Function Of Soil Properties Research Articles

Preliminary Experiences in Determining the Soil–Water Characteristic Curve of a Sandy Soil Using Physical Slope Modeling

Relating soil moisture content to soil suction, the soil–water characteristic curve (SWCC) represents an essential feature in unsaturated soil mechanics that enables estimation of different unsaturated soil property functions and modeling of the macro-scale soil behavior. However, depending on the soil and processes under consideration, proper hydraulic characterization of a soil through direct laboratory measurements can be difficult, time-consuming, and involve many uncertainties. In the case of uniformly graded sands, there is a highly nonlinear and steep shape of the SWCC, with only a few kPa of soil suction separating saturated and residual soil moisture conditions, which makes measurements for determinations of SWCC especially challenging. This study encompasses an investigation of the sandy type of soil’s behavior and presents some preliminary results and experiences on the determination of SWCC through the use of physical slope model tests. The 30 cm deep slope, inclined at 35 degrees and instrumented with soil moisture and pore water pressure sensors, was exposed to series of rainfall intensities, ranging from 37 up to 300 mm/h. The results indicated that the data on hydraulic response in monitored points are not only useful for the determination of SWCC, but that the approach is useful for investigation of hydraulic hysteresis phenomena, as well as its effects on soil moisture and pore water pressure conditions, which also affects the stability conditions of a slope. In particular, the best-fit parameters of the van Genuchten model suggested air entry values of 1.6 and 1.1 kPa for the drying and the wetting curves of the SWCC, respectively, with the two branches shifted by about 1 kPa of soil suction.

Three-Dimensional Model for Bioventing: Mathematical Solution, Calibration and Validation

Bioventing is an established technique extensively employed in the remediation of soil contaminated with petroleum hydrocarbons. In this study, the objective was to develop an improved foundational bioventing model that characterizes gas flow in vadose zones where aqueous and non-aqueous phase liquid (NAPL) are present and immobile, accounting for interphase mass transfer and first order biodegradation kinetics. By incorporating a correlation for the biodegradation rate constant, which is a function of soil properties including initial population of petroleum degrader microorganisms in soil, sand content, clay content, water content, and soil organic matter content, this model offers the ability to integrate a specific biodegradation rate constant tailored to the soil properties for each site. The governing equations were solved using the finite volume method in OpenFOAM employing the “porousMultiphaseFoam v2107” (PMF) toolbox. The equation describing gas flow in unsaturated soil was solved using a mixed pressure-saturation method, where calculated values were employed to solve the component transport equations. Calibration was done against a set of experimental data for a meso-scale reactor considering contaminant volatilization rate as the pre-calibration parameter and the mass transfer coefficient between aqueous and NAPL phase as the main calibration parameter. The calibrated model then was validated by simulating a large-scale reactor. The modelling results showed an error of 2.9% for calibrated case and 4.7% error for validation case which present the fitness to the experimental data, proving that the enhanced bioventing model holds the potential to improve predictions of bioventing and facilitate the development of efficient strategies to remediate soil contaminated with petroleum hydrocarbons.

The application of a plant community model to evaluate adaptation strategies for alleviating climate change impacts on grassland productivity, biodiversity and forage quality

Grasslands are environments characterized by an elevated biodiversity in plant species, which dynamically evolves over time as a function of management practices, soil properties, and climate conditions. Climate change can locally affect grassland growth and floristic composition and, in turn, the quality and quantity of provided ecosystem services. We show how the use of the plant community model CoSMo allows to explicitly account for the dynamics of floristic composition as response to environmental variables. This, in turn, allows quantifying climate change impacts on grasslands growth and composition and evaluating alternative strategies to improve adaptation in the mid-term. As a case study, we focused on mountain areas in the northern Apennines (Italy), where temporary alfalfa (Medicago sativa L.)-dominated grasslands are sown for the production of Parmesan cheese. Five sites and four alternative climate scenarios (RCP4.5 and RCP8.5 projections as provided by the HadGEM2 and GISS general circulation models) were considered, to explore a wide range of agro-environmental conditions. Results showed that the observed dynamics of floristic composition and biomass accumulation were successfully simulated, with a mean absolute error lower than 10% for floristic composition and less than 1 t ha−1 for total biomass. Climate change impacts were globally negative, with a clear decrease of forage production as compared to the baseline (from -3.6% to -14.3% according to the climate scenario). The biodiversity of the plant community also declined (12.5% average decrease in the inverse Simpson index) due to the increase in alfalfa dominance. The latter, however, led to preserve the forage crude protein content (+0.9% on average). Guidelines for optimizing grassland productivity and forage protein under future climate were defined, mainly focused on reducing the alfalfa field duration, sowing grass-legume mixtures, and delaying the last cut of the season. These practices can be easily adopted under operational farming conditions to support the adaptation of temporary grasslands to climate change. According to our knowledge, this is one of the first study of climate change impact that uses a plant community model to explicitly considers phytocoenosis dynamics, biodiversity, and their effect on forage production (quantity and quality). Considering the role of grasslands as providers of key ecosystem services ‒ especially in marginal areas ‒ we believe that our modelling approach can support the identification of effective adaptation strategies to address future climate challenges.

