Plant Growth Model Research Articles

Predicting Plant Growth and Development Using Time-Series Images

Early prediction of the growth and development of plants is important for the intelligent breeding process, yet accurate prediction and simulation of plant phenotypes is difficult. In this work, a prediction model of plant growth and development based on spatiotemporal long short-term memory (ST-LSTM) and memory in memory network (MIM) was proposed to predict the image sequences of future growth and development including plant organs such as ears. A novel dataset of wheat growth and development was also compiled. The performance of the prediction model of plant growth and development was evaluated by calculating structural similarity index measure (SSIM), mean square error (MSE), and peak signal to noise ratio (PSNR) between the predicted and real plant images. Moreover, the optimal number of time steps and the optimal time interval between steps were determined for the proposed model on the wheat growth and development dataset. Under the optimal setting, the SSIM values surpassed 84% for all time steps. The mean of MSE values was 46.11 and the MSE values were below 68 for all time steps. The mean of PSNR values was 30.67. When the number of prediction steps was set to eight, the prediction model had the best prediction performance on the public Panicoid Phenomap-1 dataset. The SSIM values surpassed 78% for all time steps. The mean of MSE values was 77.78 and the MSE values were below 118 for all time steps. The mean of PSNR values was 29.03. The results showed a high degree of similarity between the predicted images and the real images of plant growth and development and verified the validity, reliability, and feasibility of the proposed model. The study shows the potential to provide the plant phenotyping community with an efficient tool that can perform high-throughput phenotyping and predict future plant growth.

Modelling Plant Growth Based on Gompertz, Logistic Curve, Extreme Gradient Boosting and Light Gradient Boosting Models Using High Dimensional Image Derived Maize (<i>Zea mays</i> L.) Phenomic Data

Modelling of plant growth is vital for hypotheses testing and carrying out virtual plant growth and development experiments, which may otherwise take a long time under field conditions. Modelling of plant growth has been aggravated by new phenotyping platforms that generate high dimensional data non-destructively over the entire growth time of a plant using a set of camera system. Such platforms generate high-throughput phenomic data, which is complex and constitute many features collected at multiple growth points for the same plant. However, the classical models are limited in that they can only model a single feature at a time. The objective of this study was to apply dynamic plant growth models that could be used to dissect complex relationships between plant growth and development using several modelling strategies. These included sigmoid, light GBM and XGBoost models. The image derived phenomic data was obtained from the Leibniz Institute of Plant Genetics and Crop Plant Research Gatersleben, Germany. The models were fitted using R statistical software and compared based on RMSE, R-squared, AIC and BIC performance metrics. The results showed that the XGBoost (RMSE = 2.1641) and Light GBM (RMSE = 2.7776) performed better than the Gompertz (RMSE = 3.8378) and the logistic function (RMSE = 3.8378) models in modelling maize plant growth. The XGBoost model (RMSE = 2.1641) showed better performance than Light GBM model (RMSE = 2.7776) in modelling maize plant growth. The Gompertz model using plant volume had AIC and BIC values for 139738.3 and 139763.4, respectively. The Gompertz model for plant side area had AIC and BIC values for 98436.15 and 98461.31, respectively. The logistic function model for plant volume had AIC and BIC values for 139749.2 and 139774.4, respectively. The logistic function model for plant side area had AIC and BIC values for 98415.95 and 98441.11, respectively. The Gompertz model and logistic function models showed almost the same performance in modelling maize plant growth. The non-parametric models, the XGBoost and light GBM, were found to perform better than the classical models (Gompertz and logistic functions) in modelling maize plant growth. Therefore, the study recommends the use of XGBoost as a generic model to fit high dimensional and complex phenotypic data in modelling plant growth and prediction of plant biomass yield at different growth points.

Simulating Polyculture Farming to Learn Automation Policies for Plant Diversity and Precision Irrigation

