Tracing the molecular basis of transcriptional dynamics in noisy data by using an experiment based mathematical model
Rybakova, K.N. ; Tomaszewska, A. ; Mourik, S. van; Blom, Joke ; Westerhoff, H.V. ; Carlberg, C. ; Bruggeman, F.J. - \ 2015
Nucleic acids research 43 (2015)1. - ISSN 0305-1048 - p. 153 - 161.
differentiation-related protein - gene-expression - parameter-estimation - rna degradation - adrp expression - receptor - cells - elongation - promoter - networks
Changes in transcription factor levels, epigenetic status, splicing kinetics and mRNA degradation can each contribute to changes in the mRNA dynamics of a gene. We present a novel method to identify which of these processes is changed in cells in response to external signals or as a result of a diseased state. The method employs a mathematical model, for which the kinetics of gene regulation, splicing, elongation and mRNA degradation were estimated from experimental data of transcriptional dynamics. The time-dependent dynamics of several species of adipose differentiation-related protein (ADRP) mRNA were measured in response to ligand activation of the transcription factor peroxisome proliferator-activated receptor d (PPARd). We validated the method by monitoring the mRNA dynamics upon gene activation in the presence of a splicing inhibitor. Our mathematical model correctly identifies splicing as the inhibitor target, despite the noise in the data.
Distributed Evaluation of Local Sensitivity Analysis (DELSA), with application to hydrological models
Rakovec, O. ; Hill, M.C. ; Clark, M.P. ; Weerts, A.H. ; Teuling, A.J. ; Uijlenhoet, R. - \ 2014
Water Resources Research 50 (2014)1. - ISSN 0043-1397 - p. 409 - 426.
measuring uncertainty importance - coupled reaction systems - groundwater-flow system - net ecosystem exchange - parameter-estimation - information-content - rate coefficients - climate-change - land model - indexes
1] This paper presents a hybrid local-global sensitivity analysis method termed the Distributed Evaluation of Local Sensitivity Analysis (DELSA), which is used here to identify important and unimportant parameters and evaluate how model parameter importance changes as parameter values change. DELSA uses derivative-based “local” methods to obtain the distribution of parameter sensitivity across the parameter space, which promotes consideration of sensitivity analysis results in the context of simulated dynamics. This work presents DELSA, discusses how it relates to existing methods, and uses two hydrologic test cases to compare its performance with the popular global, variance-based Sobol' method. The first test case is a simple nonlinear reservoir model with two parameters. The second test case involves five alternative “bucket-style” hydrologic models with up to 14 parameters applied to a medium-sized catchment (200 km2) in the Belgian Ardennes. Results show that in both examples, Sobol' and DELSA identify similar important and unimportant parameters, with DELSA enabling more detailed insight at much lower computational cost. For example, in the real-world problem the time delay in runoff is the most important parameter in all models, but DELSA shows that for about 20% of parameter sets it is not important at all and alternative mechanisms and parameters dominate. Moreover, the time delay was identified as important in regions producing poor model fits, whereas other parameters were identified as more important in regions of the parameter space producing better model fits. The ability to understand how parameter importance varies through parameter space is critical to inform decisions about, for example, additional data collection and model development. The ability to perform such analyses with modest computational requirements provides exciting opportunities to evaluate complicated models as well as many alternative models.
Identifying Optimal Models to Represent Biochemical Systems
Apri, M. ; Gee, M. de; Mourik, S. van; Molenaar, J. - \ 2014
PLoS ONE 9 (2014)1. - ISSN 1932-6203 - 12 p.
parameter-estimation - optimization - reduction - discrimination - mechanisms - networks - pathways - receptor - designs
Biochemical systems involving a high number of components with intricate interactions often lead to complex models containing a large number of parameters. Although a large model could describe in detail the mechanisms that underlie the system, its very large size may hinder us in understanding the key elements of the system. Also in terms of parameter identification, large models are often problematic. Therefore, a reduced model may be preferred to represent the system. Yet, in order to efficaciously replace the large model, the reduced model should have the same ability as the large model to produce reliable predictions for a broad set of testable experimental conditions. We present a novel method to extract an “optimal” reduced model from a large model to represent biochemical systems by combining a reduction method and a model discrimination method. The former assures that the reduced model contains only those components that are important to produce the dynamics observed in given experiments, whereas the latter ensures that the reduced model gives a good prediction for any feasible experimental conditions that are relevant to answer questions at hand. These two techniques are applied iteratively. The method reveals the biological core of a model mathematically, indicating the processes that are likely to be responsible for certain behavior. We demonstrate the algorithm on two realistic model examples. We show that in both cases the core is substantially smaller than the full model.
