Fractional Ground Cover Research Articles

Canopeo app as image-based phenotyping tool in controlled environment utilizing Arabidopsis mutants.

Canopeo app was developed as a simple, accurate, rapid, and free tool to analyze ground cover fraction (GCF) from red-green-blue (RGB) images and videos captured in the field. With increasing interest in tools for plant phenotyping in controlled environments, the usefulness of Canopeo to identify differences in growth among Arabidopsis thaliana mutants in a controlled environment were explored. A simple imaging system was used to compare Arabidopsis mutants based on the FLAVIN-BINDING, KELCH REPEAT, F-BOX-1 (FKF1) mutation, which has been identified with increased biomass accumulation. Two FKF1 lines such as null expression (fkf1-t) and overexpression (FKF1-OE) lines were used along with wild type (Col-0). Canopeo was used to phenotype plants, based on biomass estimations. Under long-day photoperiod, fkf1-t had increased cellulose biosynthesis, and therefore biomass. Resource partitioning favored seedling vigor and delayed onset of senescence. In contrast, FKF1-OE illustrated a determinative growth habit where plant resources are primarily allocated for seed production. This study demonstrates the use of Canopeo for model plants and highlights its potential for phenotyping broadleaved crops in controlled environments. The value of adapting Canopeo for lab use is those with limited experience and resources have access to phenotyping methodology that is simple, accessible, accurate, and cost-efficient in a controlled environment setting.

Canopy detection beyond the field: Colored backgrounds impact precision of Canopeo

The development of Canopeo as a close-range remote sensor for measuring ground cover fraction (GCF) offered farmers and scientists an accurate, simple, low-cost tool for monitoring health and development throughout the plant lifecycle. However, a significant obstacle to image-based monitoring of plant performance is the difficulty of object distinction between plant and background sharing similar colors. The overall goal of this research was to test Canopeo's sensitivity for detecting GCF when plants were imaged on different colored backgrounds in a greenhouse environment. We therefore tested Canopeo's ability to detect plant versus non-plant pixels in each image (resolution 72 × 72) using ten complex flat backgrounds. Multicolored backgrounds resembling flooring which may be found in a greenhouse setting (concrete, brick painted white, natural wood plank, and wood painted white with scuffs) resulted in least amount of deviation (<0.46) when analyzed with the control (flat black) background. Canopeo overestimated GCF of Viburnum sp. and E. pinnatum cv. on a green background which resulted in the greatest amount of deviation (>20). Canopeo demonstrated greatest underestimation GCF for Viburnum sp. and E. pinnatum cv. on a red background. When GCF of the green background was omitted, the r2 value of 0.75, or goodness of fit, suggest approximately 75% of the sampling variation can be described by the background color and not experimental error. Canopeo is an easily accessible tool for researchers and farmers to monitor plant growth and development on a diversity of backgrounds beyond soil and field settings.

A framework for the assessment of farm diversification options in broadacre agriculture

