Simulated Leaf Area Index Research Articles

Cross-scale evaluation of dynamic crop growth in WRF and Noah-MP-Crop

Accurately representing croplands in climate models is important for simulating water and energy fluxes between the land and atmosphere, as well as evaluating the impacts of climate change on agriculture. The recent integration of dynamic crop growth in the Noah land surface model with multiparameterization (Noah-MP-Crop) has the potential to substantially advance Earth system modeling and is included in the latest release of the Weather Research and Forecasting (WRF) regional climate model. The addition of dynamic crop growth to WRF provides a unique opportunity to simultaneously evaluate biases associated with a crop model coupled to a regional climate model and address outstanding questions regarding the role of agroecosystems in modulating regional climate. Here, we analyze dynamic crop growth in WRF across three simulated spatial scales (25km, 5km, and 1km) for growing seasons with precipitation above (2010), below (2012), and approximately equal (2015) to the seasonal average. Including dynamic crop growth in WRF significantly reduces biases in simulated leaf area index over croplands in the central U.S. relative to observations. However, there is no substantial difference in calculated daily evapotranspiration, average growing season temperature, or total growing season precipitation between WRF simulations with dynamic crops (WRF-Crop) compared to the dynamic vegetation module without crops (WRF-DV). Simulated corn (soy) mean absolute error (MAE), as a percentage of observed annual average yield, ranges from 24.7% -101% (28.1% - 109%) depending on year and spatial resolution, with the most significant biases in highly irrigated counties. Forcing Noah-MP-Crop with observed climate substantially reduces the range of corn (soy) yield MAE to 9.5% - 55.1% (15.0% - 37.5). Increased model resolution consistently leads to lower corn and soy yield estimates within WRF-Crop.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

Modification of CSM-CROPGRO-Cotton model for simulating cotton growth and yield under various deficit irrigation strategies

Cotton (Gossypium hirsutum L.) is one of the main crops in Southern Xinjiang of China, and it is the major component of the regional economy. However, cotton cultivation in this region is facing severe challenges, e.g., the projected increase in the frequency of droughts and the decrease in precipitation in the future. Development and evaluation of deficit irrigation strategies in this region can potentially conserve irrigation water while maintaining cotton yields. In this study, the actual soil evaporation was introduced into the calculation process of stress factors in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM-CROPGRO-Cotton) to simulate and evaluate the growth and yield of drip-irrigated cotton under mulching in response to various deficit irrigation strategies. The modified CSM-CROPGRO-Cotton model was calibrated using field data under full irrigation (100% reference evapotranspiration (ET0)) and validated by deficit irrigation data (0.4 ET0, 0.6 ET0 and 0.8 ET0 at the bud, flowering and boll stages) during the two cotton growing seasons of 2018 and 2019. The results showed that the modified model CSM-CROPGRO-Cotton simulated the growth index of cotton well under various deficit irrigation conditions. The index of agreement (d) of the simulated and measured biomass was greater than 0.845, and the coefficient of determination (R2) was greater than 0.967 (P < 0.01). Although the simulation of leaf area index was not as good as biomass, it also yielded satisfactory estimates, with d > 0.934 and R2 > 0.817 (P < 0.01). The simulated photoperiods in various deficit irrigation treatments were consistent with the observations. The averaged absolute relative error (ARE) in seed cotton yield over the deficit irrigation treatments was 2.1%, indicating high simulation accuracy. The results showed that the modified CSM-CROPGRO-Cotton model was appropriate for simulating cotton growth and yield under various water deficit strategies, which is a reasonable decision support system in managing irrigation for sustainable agriculture. The simulations also indicated that adopting deficit irrigation practices under normal weather conditions could conserve water without adversely affecting seed cotton yield, but there was a risk of increased yield loss under dry conditions.

