Journal Paper Digests 2019 #2
- Land-use and species tipping points in a coupled ecological-economic model
- Biodiversity and robustness of large ecosystems
- Patterns of landscape seasonality influence passerine diversity: Implications for conservation management under global change
- Dynamic trade-off analysis of multiple ecosystem services under land use change scenarios: Towards putting ecosystem services into planning in Iran
- Assessing sampling sufficiency of network metrics using bootstrap
- Comparing vis-NIRS, LIBS, and Combined vis-NIRS-LIBS for Intact Soil Core Soil Carbon Measurement
- Change of support using non-additive variables with Gibbs Sampler: Application to metallurgical recovery of sulphide ores
- Joint simulation of compositional and categorical data via direct sampling technique - Application to improve mineral resource confidence
- A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
- When Should Irrigators Invest in More Water-Efficient Technologies as an Adaptation to Climate Change?
- Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network
- The Fast and the Robust: Trade-Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models
- Disaggregating Soil Moisture to Finer Spatial Resolutions: A Comparison of Alternatives
- New perspectives to use Munsell color charts with electronic devices
- Methodology for payment for ecosystem services based on the concept of land use and management capability
- Digital soil assessment for quantifying soil constraints to crop production: a case study for rice in Punjab, India
Land-use and species tipping points in a coupled ecological-economic model
Authors: Drechsler, M; Surun, C
Source: ECOLOGICAL COMPLEXITY, 36 86-91; DEC 2018
Abstract: Complex systems can have tipping points where the system behavior changes abruptly from one regime to another. We develop an ecological-economic model that simulates the spatio-temporal dynamics of the land-use induced by a tradable permit market and its consequences on the viability of a model species. The model analysis reveals that the land-use dynamics are subject to a tipping point with regard to changes in policy scheme design. One the level of species viability, this tipping point is amplified and a second tipping point emerges. The two tipping points interact and their location and sharpness depend on the characteristics of the species. We conclude that in the consideration of coupled ecological-economic systems tipping points can play an important role. The existence of tipping points considerably complicates the design of policy instruments for the sustainable management of ecological-economic systems because a small change in the policy design can have dramatic con sequences on the system dynamics.
Biodiversity and robustness of large ecosystems
Authors: Kozlov, V; Vakulenko, S; Wennergren, U; Tkachev, V
Source: ECOLOGICAL COMPLEXITY, 36 101-109; DEC 2018
Abstract: We study the biodiversity problem for resource competition systems with extinctions and self-limitation effects. Our main result establishes estimates of biodiversity in terms of the fundamental parameters of the model. We also prove the global stability of solutions for systems with extinctions and large turnover rate. We show that when the extinction threshold is distinct from zero, the large time dynamics of system is fundamentally nonpredictable. In the last part of the paper we obtain explicit analytical estimates of ecosystem robustness with respect to variations of resource supply which support the R* rule for a system with random parameters.
Patterns of landscape seasonality influence passerine diversity: Implications for conservation management under global change
Authors: Civantos, E; Monteiro, AT; Goncalves, J; Marcos, B; Alves, P; Honrado, JP
Source: ECOLOGICAL COMPLEXITY, 36 117-125; DEC 2018
Abstract: The importance of environmental heterogeneity for biodiversity across scales is widely recognized in ecological theory and profusely supported by evidence. However, our understanding of the effects of spatiotemporal patterns of landscape functional properties on biodiversity is still rather limited. We examined the relationship between common passerine species richness and ecosystem functioning dynamics, namely seasonality, measured by satellite remote sensing. We focused on rural landscapes of a mountain National Park in Portugal undergoing rapid reshaping from agro-pastoral mosaics to early successional landscapes. We applied multi-model inference to compare the hypothesis of landscape seasonality as a driver of species richness with three competing hypotheses representing structural habitat heterogeneity, disturbance, and availability of food resources. We found support for landscape seasonality and its spatial heterogeneity in explaining passerine richness in mountain rur al landscapes. Conversely, no significant support of the remaining hypotheses was found. These results highlight the role of ecosystem functioning variability in space and time. They also stress the importance of considering species-energy relationships for conservation at the landscape level. Specifically, they provide support and guidance to the identification of meaningful functional attributes of the landscape that shape its biodiversity. Our results further demonstrate the utility of remote sensing approaches and products to measure those attributes and follow their trends through time. Spatially-explicit measures of energy variability, such as the functional amplitude between winter and summer retrieved from earth observations, can link global socio-environmental change to species’ responses and support the inclusion of landscape seasonality on conservation and monitoring frameworks.
