Green Canopy Cover Research Articles

The Use of Canopeo for Seedling Cotton Health Ratings in Small Plot Research

Field experiments were conducted in 2017 and 2018 in west Tennessee to determine if Canopeo, an image analysis tool available as a smartphone app, could be used to supplement current methods to estimate cotton, Gossypium hirsutum L., seedling health in small-plot research tests. A total of six tests, providing a range of cotton seedling health, were used in this analysis. Cotton seedlings in replicated small-plot tests were visually rated for vigor and thrips (Thysanoptera: Thripidae) injury. A photograph of the center two rows of each plot was taken and analyzed to determine green canopy cover using Canopeo. Additionally, above ground biomass samples were collected in three of the tests. Strong correlations were observed between green canopy cover and biomass, green canopy cover and vigor, and thrips injury ratings and biomass. These data suggest that green canopy cover assessment using Canopeo is a useful and non-destructive way to objectively assess treatment effects on plant health in small-plot cotton research trials.

Modelling and measurement of water productivity and total evaporation in a dryland soybean crop

Simpler crop models simulating evaporation are needed to provide information to farmers, policy makers and decision makers on how to maximise crop yield responses to water. This is becoming important as the frequency and severity of droughts in South Africa is increasing. In this regard, prediction of yield, determination of water productivity and total evaporation (ET) are increasingly becoming essential in water resource management. The overall objective of the study was to compare the FAO AquaCrop daily model output of ET to the residual ET for non-stressed dryland soybean in a sub-humid climate. Energy balance residual ET estimates using an eddy covariance (EC) system and modelled ET using AquaCrop obtained from Glycine max (L.) Merrill grown in the midlands of KwaZulu-Natal, South Africa during the 2012/13 growing season are compared. The modelled and observed yield showed good agreement, while the residual ET was 21.6% less than the modelled. The energy balance closure computed using the daily sums of sensible heat and latent energy fluxes against daily available energy flux for unstable atmospheric conditions was 0.77. A closure of 0.99 was achieved when the EC latent energy flux was replaced with residual latent energy flux. A good fit between the modelled and observed percentage green canopy cover was observed (slope = 0.86, intercept = 15.46%, root mean square error = 10.50% and R2 = 0.83). Season-long daily residual ET values were consistently low for most of the growth stages compared to the modelled, except for the maturity stage. However, the residual ET comparisons with the AquaCrop model improved after gap-filling was applied to discarded data and for when the EC system failed.

Estimating Wheat Yield with Normalized Difference Vegetation Index and Fractional Green Canopy Cover

Core Ideas Normalized difference vegetation index and fractional green canopy cover best estimated wheat grain yield. Normalized difference vegetation index and fractional green canopy cover measurements should be conducted prior to Feekes 6 growth stage. Camera height should be adjusted to capture three rows of wheat in the image when using fractional green canopy cover to estimate wheat grain yield. Producers are interested in methods for estimating wheat grain yield at earlier growth stages. Ability to estimate wheat yield early in the growing season could provide producers with the flexibility needed to decide whether to terminate their wheat crop to plant a more profitable alternative crop, such as soybean. The traditional assessment method, counting plants and tillers, is tedious and time consuming. There is a need to create easier methods to estimate wheat yield, such as normalized difference vegetative index (NDVI) or fractional green canopy cover (FGCC). The objective of this study was to compare NDVI, FGCC, and stem count measurements at multiple wheat growth stages to estimate winter wheat grain yield. Trials were established during the 2015–2016 and 2016–2017 growing seasons at two locations in Ohio. Treatment included wheat seeding rates of 0.75, 1.0, 1.5, 2.0, and 2.5 million seeds/acre to mimic a poor to excellent wheat stand. Wheat stand assessment methods of NDVI, FGCC, and stem counts were conducted at Feekes 1 (emergence), Feekes 5 (leaf sheaths strongly erect), and Feekes 6 (first node visible) growth stages. Regression models were used to estimate wheat yield. Normalized difference vegetation index (R2 = 0.49) and FGCC (R2 = 0.45) best estimated wheat grain yield while stem count measurements accounted for 29% of the variability in yield. However, NDVI and FGCC measurements are time sensitive and should be conducted prior to Feekes 6 growth stage, and for FGCC, camera height should be adjusted to capture three rows of wheat in the image.