Polyculture farming, where multiple crop species are grown simultaneously, has potential to reduce pesticide and water usage while improving the utilization of soil nutrients. However, it is much harder to automate polyculture than monoculture. To facilitate research, we present AlphaGardenSim, a fast, first order, open-access polyculture farming simulator with single plant growth and irrigation models tuned using real world measurements. AlphaGardenSim can be used for policy learning as it simulates inter-plant dynamics, including light and water competition between plants in close proximity and approximates growth in a real greenhouse garden at 25, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$000\times $ </tex-math></inline-formula> the speed of natural growth. This paper extends earlier work with a new action space that includes planting, which dynamically finds new seed locations that increases resources utilization, and an adaptive sampling technique to reduce the number of actions taken at each timestep without affecting performance. We also evaluate other automation policies using a novel metric that combines plant diversity and canopy coverage. Code and supplementary material can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/BerkeleyAutomation/AlphaGarden</uri> . <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Monoculture farming is often characterized by heavy agrichemical inputs, such as chemical fertilizers and pesticides, and increased vulnerability to disease and pestilence. This paper is motivated by the lack of long-term sustainability of industrial agriculture, and its implications for human food security. Although polyculture is a sustainable alternative to monoculture farming, it requires more human labor and is more challenging to automate. In this paper we propose a fast, first order simulator that simulates the growth of plants in a polyculture setting. Simulation experiments suggest that the simulator can be used to learn a planting, watering and pruning plan a robot can follow to produce maximal yield from a diverse set of plants with limited irrigation, however it has not yet been tested on a physical garden. In future research we will develop a fully automated controller that will operate planting, irrigation and pruning tools in a physical garden over multiple plant growth cycles.

Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling

There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.

Synthesis of an optimal radiation model of plant growth according to the criterion of the minimum of integral solar radiation transmitted to the soil

Relevance. Based on satellite remote sensing data, it has been suggested that there is a linear relationship between NDVI and FPAR. However, the significant influence of seasonal factors of a particular ecosystem on this relationship creates uncertainty in the assessment of plant productivity by remote sensing. This uncertainty in the estimates of total grown products (GPP) by the indicator calculated as the ratio of GPP to photosynthetically active radiation absorbed by vegetation can be explained by daily changes in incoming optical radiation both in composition and magnitude. It is shown that the GPP indicator can be determined indirectly by calculating the extremum of the heat flow of the soil coming from the outside.Methods. A new approach to the synthesis of the optimal radiation regime of plant growth based on the methodology of optimization of isomorphic-holonomic systems is proposed. The basis of the proposed method for the synthesis of the radiation regime of plant growth is taking into account the total radiation coming from above to the crown of the plant, the peculiarity of this approach is to find a minimum of that part of the externally incoming photosynthetically active radiation that reaches the soil and is not spent on photosynthesis, provided that the production of the initially specified product is guaranteed. The optimization problem of achieving an extreme value of the total radiation coming from outside into the soil is formulated in the form of an unconditional variational optimization problem with a common target optimization functional. An approximate solution of the optimization problem is given, in which the target functional reaches a minimum, i.e. the maximum amount of external radiation is spent on the photosynthesis process.Results. A solution is obtained at which the target functional reaches a minimum, i.e. the maximum amount of external radiation is spent on the photosynthesis process. It is shown that the application of the known method of unconditional variational optimization with a guaranteed given value of GPP allows us to determine the optimal radiation regime of plant growth.

Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2.

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.

Plant competition optimization: A novel metaheuristic algorithm

AbstractPlant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.

A mechanistic model for nitrogen-limited plant growth.

Nitrogen is often regarded as a limiting factor to plant growth in various ecosystems. Understanding how nitrogen drives plant growth has numerous theoretical and practical applications in agriculture and ecology. In 2004, Göran I. Ågren proposed a mechanistic model of plant growth from a biochemical perspective. However, neglecting respiration and assuming stable and balanced growth made the model unrealistic for plants growing in natural conditions. The aim of the present paper is to extend Ågren's model to overcome these limitations. We improved Ågren's model by incorporating the respiratory process and replacing the stable and balanced growth assumption with a three-parameter power function to describe the relationship between nitrogen concentration (Nc) and biomass. The new model was evaluated based on published data from three studies on corn (Zea mays) growth. Remarkably, the mechanistic growth model derived in this study is mathematically equivalent to the classical Richards model, which is the most widely used empirical growth model. The model agrees well with empirical plant growth data. Our model provides a mechanistic interpretation of how nitrogen drives plant growth. It is very robust in predicting growth curves and the relationship between Nc and relative growth rate.

