The primary motivation is to elucidate genotype-specific responses to Cd stress, which poses significant challenges to agricultural productivity and food safety due to its toxicity. By analyzing the impacts of varying Cd concentrations on plant growth parameters such as proliferation, shoot and root lengths, and root numbers, we aim to develop predictive models that can optimize plant growth under adverse conditions. The ML models revealed complex relationships between Cd exposure and plant physiological changes, with MLP and RF models showing remarkable prediction accuracy (R2 values up to 0.98). Our findings contribute to understanding plant responses to heavy metal stress and offer practical applications in mitigating such stress in plants, demonstrating the potential of ML approaches in advancing plant tissue culture research and sustainable agricultural practices.

Using plant physiological stable oxygen isotope models to counter food fraud

Fraudulent food products, especially regarding false claims of geographic origin, impose economic damages of $30–$40 billion per year. Stable isotope methods, using oxygen isotopes (δ18O) in particular, are the leading forensic tools for identifying these crimes. Plant physiological stable oxygen isotope models simulate how precipitation δ18O values and climatic variables shape the δ18O values of water and organic compounds in plants. These models have the potential to simplify, speed up, and improve conventional stable isotope applications and produce temporally resolved, accurate, and precise region-of-origin assignments for agricultural food products. However, the validation of these models and thus the best choice of model parameters and input variables have limited the application of the models for the origin identification of food. In our study we test model predictions against a unique 11-year European strawberry δ18O reference dataset to evaluate how choices of input variable sources and model parameterization impact the prediction skill of the model. Our results show that modifying leaf-based model parameters specifically for fruit and with product-independent, but growth time specific environmental input data, plant physiological isotope models offer a new and dynamic method that can accurately predict the geographic origin of a plant product and can advance the field of stable isotope analysis to counter food fraud.

Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science.

For decades, the dynamic nature of chlorophyll a fluorescence (ChlaF) has provided insight into the biophysics and ecophysiology of the light reactions of photosynthesis from the subcellular to leaf scales. Recent advances in remote sensing methods enable detection of ChlaF induced by sunlight across a range of larger scales, from using instruments mounted on towers above plant canopies to Earth-orbiting satellites. This signal is referred to as solar-induced fluorescence (SIF) and its application promises to overcome spatial constraints on studies of photosynthesis, opening new research directions and opportunities in ecology, ecophysiology, biogeochemistry, agriculture and forestry. However, to unleash the full potential of SIF, intensive cross-disciplinary work is required to harmonize these new advances with the rich history of biophysical and ecophysiological studies of ChlaF, fostering the development of next-generation plant physiological and Earth-system models. Here, we introduce the scale-dependent link between SIF and photosynthesis, with an emphasis on seven remaining scientific challenges, and present a roadmap to facilitate future collaborative research towards new applications of SIF.

Retrieving high-resolution surface photosynthetically active radiation from the MODIS and GOES-16 ABI data

Incident photosynthetically active radiation (PAR) is a key parameter in plant physiological, biological and physical process-based terrestrial ecosystem models. Deriving highly accurate and spatially continuous PAR from remote sensing data can fill in the gaps of insufficient ground-based measurements. In this study, we provide an efficient approach for estimating PAR by employing look-up table (LUT) method using observations from both MODIS and GOES-16. The GOES-16 is the first satellite of the new-generation US Geostationary Operational Environmental Satellite R-series (GOES-R), which can capture dynamic changes in the atmosphere every 5 min. First, the diurnal variation of surface bidirectional reflectance is characterized assuming that the surface characteristics and solar zenith angles in tropical and middle latitudes at a given time of day do not change dramatically within a month. Next, the aerosol optical depth (AOD) and cloud optical depth (COD) are retrieved from the GOES-16 visible observations under both clear-sky and cloudy conditions. Finally, PAR is estimated via the retrieved AOD and COD using the LUT method and a MODIS-based surface albedo product. The estimated PAR was compared against ground-based measurements collected from seven SURFace RADiation budget network (SURFRAD) sites and 23 National Ecological Observatory Network (NEON) sites at instantaneous, hourly and daily scales. The instantaneous PAR validation results achieved R2 values of 0.97 and 0.82, and root mean square errors (RMSEs) of 18.1 and 51.1 W m‐2, for clear and cloudy skies, respectively. The RMSEs of the hourly averaged PAR were reduced to 16.8 W m−2 and 40.4 W m−2, respectively, for clear-sky and cloudy conditions. The daily mean PAR evaluation for all sky conditions shows an overall mean bias error (MBE) of 0.97 W m−2 and an RMSE of 10.5 W m−2. Compared with other global PAR datasets, the estimated PAR values in this study have lower RMSEs and higher spatial-temporal resolution. Uncertainties in the retrieved PAR caused by topography and solar-cloud-geometry effects (SCGE) can be reduced by spatial and temporal averaging. Based on an evaluation of retrievals at different spatial-temporal scales, we recommend that a 20 × 20 km2 domain-averaged PAR be utilized for instantaneous PAR validation, whereas hourly and daily averaged PAR values are nearly independent of spatial scale.

