Low Data Requirements Research Articles

Greenhouse gas emission prediction on road network using deep sequence learning

Mitigating the substantial undesirable impact of transportation systems on the environment is paramount. Thus, predicting Greenhouse Gas (GHG) emissions is one of the profound topics, especially with the emergence of intelligent transportation systems (ITS). We developed a deep learning framework to predict link-level GHG emission rate (ER) (in CO2eq gram/second) based on the most representative predictors, such as speed, density, and GHG ER of previous time steps. In particular, various specifications of the long short-term memory (LSTM) networks with explanatory variables were examined, and were compared with clustering and the autoregressive integrated moving average (ARIMA) model with explanatory variables. The downtown Toronto road network was used as the study area, and highly detailed data were synthesized using a calibrated traffic microsimulation and MOVES. It was found that LSTM specification with speed, density, GHG ER, and in-links speed from three previous minutes performed the best while adopting two hidden layers, and when the hyper-parameters were systematically tuned. Adopting a 30-second updating interval slightly improved the correlation between true (simulated) and predicted GHG ERs (from predictive models), but contributed negatively to the prediction accuracy as reflected in the increased root mean square error (RMSE) value. Efficiently predicting GHG emissions at a higher frequency with lower data requirements will pave the way for various applications, e.g. anticipatory eco-routing in large-scale road networks to alleviate the adverse impact on global warming.

Sensitivity and Interdependency Analysis of the HBV Conceptual Model Parameters in a Semi-Arid Mountainous Watershed

Hydrological models, with different levels of complexity, have become inherent tools in water resource management. Conceptual models with low input data requirements are preferred for streamflow modeling, particularly in poorly gauged watersheds. However, the inadequacy of model structures in the hydrologic regime of a given watershed can lead to uncertain parameter estimation. Therefore, an understanding of the model parameters’ behavior with respect to the dominant hydrologic responses is of high necessity. In this study, we aim to investigate the parameterization of the HBV (Hydrologiska Byråns Vattenbalansavedelning) conceptual model and its influence on the model response in a semi-arid context. To this end, the capability of the model to simulate the daily streamflow was evaluated. Then, sensitivity and interdependency analyses were carried out to identify the most influential model parameters and emphasize how these parameters interact to fit the observed streamflow under contrasted hydroclimatic conditions. The results show that the HBV model can fairly reproduce the observed daily streamflow in the watershed of interest. However, the reliability of the model simulations varies from one year to another. The sensitivity analysis showed that each of the model parameters has a certain degree of influence on model behavior. The temperature correction factor (ETF) showed the lowest effect on the model response, while the sensitivity to the degree-day factor (DDF) highly depends on the availability of snow cover. Overall, the changes in hydroclimatic conditions were found to be mostly responsible for the annual variability of the optimal parameter values. Additionally, these changes seem to actuate the interdependency between the parameters of the soil moisture and the response routines, particularly Field Capacity (FC), the recession coefficient K0, the percolation coefficient (KPERC), and the upper reservoir threshold (UZL). The latter combines either to shrink the storage capacity of the model’s reservoirs under extremely high peak flows or to enlarge them under overestimated water supply, mainly provoked by abundant snow cover.

Linkages-based indicators of production-source sectoral eco-efficiency with application to Polish data

The goal of this paper is twofold. First, a new approach to measuring production-source sectoral eco-efficiency in generalized input-output models is presented. The procedure may be used as a supplementary method to the traditional framework that is usually based on various decomposition techniques. Due to its simplicity and low data requirements, the approach provides a useful and flexible tool that allows a more general insight to be obtained into levels of emissions associated with production, which in turn is essential for constructing more sound indicators of eco-efficiency. Second, in order to trace the factors driving the dynamics of the proposed indicators of eco-efficiency, a two-step procedure based on combination of multiplicative structural decomposition analysis and a new variant of sensitivity analysis based on mathematical programing is outlined. In order to illustrate the potential applications of the new approach, an empirical analysis based on national input-output tables and environmental accounts for Poland is conducted. The empirical results provide solid evidence to claim that the sector Electricity, Gas and Water Supply plays a dominant role in shaping the structure of backward and forward linkages of all sectors of the Polish economy in the CO2-emmision-oriented generalized IO models. As a consequence, lowering the emission-output ratio in this particular sector has a particularly strong impact on improving production-source eco-efficiency of all sectors operating in Polish economy.