Merino ewes can be bred for body weight change to be more tolerant to uncertain feed supply
Rose, I.J. ; Kause, A. ; Mulder, H.A. ; Werf, J.H.J. van der; Thompson, A.N. ; Ferguson, M.B. ; Arendonk, J.A.M. van - \ 2013
Journal of Animal Science 91 (2013)6. - ISSN 0021-8812 - p. 2555 - 2565.
random regression-models - parameter-estimation - heat-stress - environment - covariance - liveweight - survival - climate - cattle
Sheep in Australia experience periods with different feed supply causing them to gain and lose BW during the year. It is more efficient if ewes lose less BW during periods of poor nutrition and gain more BW during periods of good nutrition. We investigated whether BW loss during periods of poor nutrition and BW gain during periods of good nutrition are genetically different traits. We used BW measurements from 2,336 adult Merino ewes managed over 5 yr in a Mediterranean climate in Katanning, Australia. Body weight loss is the difference between 2 BW measured 42 d apart during mating, a period of poor nutrition. Body weight gain is the difference between 2 BW measured 131 d apart during a period of good nutrition between prelambing and weaning. We estimated variance compnents of BW change using 3 methods: 1) as a trait calculated by subtracting the first BW from the second, 2) multivariate analysis of BW traits, and 3) random regression analysis of BW. The h(2) and genetic correlations (rg) estimated using the multivariate analysis of BW and the BW change trait were very similar whereas the random regression analysis estimated lower heritabilities and more extreme negative genetic correlations between BW loss and gain. The multivariate model fitted the data better than random regression based on Akaike and Bayesian information criterion so we considered the results of the multivariate model to be more reliable. The heritability of BW loss (h(2) = 0.05-0.16) was smaller than that of BW gain (h(2) = 0.14-0.37). Body weight loss and gain can be bred for independently at 2 and 4 yr of age (rg = 0.03 and -0.04) whereas at 3 yr of age ewes that genetically lost more BW gained more BW (rg = -0.41). Body weight loss is genetically not the same trait at different ages (rg range 0.13-0.39). Body weight gain at age 3 yr is genetically the same trait at age 4 yr (rg = 0.99) but is different between age 2 yr and the older ages (rg = 0.53 and 0.51). These results suggest that as the ewes reach their mature BW, BW gain at different ages becomes the same trait. This does not apply to BW loss. We conclude that BW change could be included in breeding programs to breed adult Merino ewes that are more tolerant to variation in feed supply.
Managing uncertainty in integrated environmental modelling: The UncertWeb framework.
Bastin, L. ; Cornford, D. ; Jones, R. ; Heuvelink, G.B.M. ; Pebesma, E. ; Stasch, C. ; Nativi, S. ; Mazzetti, P. - \ 2013
Environmental Modelling & Software 39 (2013). - ISSN 1364-8152 - p. 116 - 134.
sensitivity-analysis - parameter-estimation - bayesian-approach - watershed model - climate-change - error - calibration - systems - design - tools
Web-based distributed modelling architectures are gaining increasing recognition as potentially useful tools to build holistic environmental models, combining individual components in complex workflows. However, existing web-based modelling frameworks currently offer no support for managing uncertainty. On the other hand, the rich array of modelling frameworks and simulation tools which support uncertainty propagation in complex and chained models typically lack the benefits of web based solutions such as ready publication, discoverability and easy access. In this article we describe the developments within the UncertWeb project which are designed to provide uncertainty support in the context of the proposed ‘Model Web’. We give an overview of uncertainty in modelling, review uncertainty management in existing modelling frameworks and consider the semantic and interoperability issues raised by integrated modelling. We describe the scope and architecture required to support uncertainty management as developed in UncertWeb. This includes tools which support elicitation, aggregation/disaggregation, visualisation and uncertainty/sensitivity analysis. We conclude by highlighting areas that require further research and development in UncertWeb, such as model calibration and inference within complex environmental models.