Climate and lawn management interact to control C4 plant distribution in residential lawns across seven U.S. cities.

In natural grasslands, C4 plant dominance increases with growing season temperatures and reflects distinct differences in plant growth rates and water use efficiencies of C3 vs. C4 photosynthetic pathways. However, in lawns, management decisions influence interactions between planted turfgrass and weed species, leading to some uncertainty about the degree of human vs. climatic controls on lawn species distributions. We measured herbaceous plant carbon isotope ratios (δ13 C, index of C3 /C4 relative abundance) and C4 cover in residential lawns across seven U.S. cities to determine how climate, lawn plant management, or interactions between climate and plant management influenced C4 lawn cover. We also calculated theoretical C4 carbon gain predicted by a plant physiological model as an index of expected C4 cover due to growing season climatic conditions in each city. Contrary to theoretical predictions, plant δ13 C and C4 cover in urban lawns were more strongly related to mean annual temperature than to growing season temperature. Wintertime temperatures influenced the distribution of C4 lawn turf plants, contrary to natural ecosystems where growing season temperatures primarily drive C4 distributions. C4 cover in lawns was greatest in the three warmest cities, due to an interaction between climate and homeowner plant management (e.g., planting C4 turf species) in these cities. The proportion of C4 lawn species was similar to the proportion of C4 species in the regional grass flora. However, the majority of C4 species were nonnative turf grasses, and not of regional origin. While temperature was a strong control on lawn species composition across the United States, cities differed as to whether these patterns were driven by cultivated lawn grasses vs. weedy species. In some cities, biotic interactions with weedy plants appeared to dominate, while in other cities, C4 plants were predominantly imported and cultivated. Elevated CO2 and temperature in cities can influence C3 /C4 competitive outcomes; however, this study provides evidence that climate and plant management dynamics influence biogeography and ecology of C3 /C4 plants in lawns. Their differing water and nutrient use efficiency may have substantial impacts on carbon, water, energy, and nutrient budgets across cities.

Applying the principles of isotope analysis in plant and animal ecology to forensic science in the Americas.

The heart of forensic science is application of the scientific method and analytical approaches to answer questions central to solving a crime: Who, What, When, Where, and How. Forensic practitioners use fundamentals of chemistry and physics to examine evidence and infer its origin. In this regard, ecological researchers have had a significant impact on forensic science through the development and application of a specialized measurement technique-isotope analysis-for examining evidence. Here, we review the utility of isotope analysis in forensic settings from an ecological perspective, concentrating on work from the Americas completed within the last three decades. Our primary focus is on combining plant and animal physiological models with isotope analyses for source inference. Examples of the forensic application of isotopes-including stable isotopes, radiogenic isotopes, and radioisotopes-span from cotton used in counterfeit bills to anthrax shipped through the U.S. Postal Service and from beer adulterated with cheap adjuncts to human remains discovered in shallow graves. Recent methodological developments and the generation of isotope landscapes, or isoscapes, for data interpretation promise that isotope analysis will be a useful tool in ecological and forensic studies for decades to come.

A dual-biomarker approach for quantification of changes in relative humidity from sedimentary lipid <i>D</i>∕<i>H</i> ratios

Abstract. Past climatic change can be reconstructed from sedimentary archives by a number of proxies. However, few methods exist to directly estimate hydrological changes and even fewer result in quantitative data, impeding our understanding of the timing, magnitude and mechanisms of hydrological changes. Here we present a novel approach based on δ2H values of sedimentary lipid biomarkers in combination with plant physiological modeling to extract quantitative information on past changes in relative humidity. Our initial application to an annually laminated lacustrine sediment sequence from western Europe deposited during the Younger Dryas cold period revealed relative humidity changes of up to 15 % over sub-centennial timescales, leading to major ecosystem changes, in agreement with palynological data from the region. We show that by combining organic geochemical methods and mechanistic plant physiological models on well characterized lacustrine archives it is possible to extract quantitative ecohydrological parameters from sedimentary lipid biomarker δ2H data.