CONTEXTThe economic and environmental sustainability of farming systems is under increased pressure from extreme climate variability and market volatility. The diversification of large-scale farms can increase profits, resilience, and deliver sustainability outputs. OBJECTIVETo (a) propose a farm diversification framework specifically designed for large-scale commercial farms; and (b) demonstrate its application to assess the benefits and trade-offs associated with alternative diversification options for a case-study farm in Northern Queensland, Australia. METHODSA literature review and discussions with large-scale commercial farmers, a Natural Resource Management NGO, and a regional council were used to co-design our framework. The framework was then used to guide discussions with a case-study farmer and conduct a participatory modelling exercise on alternative cropping-based diversification scenarios. We used a simulation model to produce quantitative information on the present and co-designed alternative scenarios, to iteratively inform discussions with the participating case-study farmer. The simulated scenarios included (i) the current system of dryland (non-irrigated) monocropping of cotton, (ii) a dryland cotton-maize rotation, (iii) an irrigated cotton-maize rotation, and (iv) an irrigated maize-mungbean-cotton rotation. The benefits and trade-offs of each scenario were assessed across five performance measures: downside risk, profit, water-use efficiency, ground cover and, from the discussion with the participating farmer, the skills needed to de-risk the diversification process. RESULTSResults from the co-designed scenarios showed that adding dryland maize as a rotation crop can reduce downside risks by 12.5% and increase fractional ground cover by more than two-fold, i.e., from 20% to >40%, compared to the current system. The use of irrigation in the cropping system increased the median gross margin by $2000 ha−1 and reduced downside risks to zero. The water-use efficiencies of the proposed irrigated scenarios were similar. Farmers reported that developing skills in irrigated cropping could increase their capacity to dynamically chose more profitable crops to sow with certainty of higher returns, but it would require up-front investments in terms of money and time. SIGNIFICANCEOur proposed framework can be used to assess benefits and trade-offs of alternative farm diversification options across a range of performance dimensions (risk; profit; resource-use efficiency; skills). These assessments will ensure that the co-designed options are evaluated across multiple dimensions, all within the local context of the study and farmers' preference(s) and circumstances. The use of a co-design approach can be expected to increase learnings for all those involved and increase the chances of practice change and adoption.

Beyond "greening" and "browning": Trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation.

Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long-term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modeling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002-2020 time series, and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non-photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote sensing-based monitoring of trends in grasslands so that underlying processes can be discerned.

Searching for Sustainable-Irrigation Issues of Clementine Orchards in the Syrian Akkar Plain: Effects of Irrigation Method and Canopy Size on Crop Coefficients, Transpiration, and Water Use with SIMDualKc Model

Citrus is one of the most valuable crops in Syria, with the largest production areas in the Tartus and Latakia provinces. Water-saving policies have been adopted to modernize the irrigation systems and increase water productivity. Following dedicated research, this study aimed to evaluate the water balance in clementine trees irrigated with diverse methods and schedules using the SIMDualKc software model. Two experiments are reported: one with 10–14 years old trees irrigated with different methods (2007−2011) and the other with the same trees but now 18−20 years old, irrigated with different schedules (2015−2019). The SIMDualKc model successfully simulated the soil water contents measured in the various field plots, with root mean square error values lower than 0.004 m3 m−3 and modeling efficiencies up to 0.83. The model-calibrated standard basal crop coefficients (Kcb) were approximately constant throughout all growing stages, assuming values of 0.54−0.55 for the mature trees having smaller height (h) and fraction of ground cover (fc), and 0.64 for older trees with larger canopies, i.e., larger h and fc. With drip irrigation, single Kc had a higher value (1.14) at the end, non-growing, and initial stages, and a lower value (0.75–0.76) during mid-season (Kc mid), because precipitation was lesser then, contributing less to soil evaporation. On the other hand, Kc values were nearly constant with micro-sprinkler and surface irrigation techniques because the ground was fully wetted. The Kcb values derived from the fraction of ground cover and height (A&amp;P approach) were similar to those obtained from the model, thus showing that the A&amp;P approach represents a practical alternative to estimate Kcb in the practice of irrigation management. The soil water balance further revealed a large weight of the terms corresponding to the non-beneficial water consumption and non-consumptive water use when the fraction wetted was large and the application efficiencies were low. These terms were reduced, namely, evaporation losses when drip irrigation was used. This study, thus, provides a valuable tool for improving the irrigation management, water saving, and water productivity of Syrian citrus production systems.

Integration of Satellite-Based Optical and Synthetic Aperture Radar Imagery to Estimate Winter Cover Crop Performance in Cereal Grasses

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.