Water erosion modeling by the Erosion Potential Method and the Revised Universal Soil Loss Equation: a comparative analysis

Water erosion is the principal degradation process of tropical soils, and its effects can be measured by modeling techniques. Erosion models provide a diagnosis of the soil loss intensity and can support the planning of soil conservation practices. Models with low data requirements, such as the Revised Universal Soil Loss Equation (RUSLE) and, more recently, the Erosion Potential Method (EPM), are mainly applied in Brazil. Thus, the objective of this work was to estimate water erosion soil-loss rates using the EPM and RUSLE models on a tropical subbasin, followed by a comparison of their outcomes. The models’ application considered soil physical parameters, edaphoclimatic conditions of the area, land use, and subbasin management practices. The accuracy of the methods was verified using total transported sediment and water discharge data. We compared the models using Pearson's correlation analyses, considering a 5% of significance. We found a predominance of moderate-intensity erosion with average soil loss of 1.17 and 1.46 Mg ha-1 year-1, measured by EPM and RUSLE, respectively. The EPM model underestimated soil losses by 15.27%, and RUSLE overestimated by 19.08%, indicating a higher percentage of areas with high erosion rates (4.60%). The models presented results with a different order of magnitude, but with significant correlations, indicating that both methods pointed out similar zones of intense and light-erosion rates.

Soil moisture sensor network design for hydrological applications

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Micro-Networks for Robust MR-Guided Low Count PET Imaging.

Noise suppression is particularly important in low count positron emission tomography (PET) imaging. Post-smoothing (PS) and regularization methods which aim to reduce noise also tend to reduce resolution and introduce bias. Alternatively, anatomical information from another modality such as magnetic resonance (MR) imaging can be used to improve image quality. Convolutional neural networks (CNNs) are particularly well suited to such joint image processing, but usually require large amounts of training data and have mostly been applied outside the field of medical imaging or focus on classification and segmentation, leaving PET image quality improvement relatively understudied. This article proposes the use of a relatively low-complexity CNN (micro-net) as a post-reconstruction MR-guided image processing step to reduce noise and reconstruction artefacts while also improving resolution in low count PET scans. The CNN is designed to be fully 3-D, robust to very limited amounts of training data, and to accept multiple inputs (including competitive denoising methods). Application of the proposed CNN on simulated low (30 M) count data (trained to produce standard (300 M) count reconstructions) results in a 36% lower normalized root mean squared error (NRMSE, calculated over ten realizations against the ground truth) compared to maximum-likelihood expectation maximization (MLEM) used in clinical practice. In contrast, a decrease of only 25% in NRMSE is obtained when an optimized (using knowledge of the ground truth) PS is performed. A 26% NRMSE decrease is obtained with both RM and optimized PS. Similar improvement is also observed for low count real patient datasets. Overfitting to training data is demonstrated to occur as the network size is increased. In an extreme case, a U-net (which produces better predictions for training data) is shown to completely fail on test data due to overfitting to this case of very limited training data. Meanwhile, the resultant images from the proposed CNN (which has low training data requirements) have lower noise, reduced ringing, and partial volume effects, as well as sharper edges and improved resolution compared to conventional MLEM.