Dynamic trade-off analysis of multiple ecosystem services under land use change scenarios: Towards putting ecosystem services into planning in Iran
Authors: Asadolahi, Z; Salmanmahiny, A; Sakieh, Y; Mirkarimi, SH; Baral, H; Azimi, M
Source: ECOLOGICAL COMPLEXITY, 36 250-260; DEC 2018
Abstract: This study dynamically analyzes the trade-off between three ecosystem services (ESs), including soil retention, habitat quality, and food supply in the Gorganrood watershed, northeastern Iran. In this regard, several analytic tools including the Integrated Valuation of Ecosystem Services and Tradeoff (InVEST) model, Cellular Automata-Markov Chain (CA-MC) model, Intensity Analysis and Trade-Off (TO) index are employed to dynamically link the process of land use change with the temporal flow of ESs. Two scenarios of business as usual (BAU) and environmentally sound planning (ESP) are developed to produce land use layers under various planning strategies. Accordingly, the BAU scenario is more successful at providing a landscape with a higher acreage of agricultural fields and rangelands. Based on tradeoff index results, the BAU scenario establishes a landscape in which food supply is intensified by declining land capability for providing soil retention and habitat quality servic es. In contrast, the ESP scenario generates a landscape in which the outputs of the TO index indicate that habitat quality and soil retention benefits are improved by decreasing the potential of the area to supply food. This study emphasizes that a dynamic trade-off analysis of multiple ESs can assist planners and policy makers to make informed decisions and undertake strategic planning in Iran.
Assessing sampling sufficiency of network metrics using bootstrap
Authors: Casas, G; Bastazini, VAG; Debastiani, VJ; Pillar, VD
Source: ECOLOGICAL COMPLEXITY, 36 268-275; DEC 2018
Abstract: Sampling the full diversity of interactions in an ecological community is a highly intensive effort. Recent studies have demonstrated that many network metrics are sensitive to both sampling effort and network size. Here, we develop a statistical framework, based on bootstrap resampling, that aims to assess sampling sufficiency for some of the most widely used metrics in Network Ecology, namely connectance, nestedness (NODF- nested overlap and decreasing fill) and modularity (using the QuaBiMo algorithm). Our framework can generate confidence intervals for each network metric with increasing sample size (i.e., the number of sampled interaction events, or number of sampled individuals), which can be used to evaluate sampling sufficiency. The sample is considered sufficient when the confidence limits reach stability or lie within an acceptable level of precision for the aims of the study. We illustrate our framework with data from three quantitative networks of plant and frugiv orous birds, varying in size from 16 to 115 species, and 17 to 2,745 interactions. The results show that, for the same dataset, sampling sufficiency may be reached at different sample sizes depending on the metric of interest. The bootstrap confidence limits reached stability for the two largest networks, but were wide and unstable with increasing sample size for all three metrics estimated for the smallest network. The bootstrap method is useful to empirical ecologists to indicate the minimum number of interactions necessary to reach sampling sufficiency for a specific network metric. It is also useful to compare sampling techniques of networks in their capacity to reach sampling sufficiency. Our method is general enough to be applied to different types of metrics and networks.