Changes in the grasslands of the Caucasus based on Cumulative Endmember Fractions from the full 1987–2019 Landsat record

Grasslands are important for global biodiversity, food security, and climate change analyses, which makes mapping and monitoring of vegetation changes in grasslands necessary to better understand, sustainably manage, and protect these ecosystems. However, grassland vegetation monitoring at spatial and temporal resolution relevant to land management (e.g., ca. 30-m, and at least annually over long time periods) is challenging due to complex spatio-temporal pattern of changes and often limited data availability. Here we assess both short- and long-term changes in grassland vegetation cover from 1987 to 2019 across the Caucasus ecoregion at 30-m resolution based on Cumulative Endmember Fractions (i.e., annual sums of monthly ground cover fractions) derived from the full Landsat record, and temporal segmentation with LandTrendr. Our approach combines the benefits of physically-based analyses, missing data prediction, annual aggregations, and adaptive identification of changes in the time-series. We analyzed changes in vegetation fraction cover to infer the location, timing, and magnitude of vegetation change episodes of any length, quantified shifts among all ground cover fractions (i.e., green vegetation, non-photosynthetic vegetation, soil, and shade), and identified change pathways (i.e., green vegetation loss, desiccation, dry vegetation loss, revegetation green fraction, greening, or revegetation dry fraction). We found widespread long-term positive changes in grassland vegetation (32.7% of grasslands), especially in the early 2000s, but negative changes pathways were most common before the year 2000. We found little association between changes in green vegetation and meteorological conditions, and varied relationships with livestock populations. However, we also found strong spatial heterogeneity in vegetation dynamics among neighboring fields and pastures, demonstrating capability of our approach for grassland management at local levels. Our results provide a detailed assessment of grassland vegetation change in the Caucasus Ecoregion, and present an approach to map changes in grasslands even where availability of Landsat data is limited.

Prediction of crop coefficients from fraction of ground cover and height: Practical application to vegetable, field and fruit crops with focus on parameterization

The A&P approach, developed by Allen and Pereira (2009), estimates single and basal crop coefficients (Kc and Kcb) from the observed fraction of ground cover (fc) and crop height (h). The practical application of the A&P for several crops was reviewed and tested in a companion paper (Pereira et al., 2020). The current study further addresses the derivation of optimal values for A&P parameter values representing canopy transparency (ML) and stomatal adjustment (Fr), and tests the resulting model performance. Values reported in literature of ML and Fr were analysed. Optimal ML and Fr values were derived by a numerical search that minimized the differences between Kcb A&P with standard Kcb for vegetable, field, and fruit crops as tabulated by Pereira et al. (2021a, 2021b) and Rallo et al. (2021). Sources for fc were literature reviews supplemented by a remote sensing survey. Computed Kcb and Kc for mid- and end-season together with associated parameters values were tabulated. To improve the usability of the ML and Fr parameters a cross validation was performed, which consisted of the linear regression between Kcb computed by A&P and observed Kcb relative to independent data sets obtained from field observations. Results show that both series of Kcb match well, with regression coefficients very close to 1.0, coefficients of determination near 1.0, and root mean square errors (RMSE) of 0.06 for the annual crops and RMSE = 0.07 for the trees and vines. These errors represent less than 10% of most of the computed tabulated Kcb. The tabulated Fr and ML of this paper can be regarded as defaults to support A&P field practice when observations of fc and h are performed. Therefore, the A&P approach shows to be appropriate for use in irrigation scheduling and planning when fc and h are observed using ground and/or remote sensing, hence supporting irrigation water savings.

Vegetation Canopy Gap Size and Height: Critical Indicators for Wind Erosion Monitoring and Management

Indicators of vegetation cover and structure are widely available for monitoring and managing rangeland wind erosion. Identifying which indicators are most appropriate for managers could improve wind erosion mitigation and restoration efforts. Vegetation cover directly protects the soil surface from erosive winds and reduces wind erosivity by extracting momentum from the air. The portion of the soil surface that is directly protected by vegetation is adequately described by fractional ground cover indicators. However, the aerodynamic sheltering effects of vegetation, which are more important for wind erosion than for water erosion, are not captured by these indicators. As wind erosion is a lateral process, the vertical structure and spatial distribution of vegetation are most important for controlling where, when, and how much wind erosion occurs on rangelands. These controlling factors can be described by indicators of the vegetation canopy gap size distribution and vegetation height, for which data are collected widely in the United States by standardized rangeland monitoring and assessment programs. In this paper we address why canopy gap size distribution and vegetation height are critical indicators of rangeland wind erosion and health. We review wind erosion processes to explain the physical role of these vegetation attributes. We then address the management implications including availability of data on the indicators on rangelands and needs to make the indicators and model estimates of wind erosion more accessible to the range management community.