Onion biomass monitoring using UAV-based RGB imaging

Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination ( $${\text{R}}_{\text{adj}}^2$$ ) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.

Bland-Altman comparison of two methods for assessing severity of Verticillium wilt of potato

The agreement between two disease assessment approaches is important to know prior to replacing or interchanging the use of an established method with a recently developed method of measurement. Frequently used statistical methods to compare two different disease rating methods is the Pearson correlation coefficient or the ordinary least square regression (OLS), but they have their shortcomings. Bland-Altman proposed an alternative method for studying agreement between methods using simple graphs and basic statistics. Traditionally, when disease management strategies are being evaluated in the field, the severity of the disease is estimated using a visual assessment. Canopeo, designed by the Oklahoma State University app center, is a smart phone app designed for measuring green canopy cover. Thus, the aim of this study was to explain the Bland-Altman method with examples of visual and Canopeo methods of wilt measurement. Symptoms of Verticillium wilt in potato were estimated (repeated measures) in two trials using Canopeo and a traditional visual assessment method. Complete wilt data (repeated measures) were considered for studying the agreement between visual and Canopeo assessments. A preset cutoff limit of ≤5% bias (total allowable) between rating methods was considered acceptable prior to using the Bland-Altman comparison. The Bland-Altman method for determining the agreement in wilt severity methods in trial 1 and trial 2 estimated that the mean difference between rating methods were 5.10 and 5.91%, respectively. A mean difference greater than five indicates that the methods of measuring wilt are not in agreement. The study reported here demonstrates that Pearson correlation and OLS regression are inappropriate for assessing the agreement between two methods of measurement.

Can the agricultural AquaCrop model simulate water use and yield of a poplar short-rotation coppice?

We calibrated and evaluated the agricultural model AquaCrop for the simulation of water use and yield of a short‐rotation coppice (SRC) plantation with poplar (Populus) in East Flanders (Belgium) during the second and the third rotation (first 2 years only). Differences in crop development and growth during the course of the rotations were taken into account during the model calibration. Overall, the AquaCrop model showed good performance for the daily simulation of soil water content (R 2 of 0.57–0.85), of green canopy cover (R 2 > 0.87), of evapotranspiration (ET; R 2 > 0.76), and of potential yield. The simulated, total yearly water use of the SRC ranged between 55% and 85% of the water use of a reference grass ecosystem calculated under the same environmental conditions. Crop transpiration was between 67% and 93% of total ET, with lower percentages in the first than in the second year of each rotation. The observed (dry mass) yield ranged from 6.61 to 14.76 Mg ha−1 yr−1. A yield gap of around 30% was observed between the second and the third rotation, as well as between simulated and observed yield during the third rotation. This could possibly be explained by the expansion of the understory (weed) layer; the relative cover of understory weeds was 22% in the third year of the third rotation. The agricultural AquaCrop model simulated total water use and potential yield of the operational SRC in a reliable way. As the plantation was extensively managed, potential effects of irrigation and/or fertilization on ET and on yield were not considered in this study.

The Effect of Treated Waste Water by Binary Irrigation on Growth and Yield of Maize