Radiative Transfer Image Simulation Using L-System Modeled Strawberry Canopies

The image-based modeling and simulation of plant growth have numerous and diverse applications. In this study, we used image-based and manual field measurements to develop and validate a methodology to simulate strawberry (Fragaria × ananassa Duch.) plant canopies throughout the Florida strawberry growing season. The simulated plants were used to create a synthetic image using radiative transfer modeling. Observed canopy properties were incorporated into an L-system simulator, and a series of strawberry canopies corresponding to specific weekly observation dates were created. The simulated canopies were compared visually with actual plant images and quantitatively with in-situ leaf area throughout the strawberry season. A simple regression model with L-system-derived and in-situ total leaf areas had an Adj R2 value of 0.78. The L-system simulated canopies were used to derive information needed for image simulation, such as leaf area and leaf angle distribution. Spectral and plant canopy information were used to create synthetic high spatial resolution multispectral images using the Discrete Anisotropic Radiative Transfer (DART) software. Vegetation spectral indices were extracted from the simulated image and used to develop multiple regression models of in-situ biophysical parameters (leaf area and dry biomass), achieving Adj R2 values of 0.63 and 0.50, respectively. The Normalized Difference Vegetation Index (NDVI) and the Red Edge Simple Ratio (SRre) vegetation indices, which utilize the red, red edge, and near infrared bands of the spectrum, were identified as statistically significant variables (p &lt; 0.10). This study showed that both geometric (canopy seize metrics) and spectral variables were successful in modeling in-situ biomass and leaf area. Combining the geometric and spectral variables, however, only slightly improved the prediction model. These results show the feasibility of simulating strawberry canopies and images with inherent geometrical, topological, and spectral properties of real strawberry plants. The simulated canopies and images can be used in applications beyond creating realistic computer graphics for quantitative applications requiring the depiction of vegetation biological processes, such as stress modeling and remote sensing mission planning.

Organic solar powered greenhouse performance optimization and global economic opportunity

This work integrates greenhouse energy demand, solar power production, and plant growth modeling to assess the economic opportunity of organic solar powered greenhouses. Results show these systems have positive economic outlook across broad climates.

Prediction of the van Genuchten model soil hydraulic parameters for the 5-m soil profile in China’s Loess Plateau

Soil hydraulic parameters (SHPs) represent a crucial input for modelling of water flow, biogeochemical processes, and plant growth. This modelling depends on an accurate description of the water balance in a critical soil zone. To this end, pedotransfer functions (PTFs) are required to translate basic soil data into SHPs as it is challenging to measure them. This challenge is peculiarly acute for deep soil layers in thick loess deposits, such as in the China’s Loess Plateau Region (CLPR). In this study, we analysed which factors are strongly correlated with SHPs, and developed PTFs for the SHPs. To this end, undisturbed and disturbed soil samples were collected up to 8 m depth at seven sampling sites in the CLPR to measure the soil water retention curve, saturated hydraulic conductivity (Ks), and associated soil and environmental factors. Furthermore, soil core samples in the 5 m soil profile were collected at 243 sites, and the corresponding environmental factors were calculated to estimate the spatial distribution of SHPs across the CLPR using the established PTFs. Redundancy analysis of the collected data in the 0–8-m soil profile revealed that soil texture, soil depth, mean annual precipitation, slope gradient, slope aspect, and elevation were strongly correlated with SHPs. The most correlated variables were used to develop PTFs for SHPs by using stepwise multiple linear regression. The accuracy of the established regional PTFs was significantly higher compared to state-of-the-art in this domain. The spatial distribution of SHPs in the CLPR study area exhibited significant spatial clustering. Generally, SHPs transitioned from the southeast to the northwest, with significant differences between the south and north. Our results provide empirical basis for further quantification of the response of soil hydrological processes to vegetation restoration and land-use change not only in the CLPR, but also in other similar regions.

Towards a mathematical model of plant growth

Mankind has long been interested in the growth of plants, even in the Middle Ages, Leonardo da Vinci observed the seasonality of growth and some features of plant forms. In recent years, many agricultural producers have begun to implement a coordinate (precise) farming system. In this regard, there is a need to improve methods for assessing and managing the growth of agricultural crops. To assess the functioning of the system “plant - soil - air [[CHECK_DOUBLEQUOT_ENT]] (P-S-A), the concept of plant growth potential is introduced, which is the ratio of the power spent on the formation of a unit of vegetation mass. The paper considers the theoretical prerequisites for determining the growth potential. Since the operational management of the formation of the crop yield is an important task in crop production, the functioning of the P-S-A system during the growing season is proposed to be evaluated by dimensionless coefficients, which represent the ratio of the substance mastered by plants to the incoming one. The product of these coefficients in terms of light-heat-food-gas-and moisture supply represents the reliability of the P-S-A system, and allows you to evaluate and effectively manage the technological process of the growth of cultivated plants during the growing season.

