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Spatial Analysis of Cranberry Yield at Three Scales
Agronomy Journal
Abstract
of soil properties and crop characteristics and any possi- ble changes of those spatial patterns over time. Cranberry (Vaccinium macrocarpon Ait.) is an intensively man- Preliminary studies of the spatial variability in cran- aged perennial crop. Patches of disease, local variation in soil proper- berry yield indicate that factors influencing variability ties, and regional changes in soil type and hydrology cause its yield differ with scale. At the small scale, those factors are to vary spatially at several scales. We evaluated the spatial variability
... Cranberry beds can remain in production for up to 100 years (Pozdnyakova et al. 2005) and the temporal stability of yield patterns of such perennial crops is desirable for precision farming as management zones should be stable (Bramley and Lamb 2003). Understanding temporal patterns in yield is thus primarily involved with determining the influence of seasons with different weather. ...
... Cranberry quality and yield can vary greatly spatially, with variations up to 200-fold in one bed . It is difficult to identify spatial differences in yield within healthy fields using imagery alone (Kerry et al. 2016) and any such attempt needs verification with ground truth in the form of expensive pre-harvest berry counts, which can be inaccurate if berry weight or quality are very variable or the crop is damaged between count and harvest (Pozdnyakova et al. 2005). However, Kerry et al. (2016) showed that area to point kriging (Kyriakidis 2004) using per field yield values and field size and shape over the whole growing region can indicate broad patterns of within-field variation in yield that are related to yield determining factors when the field boundaries have been historically determined in relation to topography, drainage and soil variations (see Fig. 1e). ...
... Regional yield patterns can also be impacted by other factors. Controlling the water table depth is critical for good cranberry growth and farmers have a greater ability to do so when the area:perimeter (A:P) ratio of beds is small (Pozdnyakova et al. 2005). Biennial bearing, has long been thought to occur in cranberry Strik et al. 1991) and could influence yield patterns but some (Roper et al. 1992;Roper and Klueh 1994) suggest that new growth is more important. ...
Cranberries are grown in sensitive wetland ecosystems and precision farming could be beneficial to reduce agro-chemical pollution and increase production without expanding area. Precision farming requires knowledge of the variation of yield within-fields but cranberry harvesting methods produce only one yield value per field unless an expensive pre-harvest berry count is done. Co-operatives and extension services have an important role in precision farming to: (1) determine important factors affecting yield patterns within a growing region and (2) identify fields that would benefit most from future intensive survey. This paper reports a study to investigate temporal and spatial patterns in useable and poor quality cranberry yield for the New Jersey (NJ), USA growing region. Principal components analysis indicated that mean growing season temperature is important for understanding temporal patterns in useable yield and maximum temperatures and precipitation for poor quality yield. Multiple linear regression showed that some cultivars were susceptible to disease and poor quality yield in years with high maximum growing season temperatures. Analysis of spatial patterns using area to area and area to point kriging, local cluster analysis and geographically weighted regression helped identify clusters of fields that were consistently yielding or alternated between high and low yielding. They also showed differences between owners and soil types particularly in hot or wet years showing the different response to soil types to weather and the potential for improvement in irrigation practices by some owners. The methods used should be useful for other growing regions and crops, particularly where there are no yield monitors.
... Thus, spatial correlation was assessed omnidirectionally due to there were no sufficient sampling points in the three watersheds to consider the analysis directionally. The standardization was achieved by dividing the semivariance data by the sample variance what allowed a fair comparison among field sites (Pozdnyakova et al., 2005). The half of the maximum sampling distance in each field site was considered as the lag distance in all semivariograms, being including N 50 pairs per each lag distance class interval in the calculations. ...
... This was done by selecting and fitting the spherical model because the lowest residual sum of squares and highest R 2 were obtained in all cases (e.g., Cambardella et al., 1994;Davidson and Csillag, 2003;Gallardo and Paramá, 2007;Wei et al., 2008). The spherical model is defined in Eq. (3) (Liu et al., 2004;Pozdnyakova et al., 2005): ...