Quantifying energy and water fluxes in dry dune ecosystems of the Netherlands

Abstract. Coastal and inland dunes provide various ecosystem services that are related to groundwater, such as drinking water production and biodiversity. To manage groundwater in a sustainable manner, knowledge of actual evapotranspiration (ETa) for the various land covers in dunes is essential. Aiming at improving the parameterization of dune vegetation in hydrometeorological models, this study explores the magnitude of energy and water fluxes in an inland dune ecosystem in the Netherlands. Hydrometeorological measurements were used to parameterize the Penman–Monteith evapotranspiration model for four different surfaces: bare sand, moss, grass and heather. We found that the net longwave radiation (Rnl) was the largest energy flux for most surfaces during daytime. However, modeling this flux by a calibrated FAO-56 Rnl model for each surface and for hourly time steps was unsuccessful. Our Rnl model, with a novel submodel using solar elevation angle and air temperature to describe the diurnal pattern in radiative surface temperature, improved Rnl simulations considerably. Model simulations of evaporation from moss surfaces showed that the modulating effect of mosses on the water balance is species-dependent. We demonstrate that dense moss carpets (Campylopus introflexus) evaporate more (5 %, +14 mm) than bare sand (total of 258 mm in 2013), while more open-structured mosses (Hypnum cupressiforme) evaporate less (−30 %, −76 mm) than bare sand. Additionally, we found that a drought event in the summer of 2013 showed a pronounced delayed signal on lysimeter measurements of ETa for the grass and heather surfaces, respectively. Due to the desiccation of leaves after the drought event, and their feedback on the surface resistance, the potential evapotranspiration in the year 2013 dropped by 9 % (−37 mm) and 10 % (−61 mm) for the grass and heather surfaces, respectively, which subsequently led to lowered ETa of 8 % (−29 mm) and 7 % (−29 mm). These feedbacks are of importance for water resources, especially during a changing climate with an increasing number of drought days. Therefore, such feedbacks need to be integrated into a coupled plant physiological and hydrometeorological model to accurately simulate ETa. In addition, our study showed that groundwater recharge in dunes can be increased considerably by promoting moss vegetation, especially of open-structured moss species.

Sapling performance along resource gradients drives tree species distributions within and across tropical forests

Niche differentiation is a major hypothesized determinant of species distributions, but its practical importance is heavily debated and its underlying mechanisms are poorly understood. Trait‐based approaches have been used to infer niche differentiation and predict species distributions. For understanding underlying mechanisms, individual traits should be scaled up to whole‐plant performance, which has rarely been done.We measured seven key traits that are important for carbon and water balance for 37 tropical tree species. We used a process‐based plant physiological model to simulate the carbon budget of saplings along gradients of light and water availability, and quantified the performance of the species in terms of their light compensation points (a proxy for shade tolerance), water compensation points (proxy for drought tolerance), and maximum carbon gain rates (proxy for potential growth rate). We linked species performances to their observed distributions (the realized niches) at two spatial scales in Bolivian lowland forests: along a canopy openness gradient at local scale (∼1 km2) and along a rainfall gradient (1100–2200 mm/yr) at regional (∼1000 km) scale.We show that the water compensation point was the best predictor of species distributions along water and light resource gradients within and across tropical forests. A sensitivity analysis suggests that the stomatal regulation of minimum leaf water potentials, rather than stem hydraulic traits (sapwood area and specific conductivity), contributed to the species differences in the water compensation point of saplings. The light compensation point and maximum carbon gain, both driven by leaf area index and leaf nitrogen concentration, also contributed to differential species distributions at the local scale, but not or only marginally at the regional scale. Trait‐and‐physiology‐based simulations of whole‐plant performance thus help to evaluate the possible roles of individual traits in physiological processes underlying species performance along environmental gradients. The development of such whole‐plant concepts will improve our ability to understand responses of plant communities to shifts in resource availability and stress under global change.