One-dimensional models of radiation transfer in heterogeneous canopies: a review, re-evaluation, and improved model

Abstract. Despite recent advances in the development of detailed plant radiative transfer models, large-scale canopy models generally still rely on simplified one-dimensional (1-D) radiation models based on assumptions of horizontal homogeneity, including dynamic ecosystem models, crop models, and global circulation models. In an attempt to incorporate the effects of vegetation heterogeneity or “clumping” within these simple models, an empirical clumping factor, commonly denoted by the symbol Ω, is often used to effectively reduce the overall leaf area density and/or index value that is fed into the model. While the simplicity of this approach makes it attractive, Ω cannot in general be readily estimated for a particular canopy architecture and instead requires radiation interception data in order to invert for Ω. Numerous simplified geometric models have been previously proposed, but their inherent assumptions are difficult to evaluate due to the challenge of validating heterogeneous canopy models based on field data because of the high uncertainty in radiative flux measurements and geometric inputs. This work provides a critical review of the origin and theory of models for radiation interception in heterogeneous canopies and an objective comparison of their performance. Rather than evaluating their performance using field data, where uncertainty in the measured model inputs and outputs can be comparable to the uncertainty in the model itself, the models were evaluated by comparing against simulated data generated by a three-dimensional leaf-resolving model in which the exact inputs are known. A new model is proposed that generalizes existing theory and is shown to perform very well across a wide range of canopy types and ground cover fractions.

Evapotranspiration and crop coefficients from lysimeter measurements for sprinkler-irrigated canola

Canola is a water-stress tolerant crop, which could be an alternative in areas with limited water resources. However, in arid and semi-arid environments where rainfall events are scarce and increasingly erratic, the use of irrigation is necessary for canola production to reach its maximum yield. The goal of this study was to determine the crop evapotranspiration (ETc) and crop coefficients of sprinkler irrigated canola (Brassica napus L.) under non-limiting soil water content conditions. A 2-year field experiment was conducted in the lysimeter facility located in Albacete (SE Spain). A large weighing lysimeter (2.7 × 2.3 × 1.7 m), with an accuracy of 0.04 mm equivalent water depth, was used to measure the daily crop evapotranspiration (ETc) rate throughout two growing seasons. ETc values were determined using daily mass change in the lysimeter. Cumulative ETc was replaced in the lysimeter through sprinkler irrigation applications, thus crop water stress was avoided. Seasonal lysimeter based (measured) canola ETc was 472 and 602 mm in 2008 and 2012, respectively. The 28 % higher ETc value in 2012 was mainly due to a much higher evaporative demand during the crop growth mid-season period of 2012. The Kc values were determined using grass reference evapotranspiration (ETo) calculated with the FAO56 Penman-Monteith equation and the ETc calculations from the lysimeter data. The dual crop coefficient approach was used to separate crop transpiration (Kcb) from soil evaporation (Ke). For the two canola seasons, mid-season Kc and Kcb values, after FAO56 climate adjustment, were Kc mid (std) = 1.15 and Kcb mid (std) = 1.11. Those values were reached coinciding with maximum fraction of ground cover (fc) values of up 0.95 and 0.97 for 2008 and 2012, respectively. The seasonal evaporation component for sprinkler-irrigated canola was estimated to be about 24 % and 19 % of ETc in 2008 and 2012, respectively. The good linear relationship found between canola Kcb values and fc and the excellent agreement found between remotely sensed vegetation indices (VIs) and different biophysical parameters, such as Kcb and fc, will allow monitoring and estimating the spatially distributed water requirements of canola at field and regional scales using multispectral satellite imagery.