A physiologically based kinetic model for bacterial sulfide oxidation
Klok, J.B.M. ; Graaff, C.M. de; Bosch, P.L.F. van den; Boelee, N.C. ; Keesman, K.J. ; Janssen, A.J.H. - \ 2013
Water Research 47 (2013)2. - ISSN 0043-1354 - p. 483 - 492.
afvalwaterbehandeling - biotechnologie - zwavelwaterstof - oxidatie - ontzwaveling - alkalibacillus haloalkaliphilus - microbiële fysiologie - afvalwaterbehandelingsinstallaties - waste water treatment - biotechnology - hydrogen sulfide - oxidation - desulfurization - alkalibacillus haloalkaliphilus - microbial physiology - waste water treatment plants - sulfur-oxidizing bacteria - biologically produced sulfur - dissolved sodium sulfide - parameter-estimation - hydrogen-sulfide - soda lakes - bioreactors - thiosulfate - mechanisms - pathways
In the biotechnological process for hydrogen sulfide removal from gas streams, a variety of oxidation products can be formed. Under natron-alkaline conditions, sulfide is oxidized by haloalkaliphilic sulfide oxidizing bacteria via flavocytochrome c oxidoreductase. From previous studies, it was concluded that the oxidation-reduction state of cytochrome c is a direct measure for the bacterial end-product formation. Given this physiological feature, incorporation of the oxidation state of cytochrome c in a mathematical model for the bacterial oxidation kinetics will yield a physiologically based model structure. This paper presents a physiologically based model, describing the dynamic formation of the various end-products in the biodesulfurization process. It consists of three elements: 1) Michaelis–Menten kinetics combined with 2) a cytochrome c driven mechanism describing 3) the rate determining enzymes of the respiratory system of haloalkaliphilic sulfide oxidizing bacteria. The proposed model is successfully validated against independent data obtained from biological respiration tests and bench scale gas-lift reactor experiments. The results demonstrate that the model is a powerful tool to describe product formation for haloalkaliphilic biomass under dynamic conditions. The model predicts a maximum S0 formation of about 98 mol%. A future challenge is the optimization of this bioprocess by improving the dissolved oxygen control strategy and reactor design.
Monitoring marine populations and communities: methods dealing with imperfect detectability
Katsanevakis, S. ; Weber, A. ; Pipitone, C. ; Leopold, M.F. ; Scheidat, M. ; Boois, I.J. de; Jansen, J.M. - \ 2012
Aquatic Biology 16 (2012)1. - ISSN 1864-7782 - p. 31 - 52.
estimating animal abundance - visual census techniques - capture-recapture data - line-transect surveys - change-in-ratio - reef fish - aerial surveys - harbor porpoise - phoca-vitulina - parameter-estimation
Effective monitoring of populations and communities is a prerequisite for ecosystem-based management of marine areas. However, monitoring programs often neglect important sources of error and thus can lead to biased estimates, spurious conclusions and false management actions. One such source of error is ‘imperfect detectability’, i.e. the inability of investigators to detect all individuals or all species in a surveyed area. Although there has been great effort to develop monitoring methods that account for imperfect detectability, the application of such methods in the marine environment is not as apparent as in other systems. Plot sampling is by far the most commonly applied method for biological monitoring in the marine environment, yet it largely ignores detectability issues. However, distance sampling, mark-recapture methods, repeated presence-absence surveys for occupancy estimation, and removal methods do estimate detection probabilities and provide unbiased estimates of state variables. We review these methods and the relevant tools for their application in studies on marine populations and communities, with the aim of assisting marine biologists and managers to understand the limitations and pitfalls associated with some approaches and to select the best available methods for their monitoring needs
Calibration of a distributed irrigation water management model using remotely sensed evapotranspiration rates and groundwater heads
Jhorar, R.K. ; Smit, A.A.M.F.R. ; Bastiaanssen, W.G.M. ; Roest, C.W.J. - \ 2011
Irrigation and Drainage 60 (2011)1. - ISSN 1531-0353 - p. 57 - 69.