Soil mapping has been considered as an important factor in the widening of Soil Science and giving response to many different environmental questions. Geostatistical techniques, through kriging and co-kriging techniques, have made possible to improve the understanding of eco-geomorphologic variables, e.g., soil moisture. This study is focused on mapping of topsoil moisture using geostatistical techniques under different Mediterranean climatic conditions (humid, dry and semiarid) in three small watersheds and considering topography and soil properties as key factors. A Digital Elevation Model (DEM) with a resolution of 1×1m was derived from a topographical survey as well as soils were sampled to analyzed soil properties controlling topsoil moisture, which was measured during 4-years. Afterwards, some topography attributes were derived from the DEM, the soil properties analyzed in laboratory, and the topsoil moisture was modeled for the entire watersheds applying three geostatistical techniques: i) ordinary kriging; ii) co-kriging considering as co-variate topography attributes; and iii) co-kriging ta considering as co-variates topography attributes and gravel content. The results indicated topsoil moisture was more accurately mapped in the dry and semiarid watersheds when co-kriging procedure was performed. The study is a contribution to improve the efficiency and accuracy of studies about the Mediterranean eco-geomorphologic system and soil hydrology in field conditions.
... They found that yield was strongly spatially clustered, suggesting possible management by zones. Pozdnyakova et al. (2005) analysed spatial variability of yield in a cranberry plantation. They used 0.3 x 0.3 m frames to measure the number of fruits before harvesting. ...
... Aggelopoulou et al., 2011; Fountas et al., 2011; Konopatzki et al., 2015; Vatsanidou et al., 2015 Palm / Plum / Pear / Cranberry Numbering each tree before harvest and measuring the mass of fruits picked manually. Topographic model or local referencing Mazloumzadeh et al., 2010; Perry et al., 2010; Pozdnyakova et al., 2005; Käthner and Zude-Sasse, 2015 Peaches / Kiwis RFID or barcodes on the bins together with a weighing machine, RFID or barcode reader and DGPS. Ampatzidis et al., 2009; Meena et al., 2015; Taylor et al., 2007 Potatoes Load cells under the conveying chains. ...
Farmer and consumer are driving the request for sustainable production of fruit and vegetables. Precision agriculture, the consideration of spatial and temporal variability for increasing the efficiency of resources, has been developed over the last twentyfive years and was initially applied to field crops. Its application to tree crops and vegetables started later and has been developing with an increasing number of publications as well as research calls in the beginning of the 21st century. First applications were described for mechanical harvesting of horticultural crops with commercial solutions for harvesting fruit that is subjected to processing. A review of methodical approaches and upcoming challenges for precise management of tree crops and vegetables are covered in this paper, addressing horticulturists as well as researchers working in precision agriculture. The precision agriculture domains with specific implications in horticultural crops captured are: data collection, yield mapping, remote sensing, quality mapping, and variable rate application. The spatial and temporal variability in orchards as well as effects of site-specific application of inputs are documented in this paper.
... They found that yield was strongly spatially clustered, suggesting possible management by zones. Pozdnyakova et al. (2005) analysed spatial variability of yield in a cranberry plantation. They used 0.3 x 0.3 m frames to measure the number of fruits before harvesting. ...
... Numbering each tree before harvest and measuring the mass of fruits picked manually. Topographic model or local referencing Mazloumzadeh et al., 2010;Perry et al., 2010;Pozdnyakova et al., 2005;Käthner and Zude-Sasse, 2015 Peaches / Kiwis RFID or barcodes on the bins together with a weighing machine, RFID or barcode reader and DGPS. A yield monitor combining harvester and digital camera system was approached in blueberries (Zaman et al., 2008) by counting blue pixels in the images. ...
Particularly, high value tree crops with a life-Time of 6 up to 40 years such as apples and citrus have high potential to benefit from automated sensors, information systems, and robotics. At present many technologies are applied on a research level, while only few methods derived were evaluated considering their feasibility. Even less methods are further adapted to approach robust data mining and information for the process management in the production and postharvest. The present work summarizes established and recent developments and, exemplarily, points out a solution for robust calibrations in non-destructive optical pigment analyses. The conclusion is that it is a long but reasonable way to get sensors in practise.
... However, Bramley and Lamb (2003) note greater temporal stability in yield variation of perennial crops like grapes which is desirable for precision farming. Cranberry beds can remain in production for as long as 100 years (Pozdnyakova et al. 2005) and patterns in soils and topography which favour fruit rot can introduce genotypic variation into the crop (Novy et al. 1996). If seeds dropped from rotting berries germinate and are pollinated with pollen from a different cultivar, genotypic diversity of the cranberry bog is increased and the consistency of yield quantity and quality is decreased (Oudemans et al. 2008). ...