Breeding Beyond Monoculture: Putting the "Intercrop" Into Crops.

Intercropping is both a well-established and yet novel agricultural practice, depending on one’s perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., “trait-based” breeding) or quantitative genetics-driven (i.e., “product-based” breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.

Dynamically Controlled Environment Agriculture: Integrating Machine Learning and Mechanistic and Physiological Models for Sustainable Food Cultivation

Inefficiencies and imprecise input control in agriculture have caused devastating consequences to ecosystems. Urban controlled environment agriculture (CEA) is a proposed approach to mitigate the impacts of cultivation, but precise control of inputs (i.e., nutrient, water, etc.) is limited by the ability to monitor dynamic conditions. Current mechanistic and physiological plant growth models (MPMs) have not yet been unified and have uncovered knowledge gaps of the complex interplay among control variables. Moreover, because of their specificity, MPMs are of limited utility when extended to additional plant species or environmental conditions. Simultaneously, although machine learning (ML) can uncover latent interactions across conditions, phenotyping bottlenecks have hindered successful application. To bridge these gaps, we propose an integrative approach whereby MPMs are used to construct the foundations of ML algorithms, reducing data requirements and costs, and ML is used to elucidate parameters and causal inference in MPM. This review highlights research about control and automation in CEA, synthesizing literature into a framework whereby ML, MPM, and biofeedback inform what we call dynamically controlled environment agriculture (DCEA). We highlight synergistic characteristics of MPM and ML to illustrate that a DCEA framework could contribute to urban resilience, human health, and optimized productivity and nutritional content.

Parameter extraction method of virtual plant growth model based on Improved Particle Swarm Optimization

A common problem in plant modeling is to create varying shapes similar to an initial plant model. Although significant advancement of 3D plant modeling has been achieved, there are still some problems such as vague descriptions and execution speed. In view of those problems, a parameter extraction method for a virtual plant growth model is proposed based on an improved particle swarm optimization (PSO) algorithm. This method first defines a description framework to set the parameters of a virtual plant growth model, and then the optimized parameters are obtained by using the improved PSO algorithm. This algorithm helps particles leave the local optima by adding a stochastic perturbation in solution space. The experiments show that suitable parameters for simulating a plant abstraction can be obtained through multiple iterations. Meanwhile, the fitness value and standard deviation are better than from other methods under four test functions and the convergence rate is also improved. The proposed method provides a unified description of plant geometrical structure, which is relatively intuitive, and provides a new idea for generating different shapes of a plant geometrical structures.

Simulating the effects of irrigation and tillage on soil water, evapotranspiration, and yield of winter wheat with RZWQM2

Agricultural water shortages threaten food security worldwide, particularly in the North China Plain (NCP) which is an important grain production base in China. To solve this problem, we studied the effects of two tillage methods (no tillage: NT and plow tillage to 15 cm: PT) combined with two different irrigation timing (I1, 60 mm at the jointing stage and I2, 60 mm at 10 days after the jointing stage) on the grain yield of winter wheat and crop water productivity (CWP). The experimental results were used to calibrate the RZWQM2 model to improve its application in the NCP. The simulation results of the model could be used to compensate for the shortcomings of field experiments. The RZWQM2 model was calibrated and validated using the winter wheat growth data from 2015 to 2019. The normalized root mean square error (calibration and validation) between simulated and measured soil water storage (SWS), evapotranspiration (ET), leaf area index (LAI), biomass, and grain yield was 8.43 %–11.99 %, 4.39 %–8.67 %, 13.71 %–19.87 %, 16.37 %–19.36 %, and 13.83 %–17.71 %, respectively. NT improved the measured soil moisture status but decreased measured yield. However, I2 compensated for the negative effect of NT and resulted in a higher measured CWP than that after I1. The modified RZWQM2 model was used to calculate the actual transpiration and actual evaporation (AE) to expand the experimental results and reveal the effect of different treatments on actual evapotranspiration (AET). NT reduced the simulated AE during the entire growth period, reducing AET and increasing CWP. However, compared with the simulation effect of SWS and ET, the simulation effect of LAI, biomass, and grain yield was worse, and it was evident that improvements to the plant growth model are required. This study demonstrated that NT combined with delayed irrigation (10 days after the jointing stage) can improve soil moisture status and increase CWP in the NCP.