A field experiment is conducted to determine water requirements for mays crop for the fall season of 2013 in the field of Zafaraniyah Station – Ministry of Agriculture, 30 km south of Baghdad, 44.4˚ longitude, 33.14 latitude and 34 m elevation. The experiment is designed according to RCBD with three replicates and treatments are distributed randomly on experimental plots. The field is divided into experimental units. The experiment consisted five irrigation treatments: T1 (100 wastewater), T2 (75% wastewater +25% river water), T3 (50% waste water + 50% river water), T4 (25% wastewater + 75% river water) and T5 (100% river water). There is a significant increment in plant growth and yield, where plant height, leaf area, weight of 1000 grains, grains yield and grains yield per individual plant increased with increasing mixture ratio, the higher height is in T1 (100% wastewater) of 1.86 m and the lower height is 1.74 m in T5 (100% river water) with increment ratio of 2.29, 4.02, 6.32 and 6.89% for T1, T2, T3 and T4, respectively. Higher leaf area was 0.58 m2 at T1 and the lower is 0.51 m2 at T5 with increment ratio of 3.77, 5.14, 12.58 and 13.30 for T4, T3, T2 and T1, respectively. Grain weight increase with increasing mixture ratio, the higher weight is 265.3 g at T1 and the lower is 235.3 g at T5 with increment ratio of 3.82, 6.12, 10.07 and 12.75% for T1, T2, T3 and T4, respectively. The higher yield is 9.64 tons.h-1 at T1 and the lower is 7.63 tons.h-1 at T5 with increment ratio of 3.80, 9.44, 12.71 and 26.74% for T4, T3, T2 and T1, respectively. Grain yield per individual plant increase with the increasing mixture ratio, where the higher yield was 145.19 g at T1 and the lower was 114.56 g at T5 with increment ratio of 3.81, 9.44, 12.72 and 26.74% for T4, T3, T2 and T1, respectively. Results of the effect of treated wastewater on plant height and leaf area agree with those obtained for green canopy cover percent (CC) through applying AquaCrop program, where the higher CC was 81% at T1 and the lower CC is 77% at T4 (25% treated wastewater + 75% river water) and T5 at planting period.

Canopeo: a new tool to measure green canopy cover

Canopeo: a new tool to measure green canopy cover

Canopeo: a new tool to measure green canopy cover

Canopeo: a new tool to measure green canopy cover

Canopeo: A Powerful New Tool for Measuring Fractional Green Canopy Cover

Fractional green canopy cover (FGCC) is a key diagnostic variable that can be used to estimate canopy development, light interception, and evapotranspiration partitioning. Available image analysis tools for quantifying FGCC are time‐consuming or expensive and cannot analyze video. Our objective was to develop a simple, accurate, and rapid tool to analyze FGCC from images and videos. This tool, called Canopeo, was developed using Matlab and is based on color ratios of red to green (R/G) and blue to green (B/G) and an excess green index (2G–R–B). The output from this tool was compared to that from two software packages widely used to analyze FGCC, SamplePoint, and SigmaScan Pro. Canopeo's image processing speed was 20 to 130 times faster than SigmaScan and 75 to 2500 times faster than SamplePoint. Canopeo correctly classified 90% of pixels when compared to SamplePoint. Root mean squared difference (RMSD) values for Canopeo FGCC vs. FGCC determined by SamplePoint and SigmaScan ranged from 0.04 to 0.12, with an average RMSD of 0.073 across several sets of images of corn (Zea mays L.), forage sorghum [Sorghum bicolor (L.) Moench], bermuda grass [Cynodon dactylon (L.) Pers.], and switchgrass (Panicum virgatum L.). Analysis of video recordings of transects over crop canopies proved to be useful to minimize sampling error and to quantify FGCC spatial variability. This analysis was simple and rapid with Canopeo but not possible with SamplePoint or SigmaScan. The Canopeo app for Matlab and for iOS and Android mobile devices can be downloaded at www.canopeoapp.com.

Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran

The AquaCrop simulation model which has been developed by Food and Agricultural Organization (FAO) has the ability to assess the crop production under different irrigation water management. In this research, 2-year data of rainfed barley (2005–2007) from a research project at upstream of Karkheh river basin (Lorestan Province) were used to evaluate the accuracy of the AquaCrop model. The experimental treatments included: rainfed, one irrigation event at sowing time and one irrigation event at spring, that were performed in farmers’ fields. The AquaCrop model was evaluated to predict the effect of deficit irrigation and rainfed conditions on yield, soil water content and percentage of green canopy cover. The mean normalized mean root square error for comparison between the measured and predicted values for canopy cover percentage, soil water content, and grain yield were 8.7, 12.4 and 9.2%, respectively, that showed good model accuracy. Efficiency of the model in yield estimation, soil water content and green canopy cover percentage were 0.91, 0.8 and 0.98, respectively. Agreement index for yield was close to 1.0 that showed compatibility of these predicted values with actual values. The results showed that the AquaCrop model is appropriate tool to simulate barley yield under rainfed and deficit irrigation conditions in the study area. This model is a suitable tool to determine sowing date in rainfed conditions based on first effective rain.