Comparison of Hyperspectral Versus Traditional Field Measurements of Fractional Ground Cover in the Australian Arid Zone

The collection of high-quality field measurements of ground cover is critical for calibration and validation of fractional ground cover maps derived from satellite imagery. Field-based hyperspectral ground cover sampling is a potential alternative to traditional in situ techniques. This study aimed to develop an effective sampling design for spectral ground cover surveys in order to estimate fractional ground cover in the Australian arid zone. To meet this aim, we addressed two key objectives: (1) Determining how spectral surveys and traditional step-point sampling compare when conducted at the same spatial scale and (2) comparing these two methods to current Australian satellite-derived fractional cover products. Across seven arid, sparsely vegetated survey sites, six 500-m transects were established. Ground cover reflectance was recorded taking continuous hyperspectral readings along each transect while step-point surveys were conducted along the same transects. Both measures of ground cover were converted into proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil for each site. Comparisons were made of the proportions of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil derived from both in situ methods as well as MODIS and Landsat fractional cover products. We found strong correlations between fractional cover derived from hyperspectral and step-point sampling conducted at the same spatial scale at our survey sites. Comparison of the in situ measurements and image-derived fractional cover products showed that overall, the Landsat product was strongly related to both in situ methods for non-photosynthetic vegetation and bare soil whereas the MODIS product was strongly correlated with both in situ methods for photosynthetic vegetation. This study demonstrates the potential of the spectral transect method, both in its ability to produce results comparable to the traditional transect measures, but also in its improved objectivity and relative logistic ease. Future efforts should be made to include spectral ground cover sampling as part of Australia’s plan to produce calibration and validation datasets for remotely sensed products.

Water use and irrigation requirements for date palms on commercial farms in the hyper-arid United Arab Emirates

The production of dates in the hyper-arid deserts of the United Arab Emirates (UAE) is economically and culturally important. All of the commercial date palms need to be irrigated, and the prime source of this water is groundwater. However the quantity and quality of the groundwater resources in the UAE are being compromised by groundwater extraction at 25 times the rate of recharge, and a rise in salinity as the reserves dwindle. Rates of groundwater use on commercial farms need to be the minimum required for date production and the leaching of salts from the rootzone. We have shown that tree water use, ETc, can be predicted from 0.95 LI x ETo where LI is the fraction of incoming visible light intercepted by the canopy, and ETo is the reference evapotranspiration. We now surveyed 10 commercial date farms in Abu Dhabi and determined the LI of date canopies by proximal sensing with a light-stick across a range of tree densities, tree ages, and groundwater salinities. We were able to link LI to the fractional ground cover, FGC, obtained by analysis of satellite images. Thus it was possible to determine ETc of date palms across all of these commercial farms, and potentially all the UAE. The median ETc of the 10 farms was 70 kL y−1, which was about twice that observed in our experiments at a research station near Dubai. Some 10% of the trees on these commercial farms were predicted to use over 100 kL y−1. It is suggested that irrigation should be at 1.5 x ETc to account for a 25% factor-of-safety, and a 25% salt-leaching fraction. Reductions in irrigation would be achieved by more aggressive pruning of the fronds to reduce LI, and hence ETc, without compromising date yield.

Long-term vegetation response following post-fire straw mulching

BackgroundStraw mulching is one of the most common treatments applied immediately post fire to reduce soil erosion potential and mitigate post-fire effects on water quality, downstream property, and infrastructure, but little is known about the long-term effects on vegetation response. We sampled six fires that were mulched between 9 and 13 years ago in western US dry conifer forests. We compared understory plant species diversity and abundance, tree seedling density and height by species, and fractional ground cover on mulched and unmulched paired plots.ResultsMulch did not influence understory plant diversity, species richness, or fractional ground cover. However, on mulched plots, tree seedlings grew taller faster, especially on north-facing aspects, and there was slightly more graminoid cover. Mulch did not affect overall tree seedling density, but there were fewer ponderosa pine (Pinus ponderosa Lawson & C. Lawson) and more Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in mulched areas, especially on south-facing slopes.ConclusionsManagers will be able to weigh the long-term implications of mulching against the short-term reductions in soil erosion potential. While there are many concerns about vegetation suppression and exotic species introduction from using straw mulch, our study suggests that the long-term effects are subtle 9 to 13 years after post-fire mulching.