Linking an agro-meteorological model and a water cloud model for estimating soil water content over wheat fields

Agricultural drought has great impact on crop yields. Soil water content can directly reflect the degree of agricultural drought. The Water Cloud Model (WCM) is a vegetation contribution model which has been widely used in soil water content retrieval over vegetated areas. However, when optical remote sensing images are absent or are of a poor quality due to cloud cover and haze, the WCM parameters, describing the characteristics of vegetation layer, are hard to obtain. In order to effectively retrieve the soil water content over wheat fields, an agro-meteorological model, Simple Algorithm For Yield estimate (SAFY) model, was introduced to simulate the daily values of the WCM parameters during the whole winter wheat growth stages. Firstly, the leaf area index (LAI), retrieved from two Sentinel-2 (high-resolution optical) satellite images, was used to calibrate the parameters of the SAFY model in the Guanzhong Plain, PR China. The calibrated SAFY model was applied to simulate daily LAI. Then, Sentinel-1 backscatter coefficient, average ground measured soil water content from a depth of 0–12 cm and the simulated LAI were used to calibrate the WCM parameters to retrieve soil water content. In a final step, the soil water content retrieval results were validated at rain-fed and irrigated sampling sites, respectively. The SAFY model simulates the LAI of the Sentinel-1 sensing dates and uses it as an accurate vegetation description parameter for the WCM, which effectively solves the problem that the vegetation description parameter in the WCM cannot accurately describe the surface vegetation state at the time of radar satellite passing territory due to the Sentinel-1 and Sentinel-2 data in the study area cannot be acquired on the same day, and thus improving the accuracy of the WCM soil water content retrieval. For all sampling sites, the soil water content retrieval results have a satisfactory overall accuracy, and the soil water content retrieval results from rain-fed sampling sites have better accuracy than those from irrigated sampling sites. The soil water content retrieval results at the harvesting stage of winter wheat have the best accuracy among the three field experiments (R2 = 0.66; RMSE = 2.24%), followed by the tillering stage (R2 = 0.56; RMSE = 2.56%) and jointing stage (R2 = 0.36; RMSE = 2.74%). These results indicated that the combination of SAFY model and WCM can effectively retrieve the soil water content at different growth stages of winter wheat in the absence of optical remotely sensed images, and can provide significant benefit for soil water content retrieval over agricultural fields at the regional scale.

Calibration and validation of SiBcrop Model for simulating LAI and surface heat fluxes of winter wheat in the North China Plain

The accurate representation of surface characteristic is an important process to simulate surface energy and water flux in land-atmosphere boundary layer. Coupling crop growth model in land surface model is an important method to accurately express the surface characteristics and biophysical processes in farmland. However, the previous work mainly focused on crops in single cropping system, less work was done in multiple cropping systems. This article described how to modify the sub-model in the SiBcrop to realize the accuracy simulation of leaf area index (LAI), latent heat flux (LHF) and sensible heat flux (SHF) of winter wheat growing in double cropping system in the North China Plain (NCP). The seeding date of winter wheat was firstly reset according to the actual growing environment in the NCP. The phenophases, LAI and heat fluxes in 2004–2006 at Yucheng Station, Shandong Province, China were used to calibrate the model. The validations of LHF and SHF were based on the measurements at Yucheng Station in 2007–2010 and at Guantao Station, Hebei Province, China in 2009–2010. The results showed the significant accuracy of the calibrated model in simulating these variables, with which the R2, root mean square error (RMSE) and index of agreement (IOA) between simulated and observed variables were obviously improved than the original code. The sensitivities of the above variables to seeding date were also displayed to further explain the simulation error of the SiBcrop Model. Overall, the research results indicated the modified SiBcrop Model can be applied to simulate the growth and flux process of winter wheat growing in double cropping system in the NCP.