Impact of water deficit on light interception, radiation use efficiency and leaf area index in a potato crop (Solanum tuberosumL.)

SUMMARYThe aim of the current research was to analyse the effect of four water irrigation treatments (1·20, 1·00, 0·80 and 0·60 of the crop water requirement) on the relationships among leaf area index (LAI), radiation use efficiency (RUE) and green canopy cover in a potato crop (Solanum tuberosumL.) cv. Agria. The crop was established in a commercial plot irrigated with a centre pivot system in Southeast Spain during the 2011 and 2012 seasons. In both seasons, the highest light absorption efficiency values occurred at the LAI value of 3 that corresponded to maximum ground cover. With regard to the irrigation treatment, a significant linear response was indicated for RUE. The results indicate that the 1·00 irrigation treatment produced the best result, improving water resources management without reducing crop yield.

Relative Evaporative Losses and Water Balance in Subsurface Drip and Center Pivot–Irrigated Soybean Fields

This research evaluated the relative evaporative losses and water balance components in two soybean [Glycine max (L.) Merr.] fields under subsurface drip irrigation (SDI) and center pivot irrigation (CPI) systems in south-central Nebraska. Meteorological and surface energy balance components, including actual evapotranspiration (ET), above the crop canopy was measured using Bowen ratio energy balance systems installed at the center of both fields. Crop transpiration (T) was estimated based on the variable stomatal resistances using the Penman-Monteith equation in conjunction with fractional green canopy cover. Evaporation (E) losses were estimated as the difference between measured ET and estimated T. Average soil water content (ASWC) in the crop root zone and effective rainfall were estimated using the water balance method. The relative evapotranspiration (ETrel) was 99.8% for the SDI field in both years (2007 and 2008), and it was 103.4% in 2008 and 93.9% in 2010 for the CPI field. The mean ETrel between sites and across years were not significantly different (P>0.05). There were revealing differences in the relative contribution of T and E to total seasonal ET between the SDI and CPI fields. In the SDI field, T and E were 88 and 12% of ET, respectively, in 2007, and in 2008 they were 84 and 16% of ET, respectively. In the CPI field, T and E were 78 and 22% of ET, respectively, in 2008, and in 2010 they were 75 and 25% of ET, respectively. At full canopy cover, T contributed more than 85% of the total ET. Although E is assumed negligible at the full canopy cover stages, the data indicated that E was 6 and 9% of ET in SDI fields during the midseason growth stage at full canopy cover in 2007 and 2008, respectively, and 14 and 21% of ET in CPI fields during the same growth stage in 2008 and 2010, respectively. On a 2-year average, the CPI field had approximately 10% higher E losses as compared with the SDI field under these experimental conditions, which can have a large impact on crop water productivity when crop production under water-limiting conditions is considered. The reduction in E in the SDI field can be expected to be greater in arid and semiarid regions than in subhumid regions.

Increasing water use efficiency of irrigated sugarcane production in South Africa through better agronomic practices