Assessing dependence between soil ecosystem services as a function of weather and soil: Application of vine copula modeling

Soils are natural ecosystems that provide ecosystem services, whose provision depends on multiple soil properties, climate conditions and human management. Dependence among soil ecosystem services (SESs) must therefore be considered to reliably assess risks of improving SES, as a function of weather conditions or soil properties. The present study described dependence among regulating and provisioning SESs predicted by a biophysical soil and crop model, based on a dataset of soils in France. We applied vine copula modeling as a statistical method that can model joint distribution functions of three SESs and enabled us to estimate probabilities of exceeding a level of one SES as a function of another SES. Trade-offs may need to be made between them to manage soil and water resources and achieve a given yield. By highlighting the degree of dependence among multiple SESs, copula models thus provide information that may improve understanding or management of ESs.

Estimation of site-specific multivariate probability distribution of soil properties using a mixed sampling technique

Estimation of multivariate probability density function (PDF) of soil properties based on limited site investigation data plays an important role in data driven-based geotechnical engineering analysis and design. However, this estimation is often intractable because the site-specific investigation data generally are multivariate, uncertain and unique, sparse, incomplete, sometimes corrupt and spatially variable. The estimation is more challenging when the uncertainties in model parameters of the distribution (i.e., mean, standard deviations and scale of fluctuation of soil properties) are taken into consideration. To address these difficulties, this study proposed a mixed sampling technique by combing the Gibbs sampler and transitional Markov Chain Monte Carlo sampler to effectively estimate the multivariate probability density function of soil properties. The proposed method is demonstrated through a virtual site example while the effect of statistical uncertainty in the scale of fluctuation on reliability analyses is illustrated in an infinite slope example. It is shown that the proposed method can not only handle the complex features of site investigation data but also effectively quantify the statistical uncertainties of the mean values, standard deviations and scale of fluctuation of the investigated soil properties. Ignoring the statistical uncertainties of scale of fluctuation can affect the estimation of mean and standard deviations of the soil parameters. When considering the statistical uncertainty of scale of fluctuation, the 95% confidence intervals for the profiles of the soil properties can rationally cover the actual soil data at the unobserved points. Ignoring the statistical uncertainty of scale of fluctuation can lead to the underestimation of the probability of failure of the infinite slope.

Topography and canopy cover influence soil organic carbon composition and distribution across a forested hillslope in the discontinuous permafrost zone

AbstractTopography and canopy cover influence ground temperature in warming permafrost landscapes, yet soil temperature heterogeneity introduced by mesotopographic slope positions, microtopographic differences in vegetation cover, and the subsequent impact of contrasting temperature conditions on soil organic carbon (SOC) dynamics are understudied. Buffering of permafrost‐affected soils against warming air temperatures in boreal forests can reflect surface soil characteristics (e.g., thickness of organic material) as well as the degree and type of canopy cover (e.g., open cover vs. closed cover). Both landscape and soil properties interact to determine meso‐ and microscale heterogeneity of ground warming. We sampled a hillslope catena transect in a discontinuous permafrost zone near Fairbanks, Alaska, to test the small‐scale (1 to 3 m) impacts of slope position and cover type on soil organic matter composition. Mineral active layer samples were collected from backslope, low backslope, and footslope positions at depths spanning 19 to 60 cm. We examined soil mineralogical composition, soil moisture, total carbon and nitrogen content, and organic mat thickness in conjunction with an assessment of SOC composition using Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS). Soils in the footslope position had a higher relative contribution of lignin‐like compounds, whereas backslope soils had more aliphatic and condensed aromatic compounds as determined using FT‐ICR‐MS. The effect of open versus closed tree canopy cover varied with the slope position. On the backslope, we found higher oxidation of molecules under open cover than closed cover, indicating an effect of warmer soil temperature on decomposition. Little to no effect of the canopy was observed in soils at the footslope position, which we attributed, in part, to the strong impact of soil moisture content in SOC dynamics in the water‐gathering footslope position. The thin organic mat under open cover on the backslope position may have contributed to differences in soil temperature and thus SOC oxidation under open and closed canopies. Here, the thinner organic mat did not appear to buffer the underlying soil against warm season air temperatures and thus increased SOC decomposition as indicated by the higher oxidation of SOC molecules and a lower contribution of simple molecules under open cover than the closed canopy sites. Our findings suggest that the role of canopy cover in SOC dynamics varies as a function of landscape position and soil properties, namely, organic mat thickness and soil moisture. Condition‐specific heterogeneity of SOC composition under open and closed canopy cover highlights the protective effect of canopy cover for soils on backslope positions.