Crop Coefficients and Transpiration of a Super Intensive Arbequina Olive Orchard using the Dual Kc Approach and the Kcb Computation with the Fraction of Ground Cover and Height

The SIMDualKc model was used to simulate crop water requirements for a super high density olive orchard in the region of Alentejo, Portugal. This model uses the dual crop coefficient approach to estimate and partitioning the actual crop evapotranspiration (ETc act) and therefore to perform the soil water balance. The model was calibrated with 2011 tree transpiration using trunk sap flow measurements and was validated using similar data from 2012 and tested with 2013 data. Low root mean square errors (RMSE &lt; 0.53 mm·d−1) and acceptable modelling efficiency indicators (EF &gt; 0.25) were obtained. Further validation was performed comparing modelled ETc act with eddy covariance measurements. These indicators support the appropriateness of using SIMDualKc to guide irrigation management. The basal crop coefficient (Kcb) curves obtained with SIMDualKc for those 3 years were compared with the Kcb values computed with the Allen and Pereira approach (A&amp;P approach) where Kcb is estimated from the fraction of ground cover and plant height considering an adjustment factor for crop stomatal control (Fr). Fr values were obtained through a trial and error procedure through comparing the Kcb estimated with this approach and with SIMDualKc. The Kcb curves obtained by both methods resulted highly correlated, which indicates that the A&amp;P approach may be used in the irrigation management practice to estimate crop water requirements. Results of performing the soil water balance with SIMDualKc have shown that soil evaporation is a large fraction of ETc act, varying between 41% and 45% for the 3 years under study. Irrigation, applied with a drip system, represented 39 to 56% of ETc act, which shows the great importance of irrigation to achieve the water requirements of super intensive olive orchards. Nevertheless, the analysis has shown that the irrigation management adopted at the orchard produces a water deficit larger than desirable, with a ratio of ETc act to non-stressed crop evapotranspiration (ETc) varying from 70% to 94% during the mid-season, when that ratio for a eustress irrigation management could be around 90%.

Evapotranspiration Partition and Crop Coefficients of Tifton 85 Bermudagrass as Affected by the Frequency of Cuttings. Application of the FAO56 Dual Kc Model

This study aims to model the impacts of the frequency of cuttings of Tifton 85 bermudagrass on the dynamics of evapotranspiration (ETc) and to derive crop coefficients appropriate for grass water management. Two seasons of experimentation were used with four different cutting treatments which provided field data for calibration and validation of the soil water balance model SIMDualKc for all treatments. Cuttings were performed after the cumulative growth degree days (CGDD) attained 124 °C, 248 °C and 372 °C, thus from short to very long intervals between cuttings. SIMDualKc adopts the Food and Agriculture Organization (FAO) dual Kc approach for partitioning ET into crop transpiration and soil evaporation, thus providing for an assessment of their dynamics. All treatments were irrigated to avoid water stress. Grass ETc was modelled adopting a Kcb curve to describe the ET variation for each cutting cycle, that is, using the FAO Kc curve that consists of a series of Kcb curves relative to each cutting cycle. Each individual Kcb curve consisted of three segments constructed when knowing the Kcb values at the initial, at the end of rapid growth, and at cutting, respectively Kcb ini, Kcb gro and Kcb cut. These Kcb values were first estimated using the equation relating Kcb to the density coefficient (Kd), which is computed from the fraction of ground cover (fc) and canopy height (h) at the same dates. The goodness of fit indicators relative to the calibration and validation of the SIMDualKc model were rather good, with the normalized root mean square error (RMSE) ranging from 4.0% to 6.7% of the mean available soil water. As an example, the standard Kcb values obtained after model calibration relative to the cuttings treatment with CGDD of 248 °C are: Kcb ini = 0.86, Kcb gro = 0.91 and Kcb cut = 0.96. Kcb values were smaller when the frequency of cuts was larger because h and fc were smaller, and were larger for reduced cuttings frequency since h and fc were then larger. Because the soil was wet most of the time, the soil evaporation Ke varied little but its value was small due to the combined effects of the fraction of crop cover and plant litter covering the soil. The values of Kc = Kcb+Ke also varied little due to the influence of Ke and the Kc curve obtained a form different from the Kcb curves, and a single Kc value was adopted for each cutting frequency, e.g., Kc = 0.99 for the treatment with CGDD of 248 °C. Results of the soil water balance have shown that, during the experimental periods, likely due to the effects of the El Niño Southern Oscillation (ENSO), runoff and deep percolation exceeded ETc. Moreover, the soil evaporation ratio was small: 14% in case of frequent cuttings and less for more spaced cuttings, thus with a transpiration ratio close to 90%, which indicates a very high beneficial consumptive water use, mainly when cuttings are not very frequent.

Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato

Two years experiments were set up to evaluate the performance of different vegetation indices (VI) to estimate shoot N concentration (Nc) and shoot dry matter (DM) for a potato crop grown under different nitrogen (N) treatments. Possibilities to improve the performance of VI using normalization by leaf area index (LAI) or camera-derived ground cover fraction (GC) were also investigated. Results indicated that Nc was significantly correlated to RRE (Near-infrared divided by red edge reflectance) and RRE/GC with a coefficient of determination (R2) of 0.62 and 0.78, respectively, indicating that inclusion of auxiliary parameter GC together with RRE substantially improved the correlation as compared to using only RRE. However, no significant correlation between Nc and RVI (Ratio Vegetation Index, near-infrared divided by red reflectance) or NDVI (Normalized Difference Vegetation Index) was found. However, DM was highly correlated to RVI and NDVI. Moreover, DM showed significant relationship (R2 = 0.86) with GC, highlighting its versatile usefulness in estimating agronomic variables DM and Nc, which are the core variables to assess N status of crops for a better N application.

Parameterization of AquaCrop model for vining pea biomass and yield predictions and assessing impacts of irrigation strategies considering various sowing dates

The AquaCrop model was parameterized and evaluated using data relative to supplemental irrigated vining pea for industry using observations in farmers’ fields located in the Ribatejo region, Portugal. Data refers to field observations relative to leaf area index (LAI), soil water content, biomass and final yield relative to two contrasting rainfall years, 2011 and 2012, a wet and a dry year, respectively. LAI data were used to parameterize the canopy cover (CC, %) curve. Results showed that after parameterization of the CC curve the respective root mean square errors (RMSE) were smaller than 15 % of the fraction of ground cover by the crop. The model performance relative to the soil water balance simulation revealed a slight over-estimation bias for the wet year but estimation errors were small, with RMSE representing <5 % of the average soil water content observations. Good accuracy for biomass and yield predictions was obtained, with deviations between predictions and observations ranging from 1 to 7 % of the average observed biomass and up to 11 % of the average observed yield. Overall, results have shown that AquaCrop is appropriate for vining pea biomass and yield estimation. Therefore, the model was explored to assess impacts of sowing dates and alternative supplemental irrigation strategies on water use and yield. Results have shown that sowing by early January is appropriate in terms of improved water use and yield and that a very mild deficit irrigation schedule may be adopted without significantly affecting yields.