In South Africa (SA) approximately 30% of sugarcane is grown under irrigation and there is increasing pressure to demonstrate efficient use of limited water resources. Agronomic practices such as the use of a crop residue layer, changed row spacing, growing suitable varieties and accurate irrigation scheduling could potentially increase water use efficiency (WUE) by saving water and/or increasing yield. The aim of the study was to investigate to what extent WUE of irrigated sugarcane production in SA can be improved by better agronomic practices, and to gain a better understanding of the mechanisms involved in crop response to these factors.An overhead irrigated field experiment was conducted near Komatipoort, South Africa on a shallow, well-drained, sandy clay loam over a four year period (one plant (P) and three ratoon crops (R1, R2 and R3)). Treatments consisted of factorial combinations of variety (N14 and N26), row spacing (single rows spaced at 1.5m and dual rows spaced at 1.8m) and soil surface cover (bare soil and crop residue layer). Measurements included tiller population, interception of photosynthetically active radiation (FIPAR), soil water content, and cane yield at harvest. Crop water use (CWU) was estimated using the water balance approach.This study showed that significant reductions in water use and irrigation requirements, and increases in WUE, are possible by using a crop residue layer to cover the soil. Water savings were largest in P (26% in CWU and 32% in irrigation requirement) but substantial savings were also achieved in R crops (about 15%). It is essential to practice accurate irrigation scheduling to realise these savings, taking into account soil cover and cultivar effects, especially during the period of partial canopy. Although the residue layer caused small reductions in yield in the P, R1 and R2 crops (on average 9%) these were not statistically significant. The combined effect of large CWU reductions and small changes in cane yield resulted in increased WUE (on average 18%).These responses to a residue layer were achieved through a reduced rate of canopy development due to delayed emergence of tillers, causing less green canopy cover and reduced CWU, especially during the period of partial canopy cover when stalk growth has not yet commenced. CWU and FIPAR were affected much less during the subsequent period of stalk growth, thus affecting cane yield minimally, provided irrigation scheduling was adjusted.Variety N14 consistently developed a canopy more rapidly, intercepted more radiation and achieved a higher yield than N26. Row configuration had a significant impact on canopy development, seasonal FIPAR, final stalk population but did not affect cane yield or WUE.The study produced quantitative data for parameterising crop models which will improve their reliability in irrigation management and yield prediction applications.

Performance assessment of the FAO AquaCrop model for soil water, soil evaporation, biomass and yield of soybeans in North China Plain

Four years of soybean experimental data observed at Daxing, North China Plain, were used to assess the ability of the AquaCrop model to predict soybean final biomass and yield. The model was parameterized and calibrated using field data on leaf area index (LAI), available soil water, soil evaporation, biomass and final yield data. The model was assessed using calibrated and default parameters. Data on LAI were used to derive the fraction of ground cover and to calibrate the green canopy cover (CC) curve. An accurate calibration of the CC curve was performed, with low root mean square errors (RMSE<7.3%). Results relative to soil water balance simulations show a high variability of the predictions, thus a bias of the estimation, with R2 ranging 0.22–0.86 and low Nash-Sutcliffe efficiency EF, ranging between −0.47 and 0.82. The estimation errors were relatively high, with RMSE not exceeding 22.9mm. AquaCrop was compared with the soil water balance model SIMDualKc, that has shown better performance with R2≥0.83, EF generally greater than 0.75 and RMSE smaller than 12.5mm. The soil evaporation (Es) simulations were compared with the observations performed using microlysimeters; results for Aquacrop have shown a clear trend for under-estimation of Es, with “goodness-of-fit” results worse than for SIMDualKc (Wei et al., 2015). In general, AquaCrop has shown serious limitations to estimate crop transpiration or soil evaporation, which is likely due to abandoning the FAO dual Kc approach. However, the model performed well relative to biomass and yield predictions, with a yield RMSE of 302kgha−1. Overall, results show the adequacy of AquaCrop for estimating soybean biomass and yield when the model is appropriately parameterized. However, AquaCrop is not appropriate to support irrigation scheduling.

