Bridging Time-series Image Phenotyping and Functional-Structural Plant Modeling to Predict Adventitious Root System Architecture.

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional-structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns.

Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.

An Integrative Process-Based Model for Biomass and Yield Estimation of Hardneck Garlic (Allium sativum).

We introduce an integrative process-based crop model for garlic (Allium sativum). Building on our previous model that simulated key phenological, morphological, and physiological features of a garlic plant, the new garlic model provides comprehensive and integrative estimations of biomass accumulation and yield formation under diverse environmental conditions. This model also showcases an application of Cropbox to develop a comprehensive crop model. Cropbox is a crop modeling framework featuring declarative modeling language and a unified simulation interface for building and improving crop models. Using Cropbox, we first evaluated the model performance against three datasets with an emphasis on biomass and yield measured under different environmental conditions and growing seasons. We then applied the model to simulate optimal planting dates under future climate conditions for assessing climate adaptation strategies between two contrasting locations in South Korea: the current growing region (Gosan, Jeju) and an unfavorable cold winter region (Chuncheon, Gangwon). The model simulated the growth and development of a southern-type cultivar (Namdo, ND) reasonably well. Under Representative Concentration Pathway (RCP) scenarios, an overall delay in optimal planting date from a week to a month, and a slight increase in potential yield were expected in Gosan. Expansion of growing region to northern area including Chuncheon was expected due to mild winter temperatures in the future and may allow ND cultivar production in more regions. The predicted optimal planting date in the new region was similar to the current growing region that favors early fall planting. Our new integrative garlic model provides mechanistic, process-based crop responses to environmental cues and can be useful for assessing climate impacts and identifying crop specific climate adaptation strategies for the future.

Coupled Gas-Exchange Model for C4 Leaves Comparing Stomatal Conductance Models.

Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball-Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.

A process-based model for leaf development and growth in hardneck garlic (Allium sativum).

BACKGROUND AND AIMS: Phenology and morphology are two major aspects of crop growth models. A new process-based model built for hardneck garlic (Allium sativum) is presented, focusing on phenology and morphology processes and how they translate to whole-plant growth. The tight coupling between the two processes and their dynamic changes throughout the growing season were captured while incorporating storage effects and reproductive aspects unique to bulbous crops. METHODS: Non-linear temperature dependences of leaf development were integrated into the model and dynamically coupled with changes in leaf growth throughout the growing season. Bulb storage effects on leaf development and photoperiod effects on the vegetative-to-reproductive transition were also incorporated. The model was parameterized with data from a set of experiments and the literature, while its performance was tested with additional observations that had not been used for parameterization under a range of environmental conditions, management practices and cultivar choices. KEY RESULTS: The model successfully captured the dynamic nature of leaf development and growth in garlic plants throughout the growing season. It simulated with reasonable accuracy the timing of leaf initiation, maturation and senescence, as well as changes in green leaf area over time. Most parameters were relatively stable across cultivars, and parameter sensitivity tests revealed the importance of bulb storage effects. CONCLUSIONS: The model embodies a novel approach to capture the phenology and morphology of garlic under a range of environments, genotypes and management practices. The process-oriented nature of the model and inclusion of storage effects set the foundation for bulbous crop growth simulations, allowing the understanding and discovery of key processes that coordinate and integrate the dynamics of growth and development from organ to whole plant, with implications for crop improvement programmes while opening opportunities for modelling other bulbous crops.

Random Forests for Global and Regional Crop Yield Predictions.

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.