Damage evaluation of soybean chilling injury based on Google Earth Engine (GEE) and crop modelling

Frequent chilling injury has serious impacts on national food security and in northeastern China heavily affects grain yields. Timely and accurate measures are desirable for assessing associated large-scale impacts and are prerequisites to disaster reduction. Therefore, we propose a novel means to efficiently assess the impacts of chilling injury on soybean. Specific chilling injury events were diagnosed in 1989, 1995, 2003, 2009, and 2018 in Oroqen community. In total, 512 combinations scenarios were established using the localized CROPGRO-Soybean model. Furthermore, we determined the maximum wide dynamic vegetation index (WDRVI) and corresponding date of critical windows of the early and late growing seasons using the GEE (Google Earth Engine) platform, then constructed 1600 cold vulnerability models on CDD (Cold Degree Days), the simulated LAI (Leaf Area Index) and yields from the CROPGRO-Soybean model. Finally, we calculated pixel yields losses according to the corresponding vulnerability models. The findings show that simulated historical yield losses in 1989, 1995, 2003 and 2009 were measured at 9.6%, 29.8%, 50.5%, and 15.7%, respectively, closely (all errors are within one standard deviation) reflecting actual losses (6.4%, 39.2%, 47.7%, and 13.2%, respectively). The above proposed method was applied to evaluate the yield loss for 2018 at the pixel scale. Specifically, a sentinel-2A image was used for 10-m high precision yield mapping, and the estimated losses were found to characterize the actual yield losses from 2018 cold events. The results highlight that the proposed method can efficiently and accurately assess the effects of chilling injury on soybean crops.

Adapting the CROPGRO model to simulate chia growth and yield

AbstractChia (Salvia hispanica L.) seeds are becoming increasingly popular as a superfood in Europe. However, broad experience in growing chia in temperate climates is missing. Crop simulation models can be helpful tools for management and decision‐making in crop production systems in different regions. The objective of this study was to adapt the CROPGRO model for simulating growth and yield of chia. Data sets from a field experiment conducted over 2 yr in southwestern Germany (48°74′ N, 08°92′ E, 475 m above sea level) were used for model adaptation. The initial starting point was the CROPGRO–soybean [Glycine max (L.) Merr.] model as a template for parameterizing temperature functions and setting tissue composition. Considerable iterations were made in optimizing growth, development, and photosynthesis parameters. After model calibration, the simulation of leaf area index (LAI) was reasonable for both years, slightly over‐predicting LAI with an average d‐statistic of 0.95 and root mean square error (RMSE) of 0.53. Simulations of final leaf number were close to the observed data with d‐statistic of 0.98 and RMSE of 1.36. Simulations were acceptable for total biomass (d‐statistic of 0.93), leaf (d‐statistic of 0.94), stem (d‐statistic of 0.94), pod mass (d‐statistic of 0.89), and seed yield (d‐statistic of 0.88). Pod harvest index (HI) showed good model performance (d‐statistic of 0.96 and RMSE of 0.08). Overall, the model adaptation resulted in a preliminarily adapted model with realistically simulated crop growth variables. Researchers can use the developed chia model to extend knowledge on the eco‐physiology of chia and to improve its production and adaption to other regions.

Capability of a solar energy-driven crop model for simulating water consumption and yield of maize and its comparison with a water-driven crop model