Upper Bound Collapse Load of Strip Footing that Rests on Unsaturated Sands

This paper attempts to investigate the bearing capacity of a strip footing that rested on unsaturated homogenous sands and used upper bound finite-element limit analysis in association with linear optimization. The analysis is based on suction stress-based (σs) effective stress (σ′) analysis. Unlike the saturated soil, the size of the yield surface varies with the vertical position, and therefore, proper modifications are carried out in the upper bound formulations to incorporate the unsaturated soil property functions. A detailed investigation is carried out to estimate the load-carrying capacity of a strip footing that vary the mechanical strength, unsaturated soil properties, water table depth (hw), and climatic conditions. The numerical solutions are presented in terms of bearing capacity factors [BCF (Nγs, Nqs)] for the unit weight (γw) and surcharge pressure (qs). The nodal velocity patterns are drawn for several cases, and the presented solutions are compared with the available results. The design charts provided in this paper might be useful to design engineers.

Effect of parameters associated with soil-to-structure relative stiffness on seismic fragility curves of subway station

Seismic response and vulnerability of subway station underground structures are greatly influenced by kinematic soil-structure interaction, which is a function of soil properties and stiffness. Therefore, this paper investigates the influence of parameters associated with soil-to-structure relative stiffness (F) on the seismic fragility curves of underground structures. Incremental dynamic analysis method is conducted employing finite element analysis to establish the fragility curves of the station structure considering 44 earthquake records. The soil was simulated as a two-dimensional (2D) finite element domain and its nonlinear behavior is described employing the Davidenkov constitutive model. Appropriate plasticity models simulated the nonlinearity of concrete and reinforcing rebars. It was found that the failure probability of the structure decreases significantly as soil shear wave velocity increases, and the change of probability of structural failure was quantified for changes in shear wave velocity within the range 200 m/s to 600 m/s. It was also found that the failure probability of subway station in heterogeneous soil is significantly higher than that of a structure buried in homogeneous soil. On the other hand, the cross-section type of the structure has less influence on its seismic vulnerability. The seismic performance of the structure with higher F is relatively better, while there are special cases where some structures with high F that are more susceptible to failure under earthquake than structures with lower F. The findings can provide helpful guide for the performance-based seismic design of underground structures.

Crystallization deformation and phase transitions of coarse-grained sulfate saline soils upon cooling

The volumetric expansion of saline soils under cooling could pose a threat to infrastructure constructed on these soils. This study examines the crystallization deformation characteristics of coarse-grained sulfate saline soils subject to stepwise cooling from 30 °C to −15 °C. Soil columns are prepared in plexiglass buckets with different fine contents, percent compaction, moisture contents, and sodium sulfate content. Cumulative heave and temperature evolution are measured when the specimens are maintained in a temperature-controlled environmental chamber (freezing in three dimensions). Upon cooling, salt and ice crystals grow in soil pores, leading to volumetric expansion. A model is proposed to describe the precipitation-induced deformation varying with temperature based on random forest (RF). The RF-based model reveals that the initial salt-to-water ratio exerts the most significant influence on the accumulated deformation, followed by moisture content, soil salinity, percent compaction, and soil gradation. The initial precipitation and freezing points (response variables) are expressed as two separate functions of soil properties (predictor variables) using the multivariate adaptive regression splines (MARS). The MARS-based models demonstrate that the precipitation points are mainly affected by the initial salt-to-water ratio and soil salinity; all factors contribute to the freezing point prediction, given the combined action of salt and ice crystallization. A threshold soil salinity of 33.33% existed in the evolution of precipitation points, while a threshold fine content of 35% and a threshold soil salinity of 39.68% were observed in freezing point predictions. The interaction mechanism of salt and ice crystallizations led to incredible difficulty capturing freezing points rather than precipitation points and predicting the sample heave. Although the evaluation metrics (RMSE, MAPE, and R2) confirm that the RF- and MARS-based models achieve the regression tasks well, more efforts are needed toward the second phase transition phenomenon and its implications for explaining thermally induced saline soil response. The findings may guide the utilization and management of land resources in saline areas.