Consumer Profiling with Data Requirements: Structure and Policy Implications

We consider a model where a monopolist can profile consumers in order to price discriminate among them, and consumers can take costly actions to protect their identities and make the profiling technology less effective. A novel aspect of the model consists in the profiling technology: the signal that the monopolist gets about a consumer’s willingness‐to‐pay can be made more accurate either by having more consumers revealing their identities, or by spending larger amounts of money (e.g., on third‐party complementary data or data analytics capabilities). We show that both consumer surplus and social welfare are convex in the ability of consumers to conceal their identities. The interest of this result stems from the fact that consumers’ concealing cost can be interpreted as a policy tool: a stricter privacy law would make the concealing cost lower, and vice‐versa. Consequently, a policymaker who promotes total welfare should either make data protection very easy or very costly. The right direction of data regulations depends on data requirements. In particular, a higher (lower) data requirement is an instance when more (less) consumers are needed to achieve the same signal precision. We show that a strict data privacy law is preferable under a high data requirement so that firms are less likely to invest in profiling inefficiently, whereas there is less concern with little or no data regulations under a low data requirement. We also discuss when greater data protection may be beneficial to the firm.

Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase

AbstractIn various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach.

Data-Driven Model Predictive Control using Interpolated Koopman Generators

In recent years, the success of the Koopman operator in dynamical systems analysis has also fueled the development of Koopman operator-based control frameworks. In order to preserve the relatively low data requirements for an approximation via Dynamic Mode Decomposition, a quantization approach was recently proposed in [Peitz \& Klus, Automatica 106, 2019]. This way, control of nonlinear dynamical systems can be realized by means of switched systems techniques, using only a finite set of autonomous Koopman operator-based reduced models. These individual systems can be approximated very efficiently from data. The main idea is to transform a control system into a set of autonomous systems for which the optimal switching sequence has to be computed. In this article, we extend these results to continuous control inputs using relaxation. This way, we combine the advantages of the data efficiency of approximating a finite set of autonomous systems with continuous controls. We show that when using the Koopman generator, this relaxation --- realized by linear interpolation between two operators --- does not introduce any error for control affine systems. This allows us to control high-dimensional nonlinear systems using bilinear, low-dimensional surrogate models. The efficiency of the proposed approach is demonstrated using several examples with increasing complexity, from the Duffing oscillator to the chaotic fluidic pinball.

Better together: Comparing vulnerability prediction models

Abstract Context Vulnerability Prediction Models (VPMs) are an approach for prioritizing security inspection and testing to find and fix vulnerabilities. VPMs have been created based on a variety of metrics and approaches, yet widespread adoption of VPM usage in practice has not occurred. Knowing which VPMs have strong prediction and which VPMs have low data requirements and resources usage would be useful for practitioners to match VPMs to their project’s needs. The low density of vulnerabilities compared to defects is also an obstacle for practical VPMs. Objective The goal of the paper is to help security practitioners and researchers choose appropriate features for vulnerability prediction through a comparison of Vulnerability Prediction Models. Method We performed replications of VPMs on Mozilla Firefox with 28,750 source code files featuring 271 vulnerabilities using software metrics, text mining, and crash data. We then combined features from each VPM and reran our classifiers. Results We improved the F-score of the best VPM (.20 to 0.28) by combining features from three types of VPMs and using Naive Bayes as the classifier. The strongest features in the combined model were the number of times a file was involved in a crash, the number of outgoing calls from a file, and the string “nullptr”. Conclusion Our results indicate that further work is needed to develop new features for input into classifiers. In addition, new analytic approaches for VPMs are needed for VPMs to be useful in practical situations, due to the low density of vulnerabilities in software (less than 1% for our dataset).

Are Less Developed Countries More Exposed to Multinational Tax Avoidance? Method and Evidence from Micro-Data

AbstractThis paper uses a global dataset with information about 210,000 corporations in 142 countries to investigate whether tax avoidance by multinational firms is more prevalent in less-developed countries. The paper proposes a novel approach to studying cross-border profit shifting, which has relatively low data requirements and is therefore particularly well-suited for the context of developing countries. The results consistently show that the sensitivity of reported profits to profit-shifting incentives is negatively related to the level of economic and institutional development. This may explain why many developing countries opt for low corporate tax rates in spite of urgent revenue needs and severe constraints on the use of other tax bases.