Coupling a physiological grazer population model with a generalized model for vegetation dynamics

Large grazers have played a fundamental role in grassland and savannah ecosystems since these vegetation types formed in the late Miocene, but the feedback loops between vegetation and large grazers are still not well understood. Modern dynamic global vegetation models (DGVMs) lack the explicit impact of herbivory, but are calibrated to benchmarks including herbivory. We coupled a generalized model for the population dynamics of large mammalian grazers, based on animal physiology, with a plant-physiological model for vegetation dynamics and ecosystem processes, applicable at local to global scales (LPJ-GUESS). To our knowledge, this is the first attempt to combine process-based grazer population and vegetation modelling in a single generalized modelling framework, applicable at regional to continental scales. The capability of the coupled model to reproduce real-world grazer densities was tested by comparing modelled biomass densities with empirical data from African game parks, where semi-natural grazer populations still exist. The model reproduced inter-park differences in long-term average grazer biomass densities and yielded similar dependencies between major environmental drivers (e.g. precipitation, annual net primary productivity (NPP), dry season length) and grazer population densities as found in other more empirical studies. Amongst the potential environmental drivers, modelled NPP and dry season length were most strongly correlated with empirical and modelled biomass densities. Major discrepancies between modelled and empirical densities occurred for individual parks, but this was expected because the model did not include all factors that influence grazer populations (e.g. nutrient dynamics and poaching). The generalized flexible framework of the coupled model makes it possible to apply the model to other regions, to include further processes (if data for parameterizing them is available) and to parameterize other types of grazers. It could become a useful tool for investigating interactions between grazers and vegetation in a process-based framework.

New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity

Our ability to close the Earth's carbon budget and predict feedbacks in a warming climate depends critically on knowing where, when and how carbon dioxide is exchanged between the land and atmosphere. Terrestrial gross primary production (GPP) constitutes the largest flux component in the global carbon budget, however significant uncertainties remain in GPP estimates and its seasonality. Empirically, we show that global spaceborne observations of solar induced chlorophyll fluorescence – occurring during photosynthesis – exhibit a strong linear correlation with GPP. We found that the fluorescence emission even without any additional climatic or model information has the same or better predictive skill in estimating GPP as those derived from traditional remotely-sensed vegetation indices using ancillary data and model assumptions. In boreal summer the generally strong linear correlation between fluorescence and GPP models weakens, attributable to discrepancies in savannas/croplands (18–48% higher fluorescence-based GPP derived by simple linear scaling), and high-latitude needleleaf forests (28–32% lower fluorescence). Our results demonstrate that retrievals of chlorophyll fluorescence provide direct global observational constraints for GPP and open an entirely new viewpoint on the global carbon cycle. We anticipate that global fluorescence data in combination with consolidated plant physiological fluorescence models will be a step-change in carbon cycle research and enable an unprecedented robustness in the understanding of the current and future carbon cycle.

Cellulose (delta)18O is an index of leaf-to-air vapor pressure difference (VPD) in tropical plants.

Cellulose in plants contains oxygen that derives in most cases from precipitation. Because the stable oxygen isotope composition, δ(18)O, of precipitation is associated with environmental conditions, cellulose δ(18)O should be as well. However, plant physiological models using δ(18)O suggest that cellulose δ(18)O is influenced by a complex mix of both climatic and physiological drivers. This influence complicates the interpretation of cellulose δ(18)O values in a paleo-context. Here, we combined empirical data analyses with mechanistic model simulations to i) quantify the impacts that the primary climatic drivers humidity (e(a)) and air temperature (T(air)) have on cellulose δ(18)O values in different tropical ecosystems and ii) determine which environmental signal is dominating cellulose δ(18)O values. Our results revealed that e(a) and T(air) equally influence cellulose δ(18)O values and that distinguishing which of these factors dominates the δ(18)O values of cellulose cannot be accomplished in the absence of additional environmental information. However, the individual impacts of e(a) and T(air) on the δ(18)O values of cellulose can be integrated into a single index of plant-experienced atmospheric vapor demand: the leaf-to-air vapor pressure difference (VPD). We found a robust relationship between VPD and cellulose δ(18)O values in both empirical and modeled data in all ecosystems that we investigated. Our analysis revealed therefore that δ(18)O values in plant cellulose can be used as a proxy for VPD in tropical ecosystems. As VPD is an essential variable that determines the biogeochemical dynamics of ecosystems, our study has applications in ecological-, climate-, or forensic-sciences.