Weed spatial distribution as a function of soil properties in two distinct environments of the Brazilian semi-arid region

Weed spatial distribution as a function of soil properties in two distinct environments of the Brazilian semi-arid region

Physical and numerical modelling of soil-atmosphere-structure interaction

Extreme, extended wet and dry seasons increase the adverse effects that soil wetting and drying cycles have on the response of shallow geotechnical structures. In expansive soils, volumetric changes due to water content variations may result in the incompatibility of deformations at the soil-structure interface. This study proposes a physical approach and a numerical model to address the soil-atmosphere-structure interactions during soil saturation and desiccation. Experimental desiccation tests were performed on relatively thin, compacted kaolin clay samples that represent the soil-atmosphere boundary. A climatic chamber was used to impose atmospheric conditions of air relative humidity, temperature, wind velocity, and irradiance on the soil surface. Empirical mathematical expressions were obtained to estimate soil desiccation rates as a function of basic atmospheric parameters and soil properties. The experimental desiccation approach was complemented with a coupled thermo-hydro-mechanical (THM) numerical model for unsaturated soils. The coupled THM model calculates water and thermal fluxes, soil volumetric changes, vertical stresses, and the compatibility of soil-structure movements during swelling and shrinking. An example of the capabilities of the numerical model is presented for a full-scale geotechnical problem.

Nutrition in cacao (Theobroma cacao L.) crops: What determining factors should be considered?

Cacao (Theobroma cacao L.) is an important commercial crop and agricultural commodity worldwide; for some Latin American countries, it is an essential part of export products. In Colombia, this crop has promising conditions to extend and strengthen this agriculture sector. However, their productivity is low under current agricultural practices, mainly due to insufficient modernization and inadequate or no management of their nutritional schemes. This publication reviewed the different findings currently in the scientific literature regarding the factors that determine the nutritional status of cacao plants, such as the function and distribution of minerals, nutritional efficiency, soil properties, establishment systems, organic and organic and inorganic sources examined. Additionally, it highlighted the importance of using and expanding diagnostic tools to determine nutritional needs and the design of effective programs according to the particular conditions of each region and the genotypes planted. This conceptual journey highlights the existing theoretical and experimental gap in the identification of the factors that determine the nutritional status of the plantations and their effect on the implementation of the fertilization programs used today. Information together provides elements to adequately address this agronomic practice and the economic impact on farmers and the cacao production chain.

Accumulation and vertical distribution of glomalin-related soil protein in French temperate forest soils as a function of tree type, climate and soil properties

Soil organic matter (SOM) is of critical importance to the functioning of ecosystems because of its effects on soil nutritional quality, microbial activity and soil structure. SOM is made up of a variety of compounds ranging from chemically reactive and mobile forms to stable, recalcitrant forms. An operationally defined fraction of soil organic matter known as glomalin-related soil protein (GRSP) is thought to be of fungal origin, very stable and responsible for increased physical stability of soil. To date, the origin of this fraction of SOM and the factors controlling its accumulation in soils are not well understood. The aim of this study was to contribute to the understanding of the origin of this fraction and the factors which contribute to its accumulation in the soil. This study was carried out on archived soils from 102 sites of the long-term monitoring network for forest ecosystems in France (RENECOFOR), from depths up to 1 m. The effects of tree type, climate and soil properties on the accumulation and vertical distribution of GRSP were assessed. GRSP was greater under conifer forests than broadleaf forests. GRSP in the top soil layer was positively correlated with both SOC and total nitrogen, and inversely related with pH, with the strongest effect observed at very acid (pH ≤ 4.0). Both GRSP and SOC were found to follow exponential declines with depth. The contrast in vertical distribution of GRSP and SOC supports the hypothesis that GRSP is a distinct fraction of SOM. Climate and soil properties had significant influence on the vertical distribution of GRSP. The accumulation of GRSP, and the enrichment of SOC in GRSP, at acid pH could indicate a fungal origin of GRSP.