Comparing vis-NIRS, LIBS, and Combined vis-NIRS-LIBS for Intact Soil Core Soil Carbon Measurement
Authors: Bricklemyer, RS; Brown, DJ; Turk, PJ; Clegg, S
Source: SOIL SCIENCE SOCIETY OF AMERICA JOURNAL, 82 (6):1482-1496; NOV-DEC 2018
Abstract: Soil organic carbon (SOC) measurement is critically important to quantify regional and global soil C stocks and better understand soil C biogeochemical processes. Recent studies employing laser-induced breakdown spectroscopy (LIBS) and visible-near infrared diffuse reflectance spectroscopy (vis-NIRS) indicate their potential for in situ soil C determination. Visible and near infrared diffuse reflectance spectroscopy and LIBS spectroscopy fundamentally differ and we hypothesize that their integration would provide improved soil C predictions. We report the first rigorous integration of vis-NIRS and LIBS, evaluating the precision of vis-NIRS, LIBS, and combined vis-NIRS-LIBS spectra for simulated in situ soil profile total C (TC), inorganic C (IC) and SOC measurement. Three multivariate variable selection and regression approaches were evaluated for soil C prediction. The highest soil C prediction accuracies were observed using multivariate regression with covariance estimation (MRCE). Inorganic C was best predicted by LIBS, vis-NIRS provided better SOC predictions, and TC was best predicted using combined vis-NIRS-LIBS data. Combined vis-NIRS-LIBS did not consistently increase soil C prediction accuracy. Soil organic C was not well predicted, presumably due to challenges associated with scanning surfaces of intact soil cores, variable SOC chemistries, and low SOC variation in the dataset. Considering the challenging conditions under which combined vis-NIRS-LIBS was tested for soil C measurement, data integration and model calibrations had acceptable performance. Further testing under more controlled soil conditions with samples containing greater SOC diversity is necessary to determine the technical potential of combined vis-NIRS/LIBS for soil C determination.
Change of support using non-additive variables with Gibbs Sampler: Application to metallurgical recovery of sulphide ores
Authors: Garrido, M; Ortiz, JM; Villaseca, F; Kracht, W; Townleye, B; Miranda, R
Source: COMPUTERS & GEOSCIENCES, 122 68-76; JAN 2019
Abstract: Flotation tests at laboratory scale describe the metallurgical behavior of the minerals that will be processed in the operational plant. This material is generally composed of ore and gangue minerals. These tests are usually scarce, expensive and sampled in large supports. This research proposes a methodology for the geostatistical modelling of metallurgical recovery, covering the change of support problems through additive auxiliary variables. The methodology consists of simulating these auxiliary variables using a Gibbs Sampler in order to infer the behavior of samples with smaller supports. This allows downscaling a large sample measurement into smaller ones, reproducing the variability at different scales considering the physical restrictions of additivity balance of the metallurgical recovery process. As a consequence, it is possible to apply conventional multivariate geostatistical tools to data at different supports, such as multivariable exploratory analysis, calculat ion of cross-variograms, multivariate estimations, among others. The methodology was tested using a drillhole database from an ore deposit, modelling recovery at a smaller support than that of the metallurgical tests. The support allowed for the use of the geochemical database, to consistently model the metal content in the feed and in the concentrate, in order to obtain a valid recovery model. Results show that downscaling the composite size reduces smoothing in the final model.
Joint simulation of compositional and categorical data via direct sampling technique - Application to improve mineral resource confidence
Authors: Talebi, H; Mueller, U; Tolosana-Delgado, R
Source: COMPUTERS & GEOSCIENCES, 122 87-102; JAN 2019
Abstract: Ore deposits usually consist of ore materials with different discrete (e.g. rock and alteration types) and continuous (e.g. geochemical and mineral composition) features. Financial feasibility studies are highly dependent on the modelling of these features and their associated joint uncertainties. Few geostatistical techniques have been developed for the joint modelling of high-dimensional mixed data (continuous and categorical) or constrained data, such as compositional data. The compositional nature of the mineral and geochemical data induces several challenges for multivariate geostatistical techniques, because such data carry relative information and are known for spurious statistical and spatial correlation effects. This paper investigates the application of the direct sampling algorithm for joint modelling of compositional and categorical data. In some mining projects the amount of available data may be enormous in some parts of the deposit and if the density of measure ments is sufficient, multivariate geospatial patterns can be derived from that data and be simulated (without model inference) at other undersampled areas of the deposit with similar characteristics. In this context, the direct sampling multiple-point simulation method can be implemented for this reconstruction process. The compositional nature of the data is addressed via implementing an isometric log-ratio transformation. The approach is illustrated through two case studies, one synthetic and one real. The accuracy of the results is checked against a set of validation data, revealing the potential of the proposed methodology for joint modelling of compositional and categorical information. The direct sampling technique can be considered as a smart move to assess the future risk and uncertainty of a resource by making use of all the information hidden within the early data.