A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures

Abstract. Timely and accurate monitoring of pasture biomass and ground cover is necessary in livestock production systems to ensure productive and sustainable management. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since data can be returned in near real time. Proximal sensors have the potential for deployment on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. There are unresolved challenges in gathering reliable sensor data and in calibrating raw sensor data to values such as pasture biomass or vegetation ground cover, which allow meaningful interpretation of sensor data by livestock producers. Our goal was to assess whether a combination of proximal sensors could be reliably deployed to monitor tropical pasture status in an operational beef production system, as a precursor to designing a full sensor deployment. We use this pilot project to (1) illustrate practical issues around sensor deployment, (2) develop the methods necessary for the quality control of the sensor data, and (3) assess the strength of the relationships between vegetation indices derived from the proximal sensors and field observations across the wet and dry seasons. Proximal sensors were deployed at two sites in a tropical pasture on a beef production property near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multispectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each of which were operated over 18 months. Raw data from each sensor was processed to calculate multispectral vegetation indices. The data capture from the digital cameras was more reliable than the multispectral sensors, which had up to 67 % of data discarded after data cleaning and quality control for technical issues related to the sensor design, as well as environmental issues such as water incursion and insect infestations. We recommend having a system with both sensor types to aid in data interpretation and troubleshooting technical issues. Non-destructive observations of pasture characteristics, including above-ground standing biomass and fractional ground cover, were made every 2 weeks. This simplified data collection was designed for multiple years of sampling at the remote site, but had the disadvantage of high measurement uncertainty. A bootstrapping method was used to explore the strength of the relationships between sensor and pasture observations. Due to the uncertainty in the field observations, the relationships between sensor and field data are not confirmational and should be used only to inform the design of future work. We found the strongest relationships occurred during the wet season period of maximum pasture growth (January to April), with generally poor relationships outside of this period. Strong relationships were found with multispectral indices that were sensitive to the green and dry components of the vegetation, such as those containing the band in the lower shortwave infrared (SWIR) region of the electromagnetic spectrum. During the wet season the bias-adjusted bootstrap point estimate of the R2 between above-ground biomass and the normalized ratio between the SWIR and red bands (NVI-SR) was 0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of green vegetation observed in three dimensions and a simple ratio between the near infrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00). Relationships between field data and the vegetation index derived from the digital camera images were generally weaker than from the multispectral sensor data, except for green vegetation observations in two and three dimensions. Our successful pilot of multiple proximal sensors supports the design of future deployments in tropical pastures and their potential for operational use. The stringent rules we developed for data cleaning can be more broadly applied to other sensor projects to ensure quality data. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely on-farm decision-making.

Assessing post-fire ground cover in Mediterranean shrublands with field spectrometry and digital photography

Fire severity can be assessed by identifying and quantifying the fractional abundance of post-fire ground cover types, an approach with great capacity to predict ecosystem response. Focused on shrubland formations of Mediterranean-type ecosystems, three burned areas (Ibieca and Zuera wildfires and Peñaflor experimental fire) were sampled in the summers of 2006 and 2007. Two different ground measurements were made for each of the 356 plots: (i) 3-band high spatial resolution photography (HSRP) and (ii) the hemispherical-conical reflectance factor (HCRF) in the visible to near-infrared spectral range (VNIR, 400–900nm). Stepwise multiple lineal regression (SMLR) models were fitted to spectral variables (HCRF, first derivative spectra or FDS, and four absorption indices) to estimate the fractional cover of seven post-fire ground cover types (vegetation and soil – unburned and charred components – and ash – char and ash, individually and as a combined category). Models were developed and validated at the Peñaflor site (training, n=217; validation, n=88) and applied to the samples from the Ibieca and Zuera sites (n=51). The best results were observed for the abundance estimations of green vegetation (Radj.2 0.70–0.90), unburned soil (Radj.2 0.40–0.75), and the combination of ashes (Radj.2 0.65–0.80). In comparison of spectral data, FDS outperforms reflectance or absorption data because of its higher accuracy levels and, importantly, its greater capacity to yield generalizable models. Future efforts should be made to improve the estimation of intermediate severity levels and upscaling the developed models. In the context of fire severity assessment, our study demonstrates the potential of hyperspectral data to estimate in a quick and objective manner post-fire ground cover fractions and thus provide valuable information to guide management responses.