Illuminating biodiversity changes in the ‘Black Box’

Soil is often described as a ‘black box’, as surprisingly little is known about the high levels of biodiversity that reside there. For aboveground organisms, we have good knowledge of the distribution of the species and how they might change under future human impacts. Yet despite the fact that soil organisms provide a wide variety of ecosystem functions, we have very limited knowledge of their distribution and how their diversity might change in the future. In order to create accurate and generalisable models of biodiversity, the underlying data need to be representative of the entire globe. Yet even with our recently compiled global earthworm dataset of over 11000 sites, there are gaps across large regions. These gaps are consistent across many other datasets of both above- and belowground diversity. In order to fill the gaps we propose a sampling network (SoilFaUNa), to create a comprehensive database of soil macrofauna diversity and soil functions (e.g. decomposition rates). Building on the existing dataset of earthworm diversity and early data from the SoilFaUNa project, we will investigate changes in earthworm diversity. From our current work, we know that both climate and land use are main drivers in predicting earthworm diversity, but both will change under future scenarios and may alter ecosystem functions. We will, using space-for-time substitution models, estimate how earthworm diversity and their functions might change in the future, modelling earthworm diversity as a function of climate, land use and soil properties and predicting based on future scenarios. Previous studies of aboveground diversity changes over time using time-series analysis have found no-net-loss in richness, but analyses have criticisms. We aim to use time-series data on earthworms to move this debate forward, by using data and statistical methods that would address the criticisms, whilst increasing our knowledge on this understudied soil group. Field experiments and micro-/mesocosm experiments have been used to investigate the link between a number of soil organisms and ecosystem functions under few environmental conditions. Meta-analyses, which can produce generalisable results can only answer questions for which there are data. Thus, we have been lacking on information on the link between the entire community of soil fauna and ecosystem functions and impact of changes to the soil fauna community across environmental contexts. Using data collected from the SoilFaUNa project, we will, for the first time, synthesise globally distributed specifically-sampled data to model how changes in the community composition of soil macrofauna (due to changes in land use, climate or soil properties) impact the ecosystem functions in the soil.

The Ecotoxicity of Sugarcane Pesticides to Non-target Soil Organisms as a Function of Soil Properties and Moisture Conditions

The Ecotoxicity of Sugarcane Pesticides to Non-target Soil Organisms as a Function of Soil Properties and Moisture Conditions

Application of deep neural networks to forecast 60Co soil to plant transfer factor values based in pedological parameters

The soil-plant transfer factor (Fv) is used methods in the computational models for radiological risk assessment by ingestion of radiocobalt-contaminated food. Different soil types, plants types and agricultural practices contribute to a wide dispersion of Fv values, indicating the need to study the criteria that influence root uptake in a regional view. In this scenario, Artificial Neural Networks (ANN) have become a possibility to predict Fv values based on critical pedological parameters. This work aims to apply ANN to evaluate the possibility of predicting Fv for 60Co in reference plants as a function of soil properties considered relevant for transfer processes in the soil-plant system. Through the systematic literature review, mineralogy, organic matter, texture, pH, CEC and nutrients were identified as soil properties that affect Fv values for 60Co. However, although these attributes were not always reported, still it was possible to create databases of Fv for 60Co in radish root and leaf, with pH, organic matter, and CTC as potential edaphic indicators. Learning sets were structured and due to the complexity of the search space and the small amount of available data, deep ANN with regularization (dropout) layers were required to achieve good prediction and avoid overfitting. The best model obtained showed good correlation in the validation and training set, considering the chosen parameters.