LOW DATA REQUIREMENTS FRAMEWORK FOR LANDSLIDE SUSCEPTIBILITY PREDICTION USING 3D ALOS PALSAR IMAGES AND NEURAL NETWORKS

Abstract. Development of landslides susceptibility (LS) predictors based on 3D data is an active area of research in the recent years. Predicting landslides susceptibility maps help to secure human lives and maintaining infrastructures from this risk. Several advanced frameworks proposed with high input data to improve the predictors. The aim of this paper is to develop low data requirement framework for LS predictors development. This framework is only using one input 3D ALOS PALSAR image. The framework has three stages. (A) data pre-processing, (B) deriving explanatory factors, and (C) neural networks training and testing. Exactly. 22 input spatial factors were extracted from ALOS PALSAR image. Extracted factors were utilized to develop the FFNN predictor. The structure of the predictor is 22 factors (input layer) × 150 neurons (hidden layer) × 1 (output layer). Furthermore, 5829 sample points utilized during the training stage, while 745810 points sent to the trained predictor to create LS map. Based on confusion matrix metric, performance accuracy (89.3% training and 82.3 testing), While (95.22% training and 84.7% testing) based on Receiver Operating Characteristic curve. Out of the study area in Karabuk, 3.53 km2 (3.03%) were located in very high susceptibility category. Lastly, the application of the proposed framework showed that it is capable develop low data requirement predictors with high accuracy. Framework provide guideline data for future development in taxing topographic circumstances and large scale of data coverage. In addition, the framework handled the inconsistency in data quality and data updating problem.

A framework to develop a watershed pollution load model for semiarid and semihumid areas

Modeling pollutant processes in soil and water is helpful to better understand a pollutant’s potential adverse impacts on an environmental system. There are many watershed pollution load models; however, their effectivity is generally poor in semiarid and semihumid areas because of the complexity of hydrological processes and pollutant transport processes. In this paper, a pollution load module was combined with the well-established HIMS (Hydro-Informatic Modelling System) model for the purpose of simulating the processes of pollution generation and transport and quantifying the pollution load in semiarid and semihumid areas. The model has the advantages of a simple structure and low data requirement, and can simulate dissolved and solid-phase nitrogen and phosphorus. As a demonstration, the HIMS-pollution load (HIMS-PL) model was respectively tested for three semiarid and semihumid basins in China—the Liuhe and the Yixunhe River basins, two sub-basins of the Luanhe River basin, and the Chaohe River basin. Model simulations of streamflow and pollution loads at monthly time step accurately matched the observed streamflow (with NSEs 0.77–0.95 and REs 0.53–11.24%) and pollution load (with NSEs 0.54–0.83 and REs 2.45–12.72% for total nitrogen loads and with NSEs 0.51–0.79 and REs 2.22–19.70% for total phosphorus loads). In comparison with HSPF and GWLF models, the HIMS-PL model performed better than the GWLF model in all of the above-mentioned basins and better than the HSPF model in some of the basins. Overall, the proposed model is generally suitable for simulating streamflow and pollution load in semiarid and semihumid regions.

Household-specific targeting of agricultural advice via mobile phones: Feasibility of a minimum data approach for smallholder context

In recent years, agricultural extension services in developing countries have increasingly introduced modern information and communication technologies (ICT) to deliver advice. But to realize efficiency gains, digital applications may need to address heterogeneous information needs by targeting agricultural advisory contents in a household-specific way. We explore the feasibility of an automated advisory service that collects household data from farmers, for example through the keypads of conventional mobile phones, and uses this data to prioritize agricultural advisory messages accordingly. To reduce attrition, such a system must avoid lengthy inquiry. Therefore, our objective was to identify a viable trade-off between low data requirements and useful household-specific prioritizations of advisory messages. At three sites in Ethiopia, Kenya, and Tanzania independently, we collected experimental preference rankings from smallholder farmers for receiving information about different agricultural and livelihood practices. At each site, we identified socio-economic household variables that improved model-based predictions of individual farmers’ information preferences. We used the models to predict household-specific rankings of information options based on 2–4 variables, requiring the farmer to answer between 5 and 10 questions through an ICT interface. These predicted rankings could inform household-specific prioritizations of advisory messages in a digital agro-advisory application. Household-specific “top 3” options suggested by the models were better-fit to farmers’ preferences than a random selection of 3 options by 48–68%, on average. The analysis shows that relatively limited data inputs from farmers, in a simple format, can be used to increase the client-orientation of ICT-mediated agricultural extension. This suggests that household-specific prioritization of agricultural advisory messages through digital two-way communication is feasible. In future digital agricultural advisory applications, collecting little data from farmers at each interaction may feed into learning algorithms that continuously improve the targeting of advice.