... Fruit are skimmed from the surface and loaded into barrels to give just one yield value per field. The only possibility of assessing within-field variation of yield (''ground truth'') is to conduct pre-harvest berry counts, which are time-consuming and can be inaccurate if berry weights vary markedly within a bed or there is severe weather that damages the crop between the berry count and harvest (Pozdnyakova et al. 2005). ...
The method of harvesting cranberries gives just one yield value per field so characterizing within-field variation is difficult. Geostatistical disaggregation of per field yield totals using the enhanced vegetation index (EVI) from imagery as secondary information was investigated. Results were compared to within-field yield variability projected from time-consuming pre-harvest berry counts. Several scales of variation were present in the data so factorial kriging was used to separate these and determine which are most spatially coherent. The trend component of pre-harvest berry counts and/or geostatistical disaggregation of yields both give a reasonable initial definition of potential management zones likely to be related to topography and soil type differences.
... Geo-referenced soil sampling and laboratory analysis permit the quantification of the variability of soil properties (Adamchuk et al. 2007) and together with interpolation methods are used to describe their spatial variation (Pozdnyakova et al. 2005). These techniques are mathematical formulations of the variation of soil properties that minimizes the prediction error for the observed variables and provides confidence in predictions for the un-sampled locations (Corstanje et al. 2006). ...
... Others have found that water and nutrient holding capacity of soils can influence the yield of the vines (van Leeuwen et al. 2004;Bodin and Morlat 2006a;Reynolds et al. 2007), but for a given season the effect of climate is more important to determine the yield than the soil characteristics (van Leeuwen et al. 2004;Girona et al. 2006). Grapevines, such as the ones of this study that were planted 20 years ago, might develop persistent spatial variability in yield in response to edaphic factors as reported for other perennial crops (Pozdnyakova et al. 2005). Soil characteristics and the vegetative development of the vines have an influence on berry composition (Johnson et al. 2003;Reynolds et al. 2007;Andreas-de Prado et al. 2007) and this study also found significant relationships among yield components that have an influence on berry composition (Musingo et al. 2005;Pilar et al. 2007), but this is beyond the scope of this study and will be addressed in another report. ...
To obtain the best must quality, winegrowers must harvest uniform batches of grapes, thus they might define sub-units of the
vineyard and treat them as separate management units for cultivation and harvest. The objectives of this work were to determine
if there were variations of soil properties that could be arranged into different units of relative uniformity and separated
from each other by discrete boundaries, and if there was a significant relationship between those units and the vegetative
development and yield components of the grapevines. A soil index that is a linear combination of four soil characteristics
was constructed and an interpolation method allowed the definition of soil areas with relative uniformity. These areas were
significantly correlated with the vine growth that, in turn, had a significant correlation with the yield components of the
vines. This methodology might prove useful to define areas within vineyards where the vegetative development and yields warrant
a differentiated management within the vineyard.
Keywords
Vitis vinifera
–Soil index–Natural neighbor interpolation–Yield components–Precision viticulture
... Cranberry (Vaccinium macrocarpon Ait.) is a high value intensively managed perennial crop that grows on wetlands. Given the crop characteristics and strict federal guidelines that prohibit the expansion of cranberry acreage on wetlands, improving profitability of cranberry production is most likely to be achieved by precision management (Pozdnyakova et al. , 2005. Perennial crops like cranberry seem ideal for precision management as they often develop patterns of yield variability that are relatively stable in time in response to spatial variation in disease and soil properties. ...
... Intense ground surveys for individual fields are not, however, a practical or economic way forward for characterizing cranberry yield within fields. Pozdnyakova et al. (2005) conducted a spatial analysis of cranberry yield at three scales but noted that differences in sampling support, which were not explicitly taken into account, affected the yield distribution statistics more than the spatial scale at which measurements were made. ...
Cranberries are harvested by flooding the field and agitating vines so the fruit, which float can be skimmed from the surface
and loaded into barrels. This harvesting method makes application of standard precision farming practices difficult. This
paper investigates the potential of combining Area-to-Area (AtoA) and Area-to-Point (AtoP) kriging of yield totals from individual
fields with remotely sensed data for defining within-field management zones.
... When number of samples is limited an ommnidirectional (isotropic) characterization of spatial dependence is more recommendable (Davidson and Csillag, 2003). The standardization was achieved by dividing the semivariance data by the sample variance, and this allowed a fair comparison among variables and sites (Pozdnyakova et al., 2005). The half of the maximum sampling distance in each village was chosen as the active lag distance for the construction of all semivariograms, and more than 100 pairs per each lag distance class interval were included in the calculations. ...