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists
Authors: Shen, CP
Source: WATER RESOURCES RESEARCH, 54 (11):8558-8593; NOV 2018
Abstract: Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited f or information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.
When Should Irrigators Invest in More Water-Efficient Technologies as an Adaptation to Climate Change?
Authors: Malek, K; Adam, J; Stockle, C; Brady, M; Rajagopalan, K
Source: WATER RESOURCES RESEARCH, 54 (11):8999-9032; NOV 2018
Abstract: The western U.S. is expected to experience more frequent and severe droughts as a result of climate change, with potentially large impacts on agricultural production and the economy. Irrigated farmers have multiple options for minimizing the impact of drought including switching to more efficient irrigation technologies. More efficient technologies that increase the fraction of the water available to the crop root zone would allow farmers to maintain current production levels with less water. However, these systems are capital intensive. The objective of this study is to explore when (and under what climatic conditions) it makes economic sense for farmers to invest in new irrigation systems. We examine this in the Yakima River Basin in Washington State of the U.S. We use VIC-CropSyst, a large-scale grid-based modeling framework that mechanistically simulates hydrologic and agricultural processes. Water supply simulated by VIC-CropSyst drives a river system and water managemen t model (YAK-RW). A computational platform was developed to perform the economic analysis for each grid cell, crop type, and future climate scenario separately, which allowed us to explore whether the implementation of more efficient irrigation systems would be economically viable. Our results indicate that investing in a more efficient irrigation system improves agricultural economy of the Yakima River Basin (9% -25%). We also show that at the farm level, more significant droughts can provide economic incentives for investment up to a point. For severe climate change projections, droughts become frequent and severe enough that economic benefits of improving water use efficiency do not exceed investment costs.
Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network
Authors: Fersch, B; Jagdhuber, T; Schron, M; Volksch, I; Jager, M
Source: WATER RESOURCES RESEARCH, 54 (11):9364-9383; NOV 2018
Abstract: The consistent determination of soil moisture across scales is a persistent challenge in hydrology. Several measurement methods exist at distinct scales, each of which is challenging in terms of data processing, removal of vegetation and surface effects, and calibration. While in situ measurements are trusted at the point scale, distributed sensor networks extend the areal representation to the field scale. At this scale, also cosmic ray neutron sensing (CRNS) has become an established method to derive volume-averaged, root zone soil moisture over several tens of hectometers, but the signal is often biased due to biomass water. With airborne synthetic aperture radar (SAR)remote sensing, it is possible to cover regional scales, but the method is limited to the topmost soil layer and sensitive to vegetation parameters. In this study, the performance and synergistic potential of these complementary methods is investigated for the determination of soil moisture within a 55-km(2) Alpine foothill river catchment in Southern Germany. The individual approaches are evaluated and brought into synergy for a 9-ha grassland and several other locations within the catchment. The results indicate that the sensor network data provide valuable information to calibrate the mobile CRNS rover, and to optimize the vegetation removal within the polarimetric SAR retrieval algorithm. The root-mean-square errors for polarimetric synthetic aperture radar soil permittivity are 9.32 with the standard agriculture approach, 4.29 with the semi-stand-alone approach, and 0.31 with the sensor network optimized approach. Furthermore, the CRNS soil moisture product was improved by considering the remotely sensed cross-polarized backscatter product as a biomass water proxy.