Assessing the exploratory potential in Brazil by applying a creaming curve variant

This study aims at estimating potential future oil discoveries in Brazil by looking at the creaming phenomenon in past exploration trends. We apply a top-down modeling approach as fields' reserves are not disclosed. This study focuses on the four sedimentary basins which concentrate 95% of the Original Oil-in-Place (OOIP) discovered: Campos, Santos, Recôncavo and Potiguar. This work considers the 1P and 3P reserves to create a proxy for the volume of discoveries. Findings indicate potential to increase reserves by improving the recovery factor as well as by yet-to-find discoveries. We evidence such discoveries can be very sensitive to the function chosen to fit the creaming curve but also to the uncertainty in reserves estimates. Given the limited literature in Brazil concerning its exploratory potential, the creaming curve approach plays a vital role due to its relative simplicity and low data requirement. The methodology proposed in this study can be especially useful for many non-OECD countries where the availability of field-level data is limited.

A comparison of modelled ice thickness and volume across the entire Antarctic Peninsula region

ABSTRACTUnderstanding Antarctic Peninsula glacier evolution requires distributed ice thickness and subglacial topography. To date, 80% of the Antarctic Peninsula mainland ice volume has only been determined at low-resolution (1 km post spacing) and the distributed ice thickness of glaciers on surrounding islands has never been quantified. In this study we applied a perfect plasticity model, selected for its simplicity, low data requirements and minimal parameterisation, to estimate glacier thickness, subglacial topography and ice volume for the entire Antarctic Peninsula region. We compared the output of this simple model to that of a more sophisticated but spatially-restricted model and also to the spatially-coarse but more extensive Bedmap2 dataset. The simple model produced mean differences of 1.4 m (std. dev. 243 m) in comparison with the more sophisticated approach for the mountainous parts of the Peninsula. It produced similar volumes for tidewater glaciers but gave unrealistic ice thickness around grounding lines. Ice thickness across low gradient plateau surfaces are mis-represented by a perfect plasticity model and thus for the southern part of the Peninsula only regional ice volume can be approximated by our model. Overall, with consideration of ice situated below sea level, model results suggest that Trinity Peninsula, Graham Land, the part of Palmer Land north of 74°S and all glaciers on islands contain an ice mass of ∼200 300 Gt, with sea level equivalent of 553 mm (± 11.6 mm). Of this total 8% is from glaciers on islands, 70% of which is from Alexander Island.

The 3DNet-Catch hydrologic model: Development and evaluation

Hydrologic models are important for effective water resources management. They vary in complexity from parsimonious, spatially lumped, to physically-based, fully distributed models, which are generally expected to outperform the former. Wide applications of complex models are limited due to high data and computational demands. Therefore, a new approach based on well-balanced model complexity is needed to obtain reasonable simulation results with low data requirements. This paper presents a novel 3DNet-Catch hydrologic model, developed to represent key processes in sloped catchments under a temperate climate with modest data requirements. 3DNet-Catch includes runoff simulations within computational units by employing the interception, snow and soil routines, as well as runoff and channel routing. The soil routine, which is the key model feature, combines the SCS-CN method, an analytically integrated nonlinear outflow equation and the Brooks-Corey relation for unsaturated conductivity in an innovative manner. To advance runoff routing in 3DNet-Catch, an approach for analytical integration of the linear and nonlinear outflow equations is implemented. Most model parameters are physically meaningful, thus facilitating model calibration. The model structure can be adjusted according to soil and groundwater flow data, and it can include hydraulic structures, thereby providing adaptability to local conditions. A comprehensive hydrologic evaluation framework is established and conducted to examine whether 3DNet-Catch is adequately parameterised and can accurately reproduce catchment hydrologic response. The model parameterisation is evaluated by sensitivity, identifiability and correlation analyses. Model efficiency is quantified in terms of performance measures, hydrologic signatures and plausibility of the simulated hydrological processes. The results show high sensitivity of the hydrologic variables and performance measures to the model parameters, particularly to those of the soil routine. The parameters are uncorrelated and generally well identifiable. The model performs equally well in the calibration and evaluation periods. High efficiency in the hydrological signatures related to the soil routine indicates its robustness. The results, therefore, suggest that 3DNet-Catch is a comprehensively parameterised, versatile hydrologic model. It realistically reproduces observed hydrographs with modest data requirements, thus being appropriate for both engineering applications and investigative catchment dynamics studies.

Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data

Abstract Crop yield estimation at regional scale using crop model is generally subjected to large uncertainties from insufficient spatial information on heterogeneous growth environment and agronomic management practices. To solve this problem, we assimilated crop phenology and leaf area index (LAI) derived from remote sensing into a crop model (MCWLA-Wheat) to improve its reliability in estimating winter wheat yields at regional scale. Since the LAI magnitude was obviously underestimated however its spatial pattern was relatively well captured by remote sensing, we developed a novel spatial assimilation scheme that assimilated the spatial differences instead of the absolute values of LAI into crop model. Firstly, we retrieved the information of critical development stages of winter wheat from remote sensing data to adjust the simulation of phenology by MCWLA-Wheat model; then the spatial differences of LAI derived from remote sensing were assimilated into the MCWLA-Wheat model using a kind of constant gain Kalman Filter algorithm to improve the ability of the model in estimating winter wheat LAI and yields at regional scale in the North China Plain. This assimilation scheme extracted effective information from remote sensing LAI and meanwhile abandoned the information with obvious errors, ensuring that the assimilation variables could be close to the reality. It avoids the requirement for correction of the LAI derived from remote sensing using other high-quality ancillary data from field measurements. Using this assimilation scheme, the performance of crop model improved substantially. It successfully produced more accurate yield estimates at regional scale during the period of 2001–2008 (mean R 2 = 0.42, RMSE = 737/ha) than those without assimilation (mean R 2 = 0.26, RMSE = 1012 kg/ha) and those directly assimilating the absolute LAI values derived from remote sensing (mean R 2 = 0.30, RMSE = 1257/ha). Our findings demonstrated a reliable and promising assimilation scheme for improving yield estimation of crop model at regional scale with low data requirement.

Performance Evaluation of AquaCrop and DSSAT-CERES for Maize Under Different Irrigation and Manure Application Rates in the Himalayan Region of India

The agricultural modeling sector faces a lot of challenges when it comes to the evaluation of “what if” scenarios due to the immense data requirement of common models specially the crop coefficients which are not usually available for all varieties. The present study assesses the performance of two commonly used crop models: AquaCrop (less data requiring model) and DSSAT-CERES (data-intensive model) for Maize (Zea mays) at higher Himalayan region of India. Model calibration and validation were done based on the combination of different irrigation and farmyard manure application rates. Furthermore, model comparison was done post-validation with field experimental data by comparing grain yield, total biomass yield, harvest index, total soil water content and leaf area index (LAI). Both AquaCrop and DSSAT-CERES underestimated yield by 1.0 and 0.2%, respectively. The corresponding models underestimated total aboveground biomass by 0.15 and 1.7%; however, DSSAT-CERES overestimated LAI by 0.5%, whereas AquaCrop underestimated by 0.1%. Statistical tests for all the output variables indicate insignificant variation among the simulated and observed values. Results suggest that both AquaCrop and DSSAT-CERES are suitable for higher altitudes in the Himalayan region of India. However, AquaCrop being parsimonious with relatively incomparable results to DSSAT-CERES and low data requirement is recommended.