As water scarcity becomes more acute in many parts of the world, crop modeling tools that effectively simulating crop response to deficit irrigation strategies to help investigate management improvement are needed. Identifying the strengths and weaknesses of the crop models with different growth-engines are therefore of great importance. The objective of this study was to investigate the capability and improvements of the new version of a solar energy-driven crop model (DSSAT-CERES-Maize, v4.7.5.0) in simulating water consumption and yield of hybrid seed maize under different soil water conditions, and its comparison with a water-driven crop model (AquaCrop, v4.0). Data obtained from a 4-year (2012–2015) field trial on maize grown under different irrigation treatments at Wuwei, Northwest China, was used for this assessment. These models were calibrated and validated using measured daily evapotranspiration (ET), leaf area index (LAI), aboveground biomass, yield (Y), harvest index (HI) and soil water content (SWC). Daily ET was measured using a combination of an eddy covariance (EC) system, sap flow sensors, and micro-lysimeter cylinders. The ability of DSSAT-CERES-Maize using the two different ET options, i.e., Priestley-Taylor/Ritchie (PT) and FAO-56 Penman-Monteith (PM) was analyzed. The results showed that DSSAT-CERES-Maize with the PT approach had fair agreement with measured daily ET of maize under non-water stress condition (R2=0.85; NRMSE=26.7%), but poor agreement with ET under water stress conditions (R2=0.51; NRMSE=43.8%). DSSAT-CERES-Maize with the PM approach systematically underestimated ET by up to 13% under non-water stress condition, which was mainly attributed to that the maximum static CERES-Maize crop coefficient (EORATIO) was currently hard coded to 1.0. Using the PT or FAO-56 PM approach as ET input in DSSAT-CERES-Maize showed no different effect on final biomass (B) and Y simulation for full irrigation. But for water stress conditions, DSSAT-CERES-Maize with the FAO-56 PM approach simulated B and Y with higher overestimation to the measured data than those simulated using the PT approach. The simulated LAI, biomass and SWC by DSSAT-CERES-Maize using the PT approach generally well followed the trend of the measured values for most irrigation treatments. The model with the PT approach showed acceptable prediction for B and Y of different irrigation treatments across years, with NRMSE of 15.5% and 26.2%, respectively, but the accuracy decreased with an aggravation of water stress. Furthermore, the strengths and weaknesses of DSSAT-CERES-Maize and AquaCrop, and their different cores of growth-engines, i.e. RUE and normalized water productivity (WP*) were carefully discussed. It was concluded that DSSAT-CERES-Maize was a superior estimate of maize yield than was AquaCrop, especially when the climate varied dramatically between years. But DSSAT-CERES-Maize, for the simulation of maize water consumption in an arid region where drought often occurs, was inferior to AquaCrop. These results contribute to recommend the appropriate crop model for specific modeling goals.

Analysing the behaviour of a hazelnut simulation model across growing environments via sensitivity analysis and automatic calibration

Hazelnut is an emerging tree crop in the global market and current production levels are unable to sustain the demand for fruits. Research efforts are needed to support private and public stakeholders with knowledge that can help improving the management of the agricultural systems, by enhancing yield levels and safeguarding environmental sustainability. This paper extends the application of a process-based hazelnut simulation model from the single orchard where it was initially calibrated to four orchards located in Italy, Chile and Georgia, using three-year experimental datasets (2015–2017). The model sensitivity to parameter changes was assessed twice (screening and quantitative analysis) to identify the most relevant parameters affecting the prediction of leaf area index, fruit yield and soil moisture. These parameters were automatically calibrated to maximize the model accuracy in predicting field data, by comparing model performances with original parameterization. According to current model structure, canopy expansion and plant dimensions accounted for most of the variability in leaf area index simulation. Photosynthesis and root water uptake played a prominent role in affecting the model predictions of yield and soil moisture, respectively. The automatic calibration increased the accuracy in predicting leaf area index (average RRMSE = 19.3%), fruit yield (average RRMSE = 16.3%) and soil moisture (average RRMSE = 12.5%). This study provides quantitative information to support calibration activities and insights for collection of new experimental data in the short-term, while laying the groundwork for the use of the model for yield forecasting activities as the medium goal.

FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics

Abstract. We present FORHYCS (FORests and HYdrology under Climate Change in Switzerland), a distributed ecohydrological model to assess the impact of climate change on water resources and forest dynamics. FORHYCS is based on the coupling of the hydrological model PREVAH and the forest landscape model TreeMig. In a coupled simulation, both original models are executed simultaneously and exchange information through shared variables. The simulated canopy structure is summarized by the leaf area index (LAI), which affects local water balance calculations. On the other hand, an annual drought index is obtained from daily simulated potential and actual transpiration. This drought index affects tree growth and mortality, as well as a species-specific tree height limitation. The effective rooting depth is simulated as a function of climate, soil, and simulated above-ground vegetation structure. Other interface variables include stomatal resistance and leaf phenology. Case study simulations with the model were performed in the Navizence catchment in the Swiss Central Alps, with a sharp elevational gradient and climatic conditions ranging from dry inner-alpine to high alpine. In a first experiment, the model was run for 500 years with different configurations. The results were compared against observations of vegetation properties from national forest inventories, remotely sensed LAI, and high-resolution canopy height maps from stereo aerial images. Two new metrics are proposed for a quantitative comparison of observed and simulated canopy structure. In a second experiment, the model was run for 130 years under climate change scenarios using both idealized temperature and precipitation change and meteorological forcing from downscaled GCM-RCM model chains. The first experiment showed that model configuration greatly influences simulated vegetation structure. In particular, simulations where height limitation was dependent on environmental stress showed a much better fit to canopy height observations. Spatial patterns of simulated LAI were more realistic than for uncoupled simulations of the forest landscape model, although some model deficiencies are still evident. Under idealized climate change scenarios, the effect of the coupling varied regionally, with the greatest effects on simulated streamflow (up to 60 mm yr−1 difference with respect to a simulation with static vegetation parameters) seen at the valley bottom and in regions currently above the treeline. This case study shows the importance of coupling hydrology and vegetation dynamics to simulate the impact of climate change on ecosystems. Nevertheless, it also highlights some challenges of ecohydrological modeling, such as the need to realistically simulate the plant response to increased CO2 concentrations and process uncertainty regarding future land cover changes.

Assessment of a Proximal Sensing-integrated Crop Model for Simulation of Soybean Growth and Yield

A remote sensing-integrated crop model (RSCM) able to simulate crop growth processes using proximal or remote sensing data was formulated for simulation of soybean through estimating parameters required for modelling. The RSCM-soybean was then evaluated for its capability of simulating leaf area index (LAI), above-ground dry mass (AGDM), and yield, utilising the proximally sensed data integration into the modelling procedure. Field experiments were performed at two sites, one in 2017 and 2018 at Chonnam National University, Gwangju, and the other in 2017 at Jonnam Agricultural Research and Extension Services in Naju, Chonnam province, South Korea. The estimated parameters of radiation use efficiency, light extinction coefficient, and specific leaf area were 1.65 g MJ−1, 0.71, and 0.017 m2 g−1, respectively. Simulated LAI and AGDM values agreed with the measured values with significant model efficiencies in both calibration and validation, meaning that the proximal sensing data were effectively integrated into the crop model. The RSCM reproduced soybean yields in significant agreement with the measured yields in the model assessment. The study results demonstrate that the well-calibrated RSCM-soybean scheme can reproduce soybean growth and yield using simple input requirement and proximal sensing data. RSCM-soybean is easy to use and applicable to various soybean monitoring projects.

Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Mathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees.

Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information

AbstractAgroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field‐scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics‐based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm3 cm−3, respectively. The AgroC model was then used to simulate the growth of sugar beet (Beta vulgaris L.), silage maize (Zea mays L.), potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and winter rapeseed (Brassica napus L.) in the 1‐ by 1‐km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAINDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics‐based soil mapping was valuable for practical agricultural applications.

Ứng dụng mô hình CERES-Maize mô phỏng năng suất bắp lai trồng trên đất phù sa ở Đồng bằng sông Cửu Long

The objectives of this study were to calibrate and validate the CERES-Maize model and simulate the response of maize growth and yield to different doses of nitrogen and organic fertilizers in alluvial soil. Field experiments were carried out from August 2015 to April 2016 at the Soil Science Department, Can Tho University to collect data for model calibration and validation. The calibrated and validated results showed that there were good agreements between simulated and observed data, indicating through the statistical parameters, i.e. yield (EF:0.85 – 0.99; RMSE:181.49 – 669.71 kg/ha; nRMSE: 3.87 – 12.13 %); above-ground biomass (EF:0.97 – 0.98; RMSE:672.91 – 942.80 kg/ha;, nRMSE: 8.01 – 10.39 %); leaf number (EF: 0.90 – 0.95; nRMSE: 7.81 – 12.04 %. Meanwhile, the simulated leaf area index was rather good agreement to the observed data, i.e. EF:0.69 – 0.82 và nRMSE: 15.65 – 20.47%. Maximum temperature and rainfall were the parameters having the highest sensitivity among the others.