The Fast and the Robust: Trade-Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models
Authors: Kavetski, D; Qin, YW; Kuczera, G
Source: WATER RESOURCES RESEARCH, 54 (11):9432-9455; NOV 2018
Abstract: Environmental modelers using optimization algorithms for model calibration face an ambivalent choice. Some algorithms, for example, Newton-type methods, are fast but struggle to consistently find global parameter optima; other algorithms, for example, evolutionary methods, boast better global convergence but at much higher cost (e.g., requiring more objective function calls). Trade-offs between accuracy/robustness versus cost are ubiquitous in numerical computation, yet environmental modeling studies have lacked a systematic framework for quantifying these trade-offs. This study develops a framework for benchmarking stochastic optimization algorithms in the context of environmental model calibration, where multiple algorithm invocations are typically necessary. We define reliability as the probability of finding the desired (global or tolerable) optimum from random initial points and estimate the number of invocations to find the desired optimum with prescribed confidence (he re 95%). A robust algorithm should achieve consistently high reliability across many problems. A characteristic efficiency metric for algorithm benchmarking is defined as the total cost (objective function calls over multiple invocations) to find the desired optimum with prescribed confidence. This approach avoids the pitfalls of existing approaches that compare costs without controlling the confidence in algorithm success. A case study illustrates the framework by benchmarking the Levenberg-Marquardt and Shuffled Complex Evolution (SCE) algorithms over three catchments and four hydrological models. In 8 of 12 scenarios, Levenberg-Marquardt is more efficient than SCEby sacrificing some of its speed advantage to match SCE reliability through more invocations. The proposed framework is easy to apply and can help guide algorithm selection in environmental model calibration.
Disaggregating Soil Moisture to Finer Spatial Resolutions: A Comparison of Alternatives
Authors: Ajami, H; Sharma, A
Source: WATER RESOURCES RESEARCH, 54 (11):9456-9483; NOV 2018
Abstract: The spatial and temporal variability in soil moisture modulates runoff generation and the degree of land-atmosphere coupling. Numerous statistical and modeling approaches have been implemented to characterize soil moisture spatial heterogeneity at fine spatial resolution using data from sparse observational networks or distributed model simulations. This characterization has been subsequently employed to translate coarse model simulations (of the order of a few hundred meters or kilometers) to finer spatial scales for a range of ensuing applications that rely on high-resolution characterization of soil moisture. One common feature of these disaggregation methods is that the impact of soil moisture memory is ignored. This results in both spatial and temporal persistence being poorly simulated, leading to poorer specifications of cropping and irrigation plans. To overcome this shortcoming, we developed a hybrid disaggregation method that uses the first-order autoregressive mode l (AR1) constructed from fine-resolution (60m) soil moisture simulations to disaggregate catchment mean soil moisture obtained from remote sensing or semidistributed model simulations. Soil moisture simulations from an integrated land surface-groundwater model, ParFlow-Common Land Model in Baldry subcatchment, Australia, are used as virtual observations. We examined the AR1 method performance against topographic wetness index-based methods and those developed from temporal stability method. Results illustrate that the disaggregation schemes calibrated to a 10-day fine-scale model simulation perform better than the topographic-based methods in approximating soil moisture distribution at a 60-m resolution in the catchment. Furthermore, the AR1 model is the best model (Nash-Sutcliffe efficiency [NSE]>0.45) among various alternatives explored here. Applying the hybrid univariate AR1 model is promising for disaggregating semidistributed models’ soil moisture simulations while sig nificantly reducing the computational time.