Characterization of Vitis vinifera L. Canopy Using Unmanned Aerial Vehicle-Based Remote Sensing and Photogrammetry Techniques

Leaf area index (LAI), green canopy cover (GCC), and canopy volume (V) are associated with grape vigor, quality, and yield. Thus, analyzing these parameters throughout the growing season may help optimize site-specific management of grape vineyards. Because direct measurements of LAI are destructive, tedious, and not repeatable on the same vine, developing and validating nondestructive methods to estimate LAI are essential. Canopy pattern is characterized by GCC and V, which can be measured using aerial observation. The purpose of this study was to characterize growth parameters, such as LAI, GCC, and V, of irrigated and rainfed <i>Vitis vinifera</i> L. under semiarid conditions on two different vineyards using aerial images from an unmanned aerial vehicle. The relationships between GCC versus LAI and V versus LAI were calculated and validated. Relationships between LAI and the other parameters depend on canopy management, training system, and pruning practices. Relationships between LAI and growing degree days (GDD) and V and GDD were also obtained to determine the canopy structure pattern during the growing season. Exponential polynomial and second-order polynomial models showed the best fit for describing the relationships between GCC and GDD and between V and GDD, respectively, for Airén variety.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain

Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.

Hourly and Daily Single and Basal Evapotranspiration Crop Coefficients as a Function of Growing Degree Days, Days After Emergence, Leaf Area Index, Fractional Green Canopy Cover, and Plant Phenology for Soybean

<abstract><title><italic>Abstract.</italic></title> Hourly evapotranspiration (ET) crop coefficients (K_c) are needed to optimize the effectiveness and efficiency of high-frequency micro- and sprinkler irrigation practices involving the application of water multiple times a day. However, not much is known about the daily and seasonal patterns and magnitudes in hourly K_c values for soybean. In addition, locally developed K_c values are necessary for more robust within-season irrigation management, crop ET estimation, and water balance analyses. Hourly and daily K_c functions were developed for soybean in south-central Nebraska through extensive field research. Actual crop evapotranspiration (ET_a) was measured using a Bowen ratio energy balance system. Daily crop coefficients were calculated as K_c = ET_a/ET_ref, wherein reference (potential) evapotranspiration (ET_ref) was calculated using the Penman-Monteith equation with a fixed canopy resistance for both alfalfa-reference (ET_r) and grass-reference (ET_o) surfaces. The K_c values were derived in two forms: (1) a single (normal or average K_c) K_cr based on ET_r, and K_co based on ET_o; and (2) a basal coefficient (K_cbr) based on ET_r, and K_cbo based on ET_o. The seasonal patterns of variation of K_cr, K_co, K_cbr, and K_cbo were examined on five different temporal base scales: days after emergence (DAE), cumulative growing degree days (GDD), leaf area index (LAI), fractional green canopy groundcover (CC), and plant phenology (V and R stages). The 2007 and 2008 growing season ET_a totals were 535 and 514 mm, respectively. Extreme hourly K_c values were frequently observed in the early morning and late afternoon hours when ET_a was very low relative to ET_r and ET_o. Daily means of the 10 to 13 hourly values computed for K_cr ranged from 0.25 to 1.06 in 2007 and from 0.15 to 1.02 in 2008, whereas those computed for K_co ranged from 0.39 to 1.37 in 2007 and from 0.22 to 1.29 in 2008. Daily K_cr and K_co values calculated based on daily data ranged from 0.20 to 1.12 and from 0.27 to 1.47, respectively. Comparison of all daily means of hourly coefficients with the corresponding daily coefficients in one-to-one graphs and zero-origin based regression of the former on the latter revealed linear regression coefficients of 0.92 (2007 K_cr), 0.95 (2008 K_cr), 0.96 (2007 K_co), and 0.97 (2008 K_co), with R² values of 0.78 or better. On average, hourly K_c values were about 4% to 8% lower that the corresponding daily values. Substantial diurnal variability was observed in K_co and K_cr measured during daylight hours (ranging from 0.1-0.2 to 1.5-1.6) from early morning to late afternoon (8:00 to 18:00), and the range of variability was substantially dependent on the coincident V and R stages. The relationship between K_c and LAI was best represented by two regression trend lines: one representing crop development from its beginning up to the start of senescence, and the other representing crop development thereafter. A similar break in the regression trend line was observed in the relationship between basal K_c and GDD. In contrast, the relationship between K_c and fractional CC was not biphasic and could be modeled with one regression trend line. The FAO-56 tabulated K_co values and those measured in this research were significantly different (p < 0.05). Thus, the FAO-56 values, if used for south-central Nebraska soil, climate, and management practices or similar conditions, would not be able to provide accurate ET_a and crop water requirement estimates. Because this research proved that K_co and K_cr values are not constant during the day from dawn to dusk, using daily average K_co or K_cr values would not be able to provide robust and precise determination of crop irrigation requirements for irrigation practices delivered more than once per day. The crop coefficients developed in this research as a function of several base scales should provide crop consultants, extension service personnel, agronomists, irrigation practitioners, and other irrigation and water management professionals with robust and accurate methods for choosing and applying crop coefficients to be used for more precise determination of ET_a and water requirements, thus leading to more efficient and effective seasonal soybean irrigation management.