Adapting the APEX Model to Simulate Detasseling in Inbred Corn for Hybrid Seed Production

HighlightsThe APEX model was adapted to simulate detasseling in inbred corn for hybrid seed production.The adapted model satisfactorily predicted detasseling effects on LAI, grain yield, and N content.An inbred corn model could be applied to evaluate best management practices for inbred corns.Abstract. Hybrid seed corn production comprises approximately 10% of the entire corn acreage in the U.S. Because of seed corn’s high economic value, and to maximize yields, seed corn growers often over-irrigate or apply nitrogen (N) fertilizers equal to or in excess of those recommended for commercial hybrid corn. Detasseling female corn inbred lines during hybrid corn seed production is critical to ensure the purity of seeds. In addition to the removal of tassels, detasseling also results in the removal of several leaves, which may lead to reduced seed yields. The objective of this study was to adapt the Agricultural Policy/Environmental eXtender (APEX) model to simulate the detasseling of female inbred corns in hybrid seed production. An APEX inbred corn model was developed to simulate the effects of detasseling and leaf removals on the development of inbred corn, leaf area index (LAI), grain yield, and grain N content. Growth characteristics of inbred corn were parameterized in APEX using data from a field study conducted in Nebraska. Overall, the APEX inbred corn model successfully predicted the effects of detasseling on LAI, grain yield, and grain N content under the conditions of the field experiment. There was a significant correlation between simulated and measured LAI (Pearson r = 0.86 and R2 = 0.74 at p = 0.05). The computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated LAI. The mean simulation percent difference and percent bias (PBIAS) were respectively 4.2% and 4.7%, while measured and simulated LAI values had an average root mean square error (RMSE) of 0.14. The APEX model predicted grain yield with RMSE of 120 kg ha-1, mean simulation percent difference of 0.48%, and PBIAS of 0.26%. Like LAI, predicted grain yields exhibited significant correlation with field data (Pearson r = 0.99 and R2 = 0.97 at p = 0.05). Similarly, computed paired t-test and permutation test p-values indicated no significant differences between measured and simulated grain yields. Grain N content was predicted with RMSE of 6.75 kg N ha-1, mean simulation percent difference of 1.46%, and PBIAS of 2.45%. Predicted and measured grain N content values were correlated (Pearson r = 0.81 and R2 = 0.65 at p = 0.05), while the t-test and permutation test p-values indicated no significant differences between measured and predicted grain N content. Overall, detasseling effects were better predicted for grain yield than for LAI and grain N content as indicated by a Nash-Sutcliffe efficiency (NSE) of 0.92 compared to NSE values of 0.47 for LAI and 0.43 for grain N content. In conclusion, the hybrid seed corn industry could benefit from the application of inbred corn models that could allow growers to evaluate and identify optimal irrigation and N management practices for inbred corn, similar to the benefits that have been obtained with model simulation for commercial hybrid corn grain production systems. Keywords: APEX parameterization, Detasseling, Inbred corn, Leaf area index.

Monitoring and Predicting Agricultural Droughts for a Water-Limited Subcontinental Region by Integrating a Land Surface Model and Microwave Remote Sensing

Agricultural drought monitoring and prediction technology are urgently needed. We applied an ecohydrological land data assimilation system (LDAS), which can simulate soil moisture and leaf area index (LAI) by data assimilation of microwave brightness temperature into a land surface model (LSM), to monitor and predict agricultural droughts in North Africa. We successfully monitor nationwide crop failures, which are characterized by the declines of the nationwide wheat production, in Morocco, Algeria, and Tunisia using LAI and soil moisture calculated by the LDAS. Our simulated LAI is well correlated with the nationwide wheat production ( $r= 0.70$ , 0.65, and 0.72 in Morocco, Algeria, and Tunisia, respectively). A general circulation model (GCM)-based seasonal meteorological prediction significantly contributes to accurately predicting LAI and agricultural droughts in 2–3-month lead time. In addition, it is found that initial conditions have an important role in predicting LAI. We demonstrate the capability of our framework to monitor and predict agricultural drought in North Africa. Our proposed framework can contribute to mitigating the negative impact of drought on agriculture in poorly gauged water-limited subcontinental regions.