Critical soil conditions for oxygen stress to plant roots: Substituting the Feddes-function by a process-based model

Effects of insufficient soil aeration on the functioning of plants form an important field of research. A well-known and frequently used utility to express oxygen stress experienced by plants is the Feddes-function. This function reduces root water uptake linearly between two constant pressure heads, representing threshold values for minimum and maximum oxygen deficiency. However, the correctness of this expression has never been evaluated and constant critical values for oxygen stress are likely to be inappropriate. In this paper, we propose a fundamentally different approach to assess oxygen stress: we built a plant physiological and soil physical process-based model to calculate the minimum gas filled porosity of the soil at which oxygen stress occurs. Effects of insufficient soil aeration on the functioning of plants form an important field of research. A well-known and frequently used utility to express oxygen stress experienced by plants is the Feddes-function. This function reduces root water uptake linearly between two constant pressure heads, representing threshold values for minimum and maximum oxygen deficiency. However, the correctness of this expression has never been evaluated and constant critical values for oxygen stress are likely to be inappropriate. On theoretical grounds it is expected that oxygen stress depends on various abiotic and biotic factors. In this paper, we propose a fundamentally different approach to assess oxygen stress: we built a plant physiological and soil physical process-based model to calculate the minimum gas filled porosity of the soil (phi gas_min) at which oxygen stress occurs. First, we calculated the minimum oxygen concentration in the gas phase of the soil needed to sustain the roots through (micro-scale) diffusion with just enough oxygen to respire. Subsequently, phi gas_min that corresponds to this minimum oxygen concentration was calculated from diffusion from the atmosphere through the soil (macro-scale). We analyzed the validity of constant critical values to represent oxygen stress in terms Of phi gas_min, based on model simulations in which we distinguished different soil types and in which we varied temperature, organic matter content, soil depth and plant characteristics. Furthermore, in order to compare our model results with the Feddes-function, we linked root oxygen stress to root water uptake (through the sink term variable F, which is the ratio of actual and potential uptake). The simulations showed that phi gas-min is especially sensitive to soil temperature, plant characteristics (root dry weight and maintenance respiration coefficient) and soil depth but hardly to soil organic matter content. Moreover, phi gas-min varied considerably between soil types and was larger in sandy soils than in clayey soils. We demonstrated that F of the Feddes-function indeed decreases approximately linearly, but that actual oxygen stress already starts at drier conditions than according to the Feddes-function. How much drier is depended on the factors indicated above. Thus, the Feddes-function might cause large errors in the prediction of transpiration reduction and growth reduction through oxygen stress. We made our method easily accessible to others by implementing it in SWAP, a user-friendly soil water model that is coupled to plant growth. Since constant values for phi gas_min in plant and hydrological modeling appeared to be inappropriate, an integrated approach, including both physiological and physical processes, should be used instead. Therefore, we advocate using our method in all situations where oxygen stress could occur. (C) 2008 Elsevier B.V. All rights reserved.

Towards a mycorrhizal application decision model for landscape management

Papers advocating the potential value of mycorrhizal inoculation for plant establishment have been published since the 1960s. However, there is a lack of clear and comprehensive published findings from field trials that have direct application to landscape practitioners. This paper presents a conceptual decision model to predict inoculation success in a landscape context. A brief review is given of the potential benefits of inoculation and possible limitations. Several approaches are considered: a plant physiological model based on plant-fungus carbon economy; an economic approach which balances increased yield against increased management costs, and a combined economic-physiological model. This latter model considers site factors, plant performance and likelihood of mycorrhizal benefits, combined with economic values and subjective utilities. The proposed model offers a framework for the interpretation and application of research findings as these become available.

Changes in physiological attributes of ponderosa pine from seedling to mature tree.

Plant physiological models are generally parameterized from many different sources of data, including chamber experiments and plantations, from seedlings to mature trees. We obtained a comprehensive data set for a natural stand of ponderosa pine (Pinus ponderosa Laws.) and used these data to parameterize the physiologically based model, TREGRO. Representative trees of each of five tree age classes were selected based on population means of morphological, physiological, and nearest neighbor attributes. Differences in key physiological attributes (gas exchange, needle chemistry, elongation growth, needle retention) among the tree age classes were tested. Whole-tree biomass and allocation were determined for seedlings, saplings, and pole-sized trees. Seasonal maxima and minima of gas exchange were similar across all tree age classes. Seasonal minima and a shift to more efficient water use were reached one month earlier in seedlings than in older trees because of decreased soil water availability in the rooting zone of the seedlings. However, carbon isotopic discrimination of needle cellulose indicated increased water-use efficiency with increasing tree age. Seedlings had the lowest needle and branch elongation biomass growth. The amount of needle elongation growth was highest for mature trees and amount of branch elongation growth was highest for saplings. Seedlings had the highest biomass allocation to roots, saplings had the highest allocation to foliage, and pole-sized trees had the highest allocation to woody tissues. Seedlings differed significantly from pole-sized and older trees in most of the physiological traits tested. Predicted changes in biomass with tree age, simulated with the model TREGRO, closely matched those of trees in a natural stand to 30 years of age.