... Having the same model further facilitates comparisons among variables and villages (Cambardella et al., 1994;Davidson and Csillag, 2003;Gallardo and Paramá, 2007). The spherical model is defined in Eq. (3) (Liu et al., 2004;Pozdnyakova et al., 2005) as: where γ is the semivariance, h the distance, Co is the nugget, Co + C is the sill, and a is the range. These parameters were used to describe and compare spatial structures of soil properties in each village. ...
Knowledge of soil spatial variability is important in natural resource management, interpolation and soil sampling design, but requires a considerable amount of geo-referenced data. In this study, mid-infrared spectroscopy in combination with spatial analyses tools is being proposed to facilitate landscape evaluation and monitoring. Mid-infrared spectroscopy (MIRS) and geostatistics were integrated for evaluating soil spatial structures of three land settlement schemes in Zimbabwe (i.e. communal area, old resettlement and new resettlement; on loamy-sand, sandy-loam and clay soils, respectively). A nested non-aligned design with hierarchical grids of 750, 150 and 30 m resulted in 432 sampling points across all three villages (730–1360 ha). At each point, a composite topsoil sample was taken and analyzed by MIRS. Conventional laboratory analyses on 25–38% of the samples were used for the prediction of concentration values on the remaining samples through the application of MIRS–partial least squares regression models. These models were successful (R2 ≥ 0.89) for sand, clay, pH, total C and N, exchangeable Ca, Mg and effective CEC; but not for silt, available P and exchangeable K and Al (R2 ≤ 0.82). Minimum sample sizes required to accurately estimate the mean of each soil property in each village were calculated. With regard to locations, fewer samples were needed in the new resettlement area than in the other two areas (e.g. 66 versus 133–473 samples for estimating soil C at 10% error, respectively); regarding parameters, less samples were needed for estimating pH and sand (i.e. 3–52 versus 27–504 samples for the remaining properties, at same error margin). Spatial analyses of soil properties in each village were assessed by constructing standardized isotropic semivariograms, which were usually well described by spherical models. Spatial autocorrelation of most variables was displayed over ranges of 250–695 m. Nugget-to-sill ratios showed that, in general, spatial dependence of soil properties was: new resettlement > old resettlement > communal area; which was potentially attributed to both intrinsic (e.g. texture) and extrinsic (e.g. management) factors. As a new approach, geostatistical analysis was performed using MIRS data directly, after principal component analyses, where the first three components explained 70% of the overall variability. Semivariograms based on these components showed that spatial dependence per village was similar to overall dependence identified from individual soil properties in each area. In fact, the first component (explaining 49% of variation) related well with all soil properties of reference samples (absolute correlation values of 0.55–0.96). This showed that MIRS data could be directly linked to geostatistics for a broad and quick evaluation of soil spatial variability. It is concluded that integrating MIRS with geostatistical analyses is a cost-effective promising approach, i.e. for soil fertility and carbon sequestration assessments, mapping and monitoring at landscape level.
... They use a SW normality test in a previous step of the analysis. Other references about the use of univariate normality tests with spatially distributed data are [14], [15], [16], [17] and [18]. These are just a few examples on the use of classical normality tests with samples from spatial processes. ...
En geoestadística, bajo estacionariedad, kriging simple (KS) es el mejor predictor lineal (MPL) y kriging ordinario (KO) es el mejor predictor lineal insesgado (MPLI). Cuando el proceso estocástico es Normal, KS no es solo un MPL sino un mejor predictor (MP), es decir que bajo la función de pe ́rdida cuadrática, éste coincide con la esperanza condicional del predictor dada la información. En este escenario, el predictor KO sirve como aproximación del MP. Por esta razón, en geoestadística aplicada, es importante probar el supuesto de normalidad. Dada una realización de un proceso espacial, KS será un predictor óptimo si el vector aleatorio subyacente sigue una distribución normal multivariada. Algunas pruebas de normalidad clásicas como Shapiro-Wilk (SW), Shapiro-Francia (SF), o Anderson-Darling (AD) son usadas para evaluar este supuesto. Estas asumen independencia y por ello no son apropiadas en geoestadística (y en general en estadística espacial). Por un lado, las observaciones en geoestadística son espacialmente correlacionadas. Por otro lado la optimalidad del kriging es fundamentada en normalidad multivariada (no en normalidad univariada). En este trabajo se presenta un estudio de simulación para mostrar por qué es inapropiado el uso de pruebas univaridas de normalidad con datos geoestadísticos. También, como solución al problema anterior, se propone una adaptación de la prueba de Mahalanobis al contexto geoestadístico para hacer de manera correcta el test de normalidad en este ambito.