Model-Based Evaluation of Maturity Type of Potato Using a Diverse Set of Standard Cultivars and a Segregating Diploid Population

The objective of this paper is to evaluate the performance of the conventional system of classifying maturity type in potato and to provide a concept of maturity type based on crop physiology. We present an approach in which physiological traits are used to quantify and assess maturity type unambiguously for a set of varieties covering a wide range of maturity classes and a diploid F1 population separating for maturity and well-adapted to Dutch growing conditions, both grown in six environments. We defined physiological maturity based on four traits: the duration of maximum green canopy, the area under the green canopy cover progress curve, and the rate and duration of tuber bulking. The results indicated that physiological maturity type criteria tended to define maturity classes less ambiguously than the conventional criterion. Moreover, the conventional criterion was subject to more random noise and lacked stability and/or repeatability compared with the physiological traits. The physiological maturity criteria also illustrated the physiological trade-offs that existed between the selected traits and underlined the subtle complexities in classifying maturity type. This study highlighted the capabilities of different maturity type criteria in discriminating between different maturity classes among the large set of genotypes. Our new approach involving key physiological traits could be beneficial in offering physiology-based criteria to re-define maturity type. An improved criterion based on important physiological traits would allow relating the maturity to crop phenology and physiology. These new criteria may be amenable to further genetic analysis and could help in designing strategies for potato ideotype breeding for genotypes with specific maturity types.

Moderate resolution satellite data for mapping salt marshes

Biophysical characteristics of salt marshes are primary indicators of their photosynthetic capacity, nitrogen content, and physiological status. These include the four main characteristics of canopy chlorophyll content: green leaf area index (ratio of green foliage area to ground area), green vegetation fraction (percent green canopy cover), green biomass (which also act as proxies for estimating net primary productivity —NPP), and carbon sequestration potential. Monitoring these characteristics through remotely sensed data can help infer the overall condition and productivity of these valuable natural resources on a larger scale. This would allow effective management strategies in high priority areas. Our research aims to enhance the marsh monitoring practices by including biophysical characteristics (the true early indicators of marsh health status) derived accurately from information such as NASA’s moderate resolution imaging spectroradiometer (MODIS) 250m and 500m data. Our objectives were to calibrate and test a suite of MODISbased vegetation indices (VIs) and develop prototype weekly composites of the salt marsh biophysical characteristics for the entire US Gulf Coast from 2000 to 2010. We then planned to perform a time-series analysis to study the overall trend of salt marsh productivity during the last decade. These VIs (normal difference, wide dynamic range, enhanced, soil-adjusted, etc.) have been widely used and tested for monitoring terrestrial vegetation, but not for salt marsh ecosystems. Our VI calibration and tuning came from establishing relationships between MODIS 250m or 500m data and field data in order to predict the four marsh biophysical characteristics mentioned above. We used atmospherically corrected MODIS 8-day surface reflectance products (250m and 500m), and in situ measurements of biophysical data from 182 locations covering four states (LA, MS, AL, FL). Preliminary analysis revealed that the accuracy of the MODIS-based salt marsh VI calibration depends primarily on three factors: the accuracy of the field data collection, the Figure 1. (a) The dual-headed OceanOptics sensor mounted on a 5m (16ft) high frame. (b, c) Instantaneous field of view (spatial resolution) of the sensor (diameter: 2.2m). (d) In situ sensor calibration.