Flood aftermath on soil quality in north central laterites of Kerala

Soil quality mostly refers to the dynamic properties of surface soil (0 to 25 cm) that can be strongly altered by management practices. The study aimed at the assessment of soil quality in the post-flood soils and the findings were quantified by expressing as a function of measurable soil properties. Among the 23 parameters analyzed on 100 geo-referenced soil samples, soil pH, bulk density, available nitrogen (N), phosphorus (P), calcium (Ca), copper (Cu), boron (B) and microbial biomass carbon (MBC) were found as key indicators influencing soil quality. Depending on the contribution of indicators to soil quality they were categorised, scored and finally, weighted indicators were summarised into soil quality index (SQI) using standard formula. The contribution of pH and bulk density was highest to soil quality index while, the lowest contribution was shared by available B. The SQI was highest in Pariyaram Panchayat, where the analytical value of key indicators were near to optimum range and lowest in Meloor due to the poor content of available N and MBC. The deviation in soil quality was measured and expressed in terms of relative soil quality index (RSQI), which was found to be medium in 82% of the samples. Improvement in SQI can be achieved by optimizing the key soil indicators in each site

The contribution of organic acid on heterotrophic CO2 flux from tropical peat: a trenching study

Quantification of CO2 flux from peat has been studied with various methods of measurement and data analysis. Several studies have applied regression analysis to assess carbon flux from tropical peatland as a function of groundwater level. Such an analysis simplified the complex nature of peat decomposition, which involved microbial activities. The study was conducted at Buatan Village, Siak Indrapura Regency, Riau Province, Indonesia. Soil sampling was done every month for a year observation, from July 2018 to June 2019. This study aimed to comprehend CO2 production from the respiration of heterotrophic components (Rh-CO2) as a function of soil properties determined by soil pH, N-NH4, N-NO3, available P, exchangeable-K, C-organic acids, and environmental factors that are determined by soil water content, and groundwater level. The study applied trenching experimentation to quantify Rh-CO2 flux by first removing plant roots from the trenching plot. The CO2 flux and groundwater level were measured for five consecutive days each month for a one-year period. Multiple regression analysis was performed to determine the main determinant for the Rh-CO2 flux. The results showed that seasonal fluctuation of Rh-CO2 flux, negatively correlated with available P (p = 0.037), and positively (p = 0.018) with C-substrate as C-organic acids but not with either of the speciated ones as acetic, lactic, citric, malic, nor oxalic acids. More specifically, the C-organic acids were found as the main determinant factor (p = 0.039) affecting the Rh-CO2 flux.

Myths and misconceptions related to unsaturated soil mechanics

There have been three main pillars associated with the development of an applied engineering science for both saturated and unsaturated soil mechanics; namely, i) the synthesis of continuum mechanics theories of physical behavior, ii) the laboratory measurement of relevant soil properties, and iii) analyses that illustrate the solution of practical example problems. Geotechnical engineers have, however, been relatively slow in adopting unsaturated soil mechanics into geotechnical engineering practice. There have been several so-called “myths or misconceptions” that appear to have hindered the application of unsaturated soil mechanics. This paper attempts to describe and dispel what are deemed to be misconceptions related to the more general implementation of unsaturated soil mechanics into engineering practice. The so-called “myths” come from the acceptance of false information related to unsaturated soil behavior and a hesitancy to embrace changes to existing empirical protocols. Several misconceptions are identified in the paper that are related to: i) complexity of unsaturated soil mechanics theories, ii) inability to readily measure soil suctions in-situ, iii) the nonlinearity of unsaturated soil property functions, iv) permanency of soil suctions above the water table, v) difficulties associated with assessing ground surface moisture flux conditions, and vi) difficulties associated with numerical modeling that involves solving nonlinear partial differential equations. Each of the above-mentioned items are dealt with as myths or misconceptions in the sense of being impediments to the application of unsaturated soil mechanics in geotechnical engineering practice.

Adapting the CROPGRO model to simulate growth and production of Brassica carinata, a bio‐fuel crop

AbstractCarinata (Brassica carinata) is an oilseed crop which, because of its non‐edible oil composition and favorable fatty acid profile, is proposed as a “green” sustainable aviation fuel. It can be grown as a winter crop in the southeastern USA or as a summer annual crop in northern latitudes. No crop models exist for carinata because it is a relatively new crop. The CROPGRO model is a mechanistic crop simulation of daily crop growth and development as a function of daily weather, soil properties, crop management, and species parameters. We adapted the CROPGRO model to simulate carinata based on growth analysis data collected over two seasons at three sites: Quincy, FL, Jay, FL, and Shorter, AL. The adaptation process required literature knowledge as well as optimization against field observations. The parameterization of model sensitivities to climatic factors is presented. The adapted model gave good simulations of carinata growth dynamics compared to observed growth during different seasons and locations and in response to N fertilization. While additional testing is appropriate, the model is sufficiently ready to be used for various applications. An example application is presented for the effect of sowing date on carinata yield and maturity over long‐term weather in the Southeastern USA.