New perspectives to use Munsell color charts with electronic devices
Authors: Kirillova, NP; Grauer-Gray, J; Hartemink, AE; Sileova, TM; Artemyeva, ZS; Burova, EK
Source: COMPUTERS AND ELECTRONICS IN AGRICULTURE, 155 378-385; DEC 2018
Abstract: This article considers the evolution of the use of the Munsell system for assessing soil color. In the 1920s, Munsell color disks were recommended for soil color measurement. Munsell soil color charts (MSCs) were introduced in the 1940s. In 1942, the American Standards Association recommended that spectrophotometers be used for quantitative measurements and the Munsell system be used only for the psychological characterization of color. Despite this recommendation, soil scientists have continued using MSCs to measure soil color semi-quantitatively.ve color was measured with a spectrophotometer. Chip colors were compared using the CIELAB color difference (Delta E-ab). To examine ambiguity, we calculated the Delta E-ab for every possible MSC color chip pair. Considerable ambiguity was found; 60% of the chips had duplicates. Chips were considered duplicates if the color difference was barely perceptible visually (Delta E-ab* < 3).To investigate adequacy, the color difference b etween 161 soil samples and their closest MSC chips was calculated. Only 52% of the samples had Delta E-ab* < 3. This indicated that the color range of the MSC does not adequately cover the range of natural soil colors.To study the reliability of MSCs, an old and a new MSC were compared. The identically designated chips in the old and new MSC generally had color differences of less than 3. Only 16% had Delta E-ab* > 3. In addition, chips within a chart or even within a sheet can fade over time in a non-uniform way. On the 2.5Y sheet, a stable group of color chips (similar to 63%) occurred. This stable group consisted of the chips that were the least prone to discoloration (Delta E-ab* < 3).This study determined that the inadequacy and ambiguity of MSCs can be overcome by using MSCs in combination with flatbed scanners. MSCs can be used to calibrate flatbed scanners for the purpose of soil color measurement. A procedure is proposed. The procedure calibrates the scanner using 7 chips from the stable group of the 2.5Y sheet. This proced! ure enables the measurement of soil color inexpensively and efficiently. The high efficiency of the method was confirmed by testing the accuracy of the soil color determinations for 20 soil samples, covering a wide color range. The calibration procedure quadruples the precision of color estimation compared to solely using MSCs and results in soil color measurements close to those achievable with spectrophotometers.
Methodology for payment for ecosystem services based on the concept of land use and management capability
Authors: Monteiro, LIB; Pruski, FF; Calegario, AT; Oliveira, ANG; Pereira, SB
Source: SOIL USE AND MANAGEMENT, 34 (4):515-524; DEC 2018
Abstract: Global population growth drives the increase in demand for water and food. Consequently, there is a build-up of pressure for land use for agricultural production, creating the necessity of sacrificing areas previously occupied by the native land cover to create production areas. However, globally, the possibilities of agricultural frontier expansion are limited, and agricultural expansion activities conducted without adequate planning can accelerate the erosive processes that decrease the potential land production capability. In an attempt to attenuate environmental imbalances, payment for ecosystem services (PES) programmes have been created, highlighting the possibility of their being applied in the agricultural sector. This study developed a methodology for PES that follows the basic principles of the land use capability classification system proposed by the United States Department of Agriculture. However, the study aimed to make the classification process operational in a way that notes the conditions in which a rural property maintains its production capability without jeopardizing its environmental role. In this way, it is possible to evaluate whether a rural property is suitable to receive PES. To detail the steps for applying the methodology, a case study was conducted on a rural property in the town of Itabira, Minas Gerais State, Brazil.
Digital soil assessment for quantifying soil constraints to crop production: a case study for rice in Punjab, India
Authors: Okonkwo, EI; Corstanje, R; Kirk, GJD
Source: SOIL USE AND MANAGEMENT, 34 (4):533-541; DEC 2018
Abstract: Assessments of land capability for particular functions such as food production need to allow for uncertainties both in the criteria used to specify the function and in information on relevant soil properties. In this paper, we evaluate the use of digital soil assessment (DSA) for dynamic assessment of soil capability allowing for both uncertainties and spatial variability in soil properties and flexibility in the values of assessment criteria. We do this for soil constraints to rice production in the state of Punjab, India, where soil salinity and alkalinity are potentially important constraints to cropping. In DSA, spatial predictions of soil properties and associated uncertainties made with digital soil mapping (DSM) are used to assess soil functions. We use a combination of DSM and Monte Carlo simulation methods to estimate the spatial variation in soil electrical conductivity (ECe) and pH to 20 cm depth in soils across Punjab. We then use the estimates and associated unc ertainties to assess the likelihood that soil salinity or alkalinity or both could constrain rice production. Results show that allowing for prediction uncertainties of soil attributes results in far smaller areas affected by salinity (1.2 vs. 2.0 Mha) and alkalinity (3.0 vs. 3.2 Mha). Results also show the importance of correctly setting threshold values for constraint criteria and the flexibility of the DSA approach for setting thresholds.