Modelling the spatial distribution of evapotranspiration on different scales using remote sensing data

The PROMET-family of spatial evapotranspiration models permits modelling of evapotranspiration at field scale, as well as microscale and mesoscale. The model-family consists of a kernel model (a SVAT based on Penman-Monteith and a plant-physiological model for the influence of environmental parameters on canopy resistance) and a spatial modeller, which provides and organises adequate spatial input data on the field , micro and mesoscale. Model results on the field scale show good agreement with measurements. Spatial data is set up using remote sensing and conventional data sources for a 150x100km mesoscale test region in Upper Bavaria and a 7x13 km microscale test region embedded in the mesoscale area. The model is run on both scales. Microscale model results are successfully verified using field measurements and water-balance data taken from literature. Mesoscale model results compare well both with microscale model results and satellite-measured patterns of surface temperature.

Spatial and temporal predictions of soil moisture patterns and evaporative losses using TOPMODEL and the GASFLUX model for an Alaskan catchment

Abstract. By using topographic indices as derived from a Digital Terrain Models (DTM), it is possible to represent the heterogeneity within a landscape. This heterogeneity can reflect both long term evolutionary patterns seen in a landscape and the short term forcing of flow dynamics during storm events. By spatial analysis, the linkage between the geomorphological- hydrological-plant physiological phenomena can be examined. In this study, a direct link will be established between the topographically-driven hydrological phenomena and the eco-physiological response. The topographic distribution function of TOPMODEL is used to control the spatial and temporal flux of the channel flow and water table. The plant physiological model GAS-FLUX is used to give a spatially and temporally dissaggregated species-sensitive estimate of evapotranspiration flux. Evapotranspiration is sensitive to the vegetation phonology, to tundra community physiology and to the temperature regime. A simple linking of TOPMODEL and the GAS-FLUX model is applied to a summer snow-free period to the Imnavait catchment, Alaska (2.2 km2). A species-sensitive evapotranspiration model proved to give the highest quality results when validated against flow observations. Predicted dynamics of variable source area and the component hydrological processes are illustrated.

Designing Model and Climate Controls of the Greenhouse Cucumber by Means of Repetitive Nonlinear Optimization

Starting from a descriptive plant-physiological model of the growing cucumber, greenhouse climate strategies are developed by means of nonlinear optimization. For this, the descriptive model has been approximated by a simplified time-variant nonlinear dynamic input-output control model. Contrasted with a former version other model in-puts have been chosen. Especially, the global radiation is now an explicit input. A hierarchical control structure with strategic and operative layer allows to take into consideration Apriori-and actual information on the outer climate by means of repetitive optimizations with different repetition periods at the upper and lower layer. Results of simulation runs and greenhouse tests will be reported.

Modelbuilding and optimization of the plant growth process of the greenhouse cucumber

For optimizing the greenhouse climate with regard to energy consumption and yield, a strategy will be presented which has been developed on the basis of a plant-physiological model which has been approximated by simplified input-output models in form of time-discrete time-variant nonlinear models of Wiener- and Hammerstein-type, resp. The dynamic optimization has been performed by building augmented Lagrangians and determining the saddle point. First results of greenhouse experiments confirm the usefulness of the method proposed.

Contribution to the Optimization of the Plant Growth Factors in the Greenhouse and in the Open Land

With regard to the modelling of plant growth processes, a distinction is suitably made between plant-physiological descriptive models and control models which are appropriate for the control design. The first part of the paper is concerned with the setting up of a control model on the basis of a plant-physiological model of the greenhouse cucumber developed by biologists of the Academy of Agricultural Sciences of the GDR, and with designing a strategy for the climate control ensuring optimum yields at limited expenditure of energy. This is performed by simulating the complicated descriptive model on a digital computer (substitute of the original) and parameter identification of a simplified non-linear multivariable Hammerstein and Wiener type resp. control model. On the basis of this model and of a multilevel control conception, a strategy is designed for achieving optimum climate control under “normal” environmental concitions. In order to take the actual and past outside climate into account, some repetitive optimizations with shortened horizon are performed. In the second part, a growth model of the sugar-beet developed by the Academy of Agricultural Sciences is processed such that mathematical optimization techniques can be used for maximizing the final yield. This model was already available as control model. The control variables are in this case the water stress (state of the water supply of the plant) and the nitrogen content of the leaves (nitrogen supply The off-line control design is carried out using a method of variable metrics without direction minimization.