A Simple Crop Phenology Algorithm in the Land Surface Model CN‐CLASS

Abstract

Land surface models are useful tools for estimating the contribution and response to climate change of C dynamics in various terrestrial ecosystems. In many land surface models, plant phenological algorithms are incorporated based on eld studies in forests. However, to simulate adequately the C cycle over a large area, there is a need to include and validate algorithms for other ecosystems. e Carbon and Nitrogen-coupled Canadian Land Surface Scheme (CN-CLASS) is a land surface model that has been applied successfully to the study of C stocks in forest ecosystems. e objective of this study is to incorporate a simple crop phenology algorithm into CN-CLASS and validate its ability to simulate C cycles at an agricultural site in southern Ontario, Canada. e model was validated on a corn crop (Zea mays L.) in 2005 and 2008 based on measurements of aboveground biomass and net ecosystem productivity (NEP), as well as a well-tested agricultural model, DayCENT (the daily time-step version of the CENTURY model). e modi ed CN-CLASS showed similar dynamics of biomass allocation compared with eld measurements and DayCENT simulations. Regression analysis indicated that the modi cations improved the NEP simulation for a corn eld, with the coe cient of determination (R 2 ) relating simulated and observed NEP increasing from 0.51 in the original CN-CLASS to 0.78 in the modi ed model. Other crop species could be further validated to expand the model application to crop rotation studies and large areas covered by forests and crop elds in consideration of land management practices.

VIC–CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

“…In their simulations, they considered only three crop species (wheat, corn, and soybean) and used a fixed planting date, which can lead to a discrepancy with observations because actual planting dates vary in time as a function of weather (Zeng et al, 2013); this discrepancy can result in an overestimation of the negative impacts of warming on crop yield, as an earlier planting date is a viable adaptation strategy in many regions of the world (Waha et al, 2013). While a newer version of CLM (CLM4-Crop; Lu et al, 2015) simulates irrigation events and CO 2 fertilization as well as biomass and vegetation growth processes, its application is also limited to three crop types (Chen et al, 2015) and is not able to mechanistically simulate irrigation efficiency. Elliott et al (2014) compared 10 GHMs and 6 global gridded crop models (GGCMs); they reported that the performance of GHMs is generally poor in the simulation of future irrigation water demand.…”

Influences of agricultural phenology dynamic on land surface biophysical process and climate feedback

“…Phenophase and organic matter accumulation and allocation process based on physiology CLASS Carbon and nitrogen model (Chang et al, 2014) Farmland in Canada Increase the determination coefficient between simulated and observed data of NEP; More rational distribution process of organic matter.…”

PPMaP: Reproducible and Extensible Open-Source Software for Plant Phenological Phase Duration Prediction and Mapping

“…Therefore, it follows that temperature determines the progression of processes to the next developmental stage and their respective rate of development [18]. Numerous relationships have been established to portray the way temperature influences plant development [19][20][21]. These comprise degree-days, day-degrees, heat units, heat sums, thermal units, and growing degree-days [8,16,17].…”

“…All these tools provide mechanisms to handle large datasets and further facilitate the distribution of information among the growing scientific community. Additional examples of efforts and software that were developed to improve agricultural modeling include the CN-CLASS [21] that was initially developed to study Carbon (C) stock in forest ecosystems but was later modified to link crop phenological development and C allocation during the growth of maize. Also, CropScape is a w eb service-based application for discovering and disseminating geospatial cropland data products for decision making [4].…”