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACTWe designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.

Evaluation of the Uncertainty in Satellite-Based Crop State Variable Retrievals Due to Site and Growth Stage Specific Factors and Their Potential in Coupling with Crop Growth Models.

Coupling crop growth models and remote sensing provides the potential to improve our understanding of the genotype x environment x management (G × E × M) variability of crop growth on a global scale. Unfortunately, the uncertainty in the relationship between the satellite measurements and the crop state variables across different sites and growth stages makes it difficult to perform the coupling. In this study, we evaluate the effects of this uncertainty with MODIS data at the Mead, Nebraska Ameriflux sites (US-Ne1, US-Ne2, and US-Ne3) and accurate, collocated Hybrid-Maize (HM) simulations of leaf area index (LAI) and canopy light use efficiency (LUECanopy). The simulations are used to both explore the sensitivity of the satellite-estimated genotype × management (G × M) parameters to the satellite retrieval regression coefficients and to quantify the amount of uncertainty attributable to site and growth stage specific factors. Additional ground-truth datasets of LAI and LUECanopy are used to validate the analysis. The results show that uncertainty in the LAI/satellite measurement regression coefficients lead to large uncertainty in the G × M parameters retrievable from satellites. In addition to traditional leave-one-site-out regression analysis, the regression coefficient uncertainty is assessed by evaluating the retrieval performance of the temporal change in LAI and LUECanopy. The weekly change in LAI is shown to be retrievable with a correlation coefficient absolute value (|r|) of 0.70 and root-mean square error (RMSE) value of 0.4, which is significantly better than the performance expected if the uncertainty was caused by random error rather than secondary effects caused by site and growth stage specific factors (an expected |r| value of 0.36 and RMSE value of 1.46 assuming random error). As a result, this study highlights the importance of accounting for site and growth stage specific factors in remote sensing retrievals for future work developing methods coupling remote sensing with crop growth models.

Simulation of cotton growth and soil water content under film-mulched drip irrigation using modified CSM-CROPGRO-cotton model

Cotton is one of the major cash crops in Xinjiang, an arid region in northwestern China. Most cotton fields in this region are drip irrigated with plastic film mulch. However, few studies have tested the performance of the DSSAT (Decision Support System for Agrotechnology Transfer) model on cotton under film-mulched drip irrigation. The objective of this study is to evaluate the performance of the modified CSM-CROPGRO-Cotton model and determine the genetic coefficients of cotton using data from different irrigation treatments conducted at the irrigation experiment station in Bayingol Mongolian Autonomous Prefecture, Korla, China. Based on the selected genetic coefficients, the model was calibrated using field experiment datasets in 2016 and validated using datasets in 2015. The simulation results of soil water content, leaf area index, phenological period and cotton yield were compared with their corresponding measurements. Absolute relative error (ARE) of seeding emergence date, flowing date, maturity date and cotton yield simulated by the original model were 5.8%, 7.3%, 5.0% and 70.6%, respectively, while those of the modified model were 1.7%, 1.1%, 1.1% and 0.1%, respectively. Simulated leaf area index agreed well with the field observations with the coefficient of determination (R2) > 0.739, the index of agreement (d) > 0.910 and the root mean square error (RMSE) < 0.965. Simulated soil water content in the 20–40 cm soil layer were generally consistent with the measured values with R2 = 0.724, d = 0.639 and RMSE = 0.087 cm3/cm3, while the simulated soil water content in the 40–60 cm was slightly worse than the observations with R2 = 0.708, d = 0.519 and RMSE = 0.109 cm3/cm3. Overall, the modified CSM-CROPGRO-Cotton model can be used as a practical tool to simulate the cotton growth under film-mulched drip irrigation in this region. Potential opportunities for the model improvement in cotton growth include the influence of various scenarios such as soil types, climates and irrigation strategies.

Towards a Long-Term Reanalysis of Land Surface Variables over Western Africa: LDAS-Monde Applied over Burkina Faso from 2001 to 2018

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.