parameter-estimation - hydrologic-models - flow models - identification - algorithm - space
Parameters of the distributed irrigation water management model FRAME are determined by an inverse method using evapotranspiration (ET) rates estimated from the SEBAL remote sensing procedure and in situ measurement of groundwater heads. The model simulates canal and on-farm water management as well as regional groundwater flow. The calibration is achieved in two phases. The data on ET were introduced with the primary intent of improving predictions of ET through better estimated soil hydraulic parameters. During the first phase, soil hydraulic parameters sensitive to ET were optimized. As per the canal running schedule in the study area, the daily values of ET data were synthesized into 16 time periods with 15 periods each of 24 days and one period of 5 days. Use of cumulative (annual basis) ET data results in better estimates of soil hydraulic parameters as compared to temporal (24-day period basis) ET data due to possible errors in other input data. During the second phase of calibration, aquifer drainable porosity and maximum allowable groundwater extraction were optimized against groundwater heads for five years. The calibration was very successful in about 70% of the study area with a coefficient of correlation between simulated and observed groundwater levels of more than 80%. Subsequently the model is validated against groundwater heads for nine years.
FIELD-A summary simulation model of the soil–crop system to analyse long-term resource interactions and use efficiencies at farm scale
Tittonell, P.A. ; Corbeels, M. ; Wijk, M.T. van; Giller, K.E. - \ 2010
European Journal of Agronomy 32 (2010)1. - ISSN 1161-0301 - p. 10 - 21.
radiation-use efficiency - fertility management - parameter-estimation - organic-matter - growth-model - maize - tropics - kenya - complexity - prediction
Resources for crop production are often scarce in smallholder farming systems in the tropics, particularly in sub-Saharan Africa (SSA). Decisions on the allocation of such resources are often made at farm rather than at field plot scale. To handle the uncertainty caused by both lack of data and imperfect knowledge inherent to these agricultural systems, we developed a dynamic summary model of the soil–crop system that captures essential interactions determining the short- and long-term crop productivity, while keeping a degree of simplicity that allows its parameterisation, use and dissemination in the tropics. Generic, summary functions describing crop productivity may suffice for addressing questions concerning trade-offs on resource allocation at farm scale. Such functions can be derived from empirical (historical) data or, when they involve potential or water-limited crop yields, can be generated using process-based, detailed crop simulation models. This paper describes the approach to simulating crop productivity implemented in the model FIELD (Field-scale Interactions, use Efficiencies and Long-Term soil fertility Development), based on the availability of light, water, nitrogen, phosphorus and potassium, and the interactions between these factors. We describe how these interactions are simulated and use examples from case studies in African farming systems to illustrate the use of detailed crop models to generate summary functions and the ability of FIELD to capture long-term trends in soil C and crop yields, crop responses to applied nutrients across heterogeneous smallholder farms and the implications of overlooking the effects of intra-seasonal rainfall variability in the model. An example is presented that evaluates the sensitivity of the model to resource allocation decisions when operating (linked to livestock and household models) at farm scale. Further, we discuss the assessment of model performance, going beyond the calculation of simple statistics to compare simulated and observed results to include broader criteria such as model applicability. In data-scarce environments such as SSA, uncertainty in parameter values constrains the performance of detailed process-based models, often forcing model users to ‘guess’ (or set to default values) parameters that are seldom measured in practice. The choice of model depends on its suitability and appropriateness to analyse the relevant scale for the question addressed. Simpler yet dynamic models of the various subsystems (crop, soil, livestock, manure) may prove more robust than detailed, process-based models when analysing farm scale questions on system design and resource allocation in SSA.
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data
Fox, A. ; Williams, M. ; Richardson, A.D. ; Cameron, D. ; Gove, J.H. ; Quaife, T. ; Ricciuto, D. ; Reichstein, M. ; Tomelleri, E. ; Trudinger, C.M. ; Wijk, M.T. van - \ 2009
Agricultural and Forest Meteorology 149 (2009)10. - ISSN 0168-1923 - p. 1597 - 1615.