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

Bayesian Estimation for the GreenLab Plant Growth Model with Deterministic Organogenesis

Cross-sphere modelling to evaluate impacts of climate and land management changes on groundwater resources

Climate change affects both water resources and agricultural production. With rising temperatures and decreasing summer precipitation, it is expected that agricultural production will be increasingly limited by drought. Where surface- or groundwater resources are available for irrigation, an increase in water withdrawals for irrigation is to be expected. Therefore, quantitative approaches are required to anticipate and manage the expected conflicts related to increased water abstraction for irrigation. This project aims to investigate how agricultural production, water demand for irrigation, runoff and groundwater dynamics are affected by future climate change and how climate change impacts combined with changes in agricultural water use affect groundwater dynamics. To answer these research questions, a comprehensive, loosely coupled model approach was developed, combining models from three disciplines: an agricultural plant growth model, a hydrological model and a hydrogeological model. The model coupling was implemented and tested for an agricultural area located in Switzerland in which groundwater plays a significant role in providing irrigation water. Our suggested modelling approach can be easily adapted to other areas.The model results show that yield changes are driven by drought limitations and rising temperatures. However, an increase in yield may be realized with an increase in irrigation. Simulation results show that the water requirement for irrigation without climate protection (RCP8.5) could increase by 40% by the end of the century with an unchanged growing season and by up to 80% with varietal adaptations. With climate change mitigation (RCP2.6) the increase in water demand for irrigation would be limited to 7%. The increase in irrigation (+12 mm) and the summer decrease in recharge rates (~20 mm/month) with decreasing summer precipitation causes a lowering of groundwater levels (40 mm) in the area in the late summer and autumn. This impact may be accentuated by an intensification of irrigation and reduced by extensification.

Use of PhenoCam Measurements and Image Analysis to Inform the ALMANAC Process-based Simulation Model

Near-surface remote sensing has been used to document seasonal growth patterns (i.e. phenology) for plant communities in diverse habitats. Phenology from this source may only apply to the area within the images. Meanwhile ecosystem models can accommodate variable weather and landscape differences to plant growth, but accuracy is improved by adding ground-truthed inputs. The objective of this study was to use PhenoCam data, image analysis, and Beer’s law with established extinction coefficients to compare leaf area index (LAI) development in the ALMANAC model for diverse plant types and environments. Results indicate that PhenoCam time series imagery can be used to improve leaf area development in ALMANAC by adjusting parameter values to better match LAI derived values in new diverse environments. Soybeans, mesquite, and maize produced the most successful match between the model simulations and PhenoCam data out of the eight species simulated. This study represents, to our knowledge, the first independent evaluation of the ALMANAC process-based plant growth model with imagery in agroecosystems available from the PhenoCam network. The results show how PhenoCam data can make a valuable contribution to validate process-based models, making these models much more realistic and allows for expansion of PhenoCam influence.

An updated framework for climate change impact assessment of bioenergy and an application in poplar biomass

Mainstream life cycle assessment (LCA) studies on the climate change impact of bioenergy fail to account for some direct impacts, such as biomass-derived CO2 emissions and potential impacts on future carbon dynamics. For a more persuasive assessment of the climate change impacts of bioenergy, a simplified and comprehensive framework was developed for LCA by integrating the atmospheric carbon cycle model, plant growth model, and biomass decomposition model. The system includes four components: fossil fuel-derived greenhouse gas (GHG) emissions, biogenic CO2 emissions, biomass regrowth for compensation, and the difference in carbon sequestration from the reference scenario. For an illustration purpose, the framework was applied in the study of the climate change impacts of hybrid poplar use for bio-ethanol, bio-diesel, heating, and bio-power. The LCA study indicated that the fossil fuel-derived GHG emissions (15.77–42.55 kg CO2 eq/GJ) from biomass utilization were lower than those from the combustion of fossil fuels. Because the fossil fuel-derived GHG emissions, biogenic CO2 emissions, and biomass regrowth for compensation were positive, the sums of the three components (120.21–243.97 kg CO2 eq/GJ) were higher than the emissions from the combustion of fossil fuels. However, biomass utilization may improve biomass regrowth or avoid biomass decomposition. The differences in carbon sequestration are usually negative and can offset the positive climate change impacts. The final total climate change impacts for the different types of bioenergy were from –26.85 kg CO2 eq/GJ to 1.08 kg CO2 eq/GJ. The newly developed framework is more persuasive and comprehensive than the conventional LCA because it includes all the direct impacts of biomass utilization based on the biomass characteristics. The application of this framework can ensure the sustainable utilization of biomass and avoid ecosystem degradation.