Design and Implementation of Declarative Crop Modeling Framework

Thesis (Ph.D.)--University of Washington, 2021Crop modeling is a process of translating quantitative knowledge on crop growth into a computer program that simulates the growth in silico. From a software engineering perspective, crop modeling has suffered from a legacy built decades ago when early crop models appeared. Many crop models have been developed in imperative programming approaches striving for high performance, but frequently suffered from error-prone code and technical debts left behind. In this study, we propose a new declarative modeling framework named Cropbox written in Julia programming language to support developing crop models in a concise form equipped with useful abstractions commonly required in modeling. With an insight that a crop model is essentially an integrated network of generalized state variables, the framework provides various primitives for representing variables and systems as well as functions essential to modeling workflow. The modeling workflow based on Cropbox was used to create and illustrate its applications in crop modeling at different levels of organization and complexity. The first application was phenology modeling for building an ensemble model to predict flowering time based on various existing approaches. Extracting common patterns in the models and developing reusable interface for simulation, visualization, and calibration motivated an idea of a new modeling framework. The second application was a coupled gas-exchange model which combines two models for biochemical photosynthesis and empirical stomatal conductance with an additional link to an energy balance equation. The model implemented in Cropbox framework provided the same functionality as an existing model written in C++ with less code and a more flexible interface in terms of parameter management and output visualization. To demonstrate the capability of the model, we evaluated two stomatal conductance modeling approaches and applied them to replicate the observed behavior of transgenic plants from the literature. The last application was a whole-plant crop simulation model for garlic (Allium sativum) translated from an existing C++ model aimed at simulating leaf development and growth. The new model was expanded and improved to simulate biomass and yield with an emphasis on whole-plant carbon budget. The model was evaluated with three datasets for analyzing effective planting dates as a climate adaptation strategy in South Korea under future climate conditions projected by different greenhouse gas emission scenarios. The Cropbox framework can support development of conventional crop models but also show potential for incorporating other approaches like functional-structural plant modeling (FSPM) as briefly illustrated by a 3D root structure growth model for switchgrass (Panicum virgatum). With a domain-specific language and unified interface specifically designed for crop modeling, the Cropbox framework will become a useful tool for research and teaching in this field

IncreTable, a mixed reality tabletop game experience

IncreTable is a mixed reality tabletop game inspired by The Incredible Machine. Users can combine real and virtual game pieces in order to solve puzzles in the game. Game actions include placing virtual domino blocks with digital pens, controlling a virtual car by modifying the virtual terrain through a depth camera interface or controlling real robots to topple over real and virtual dominoes

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

Random Forests for Global and Regional Crop Yield Predictions - Fig 1

<p><b>Study regions: global wheat mega-environments (A), US maize producing counties (B), and northeastern seaboard region (NESR) (C).</b> All 12 wheat mega-environments are shown with different colors (A). Maize grain yield by the US counties in 2013 surveyed by USDA-NASS is visualized using different shades with darker shades representing higher yields (B). The NESR includes 433 counties of Connecticut, Delaware, Maine, Massachusetts, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, and West Virginia. The red dots indicate the location of the data points, where weather stations exist. Point type data was used for this region (C).</p

Random Forest (RF) and multiple linear regression (MLR) model performance evaluation statistics.

<p>See text for detailed description and equation of each statistic shown in the table.</p

Random Forests model performance for test datasets.

<p>Observed vs. predicted plots are shown for four case studies: (A) global wheat grain yield, (B) US maize grain yield over 30 years, (C) potato wet tuber yield in northeastern seaboard region (NESR), and (D) maize silage yield in NESR The dashed lines indicate 1:1 relation and the solid line represents linear regression between the observations and predictions made for test datasets. The linear regression equation for the solid line is provided along with <i>RMSE</i>, <i>EF</i>, <i>d</i>, and Pearson’s <i>r</i>.</p

The predictors used for random forests (RF) and multiple linear regression models.

<p>Rank corresponds to a variable importance measure determined by RF models for each dataset.</p

Partial dependence plots for the top ranked predictor variable from variable importance measures of Random Forests models.

<p>(A) N fertilization rate (NFERT) in global wheat grain yield predictions, (B) year (YR) in the 30-year US maize grain yields, (C) Latitude (<i>lat</i>) for potato wet tuber yields in northeastern seaboard region (NESR), and (D) <i>lat</i> for maize silage yield in NESR. The <i>Y</i>-axis of each plot indicates the average of all of the possible model predictions for the <i>X</i> predictor value. The <i>X</i>-axis hash marks indicate deciles.</p