parameter-estimation - data assimilation - carbon-dioxide - uncertainty - climate - forest - productivity - variability - simulation - feedbacks
We describe a model-data fusion (MDF) inter-comparison project (REFLEX), which compared various algorithms for estimating carbon (C) model parameters consistent with both measured carbon fluxes and states and a simple C model. Participants were provided with the model and with both synthetic net ecosystem exchange (NEE) of CO2 and leaf area index (LAI) data, generated from the model with added noise, and observed NEE and LAI data from two eddy covariance sites. Participants endeavoured to estimate model parameters and states consistent with the model for all cases over the two years for which data were provided, and generate predictions for one additional year without observations. Nine participants contributed results using Metropolis algorithms, Kalman filters and a genetic algorithm. For the synthetic data case, parameter estimates compared well with the true values. The results of the analyses indicated that parameters linked directly to gross primary production (GPP) and ecosystem respiration, such as those related to foliage allocation and turnover, or temperature sensitivity of heterotrophic respiration, were best constrained and characterised. Poorly estimated parameters were those related to the allocation to and turnover of fine root/wood pools. Estimates of confidence intervals varied among algorithms, but several algorithms successfully located the true values of annual fluxes from synthetic experiments within relatively narrow 90% confidence intervals, achieving >80% success rate and mean NEE confidence intervals
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?
Vrugt, J.A. ; Braak, C.J.F. ter; Gupta, H.V. ; Robinson, B.A. - \ 2009
Stochastic environmental research and risk assessment 23 (2009)7. - ISSN 1436-3240 - p. 1011 - 1026.
rainfall-runoff models - uncertainty assessment - metropolis algorithm - parameter-estimation - sensitivity - calibration - optimization - zone - methodology - predictions
In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments
Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation
Vrugt, J.A. ; Braak, C.J.F. ter; Clark, M.P. ; Hyman, J.M. ; Robinson, B.A. - \ 2008
Water Resources Research 44 (2008). - ISSN 0043-1397 - p. W00B09 - W00B09.
ensemble kalman filter - rainfall-runoff models - streamflow simulation - metropolis algorithm - parameter-estimation - bayesian-estimation - data assimilation - optimization - prediction - calibration
There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices.
Observer design and tuning for biomass growth and kLa using online and offline measurements
Soons, Z.I.T.A. ; Shi, J. ; Stigter, J.D. ; Pol, L.A. van der; Straten, G. van; Boxtel, A.J.B. van - \ 2008
Journal of Process Control 18 (2008)7-8. - ISSN 0959-1524 - p. 621 - 631.
extended kalman filter - parameter-estimation - bordetella-pertussis - batch fermentation - state estimation - off-line - bioreactor - estimator - systems - reactor
Measurement of the key process variables is essential during biopharmaceutical production. These measurements are often not available online. This work combines frequent online measurements (oxygen uptake rate) with infrequent offline measurements (biomass) to estimate the specific growth rate, biomass, and the oxygen transfer coefficient (kLa) online. The system consists of an extended Kalman filter and parameter adaptation for the time-varying kLa. Tuning is based on minimization of the error between the simulation and the estimation. Although the process itself is not stable, stability of the observer is evaluated heuristically by application of the Routh criterion. Performance and convergence of the observer are shown in both simulations and experiments in continuous and fed-batch cultivations of Bordetella pertussis.
Measurement network design including traveltime determinations to minimize model prediction uncertainty
Janssen, G.M.C.M. ; Valstar, J.R. ; Zee, S.E.A.T.M. van der - \ 2008
Water Resources Research 44 (2008). - ISSN 0043-1397 - 17 p.
groundwater solute transport - waste management sites - monitoring network - shallow groundwater - sampling design - cape-cod - environmental tracers - parameter-estimation - engineering design - regulatory policy
Traveltime determinations have found increasing application in the characterization of groundwater systems. No algorithms are available, however, to optimally design sampling strategies including this information type. We propose a first-order methodology to include groundwater age or tracer arrival time determinations in measurement network design and apply the methodology in an illustrative example in which the network design is directed at contaminant breakthrough uncertainty minimization. We calculate linearized covariances between potential measurements and the goal variables of which we want to reduce the uncertainty: the groundwater age at the control plane and the breakthrough locations of the contaminant. We assume the traveltime to be lognormally distributed and therefore logtransform the age determinations in compliance with the adopted Bayesian framework. Accordingly, we derive expressions for the linearized covariances between the transformed age determinations and the parameters and states. In our synthetic numerical example, the derived expressions are shown to provide good first-order predictions of the variance of the natural logarithm of groundwater age if the variance of the natural logarithm of the conductivity is less than 3.0. The calculated covariances can be used to predict the posterior breakthrough variance belonging to a candidate network before samples are taken. A Genetic Algorithm is used to efficiently search, among all candidate networks, for a near-optimal one. We show that, in our numerical example, an age estimation network outperforms (in terms of breakthrough uncertainty reduction) equally sized head measurement networks and conductivity measurement networks even if the age estimations are highly uncertain.
Describing the soil physical characteristics of soil samples with cubical splines
Wesseling, J.G. ; Ritsema, C.J. ; Stolte, J. ; Oostindie, K. ; Dekker, K. - \ 2008
Transport in Porous Media 71 (2008)3. - ISSN 0169-3913 - p. 289 - 309.
unsaturated hydraulic conductivity - pedotransfer functions - water-retention - parameter-estimation - outflow experiments - field measurement - inverse problem - flow - infiltration - transport
The Mualem-Van Genuchten equations have become very popular in recent decades. Problems were encountered fitting the equations¿ parameters through sets of data measured in the laboratory: parameters were found which yielded results that were not monotonic increasing or decreasing. Due to the interaction between the soil moisture retention and the hydraulic conductivity relationship, some data sets yield a fit that seems not to be optimal. So the search for alternatives started. We ended with the cubical spline approximation of the soil physical characteristics. Software was developed to fit the spline-based curves to sets of measured data. Five different objective functions are tested and their results are compared for four different data sets. It is shown that the well-known least-square approximation does not always perform best. The distance between the measured points and the fitted curve, as can be evaluated numerically in a simple way, appears to yield good fits when applied as a criterion in the optimization procedure. Despite an increase in computational effort, this method is recommended over the least square method.
Water productivity analysis of irrigated crops in Sirsa district, India
Singh, R. ; Dam, J.C. van; Feddes, R.A. - \ 2006
Agricultural Water Management 82 (2006)3. - ISSN 0378-3774 - p. 253 - 278.
soil hydraulic-properties - parameter-estimation - flow - performance - evaporation - model - rice - conductivity - management - simulation
Water productivity (WP) expresses the value or benefit derived from the use of water, and includes essential aspects of water management such as production for arid and semi-arid regions. A profound WP analysis was carried out at five selected farmer fields (two for wheat¿rice and three for wheat¿cotton) in Sirsa district, India during the agricultural year 2001¿02. The ecohydrological soil¿water¿atmosphere¿plant (SWAP) model, including detailed crop simulations in combination with field observations, was used to determine the required hydrological variables such as transpiration, evapotranspiration and percolation, and biophysical variables such as dry matter or grain yields. The use of observed soil moisture and salinity profiles was found successful to determine indirectly the soil hydraulic parameters through inverse modelling. Considerable spatial variation in WP values was observed not only for different crops but also for the same crop. For instance, the WPET, expressed in terms of crop grain (or seed) yield per unit amount of evapotranspiration, varied from 1.22 to 1.56 kg m¿3 for wheat among different farmer fields. The corresponding value for cotton varied from 0.09 to 0.31 kg m¿3. This indicates a considerable variation and scope for improvements in water productivity. The average WPET (kg m¿3) was 1.39 for wheat, 0.94 for rice and 0.23 for cotton, and corresponds to average values for the climatic and growing conditions in Northwest India. Including percolation in the analysis, i.e. crop grain (or seed) yield per unit amount of evapotranspiration plus percolation, resulted in average WPETQ (kg m¿3) values of 1.04 for wheat, 0.84 for rice and 0.21 for cotton. Factors responsible for low WP include the relative high amount of evaporation into evapotranspiration especially for rice, and percolation from field irrigations. Improving agronomic practices such as aerobic rice cultivation and soil mulching will reduce this non-beneficial loss of water through evaporation, and subsequently will improve the WPET at field scale. For wheat, the simulated water and salt limited yields were 20¿60% higher than measured yields, and suggest substantial nutrition, pest, disease and/or weed stresses. Improved crop management in terms of timely sowing, optimum nutrient supply, and better pest, disease and weed control for wheat will multiply its WPET by a factor of 1.5! Moreover, severe water stress was observed on cotton (relative transpiration <0.65) during the kharif (summer) season, which resulted in 1.4¿3.3 times lower water and salt limited yields compared with simulated potential yields. Benefits in terms of increased cotton yields and improved water productivity will be gained by ensuring irrigation supply at cotton fields, especially during the dry years.