... Georeferenced yield monitoring for manually harvested berries and manually-harvested vegetables during harvest is limited, probably as the measurement of yields from single plants or defined number of plants is not feasible. Typically, yield from fixed areas are used, for example Pozdnyakova et al., (2005) measured the yield in a cranberry field using a 0.3 m x 0.3 m frame while Akdemir et al., (2005) used a 10 m x 10 m frame for dry onions. Fountas et al., (2015) measured the yield of watermelons by weighing the crop from fixed blocks within a field. ...
Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems.
... the previous experimental procedure highlighted the spatial variability on the olive field. Pozdnyakova et al. (2005) analysed spatial variability of yield in a cranberry plantation. They used 0.3 x 0.3 m frames to measure the number of fruits before harvesting. ...
... They found that the crop was highly geographically integrated, suggesting management as possible geographically. Pozdnyakova et al. (2005) analyzed crop yield variability in cranberry growing areas. They used 0.3 x 0.3 m frames to measure the amount of fruit before harvesting. ...
... They found that the crop was highly geographically integrated, suggesting management as possible geographically. Pozdnyakova et al. (2005) analyzed crop yield variability in cranberry growing areas. They used 0.3 x 0.3 m frames to measure the amount of fruit before harvesting. ...
In the present age, the agrarian community has been pursuing various types of subsidiary occupations apart from their primary agricultural activity. Thus, the subsidiary occupation plays a very significant role in transformation of the agrarian structure. So this study is taken up to examine the subsidiary occupations content in people at the farms in Punjab state with following objectives i) to highlight the type of subsidiary occupations adopted by the farmers ii) to study attitude of the respondents while pursuing subsidiary occupations. The study was conducted in Abohar district of Punjab. From the district one block and further two villages namely, Gobindgarh and Bangala were selected. Total sample of 100 farmers who have adopted subsidiary occupations were taken up. The results revealed that, the respondents were young, having income up to 5 lac , educated upto 10+2 and owned 5-10 acres of land. Study revealed that major profitable subsidiary occupation remained dairy farming because milk is the part of our daily diet. The utilization of debt was also seen and the data showed that 24.00 percent of the respondents repaid their old debts, whereas 20.00 percent of the respondents used money for expanding the business and same number invested money in taking health facilities. The apps which are in phones such as YouTube, WhatsApp and Face book were playing a positive role to get information on subsidiary occupations and widely used by the respondents. . 88.00 percent stated that subsidiary occupations are need of an hour and every farmers had to take shift from traditional occupation to subsidiary occupations 68.00 percent stated that subsidiary occupation gave a positive image to a farmer and same number perceived it as more profitable than agriculture. Overall, the respondents found these occupations as need of an hour and stated that every farmer must have these occupations along with primary occupation.
... Some recent works on agricultural soils have studied the application of these modern methods to cases in which a measure along a transect is observed as a random signal. For instance, Pozdnyakova et al. (2005) evaluated the spatial variability of cranberry yield by applying a generalized structure function (GSF) and proved the influence of multiscale factors (nonlinear structure functions). Kravchenko (2008) approached the spatial features of environmental and agronomic variables using multifractal characteristics in a stochastic simulation. ...
... Some recent works on agricultural soils have studied the application of these modern methods to cases in which a measure along a transect is observed as a random signal. For instance, Pozdnyakova et al. (2005) evaluated the spatial variability of cranberry yield by applying a generalized structure function (GSF) and proved the influence of multiscale factors (nonlinear structure functions). Kravchenko (2008) approached the spatial features of environmental and agronomic variables using multifractal characteristics in a stochastic simulation. ...
... The application of above-mentioned interpolation methods to cranberry yields can provide insights into the spatial relationships between cranberry yields and water table depth (WTD). This is a necessary step for developing a precision agriculture system for cranberries [26]. Thus, the objectives of this study were (1) to compare the aforementioned spatial interpolation methods and to select the one with the best performance, (2) to test the influence of the network density on method performance, and (3) to evaluate the performance of two drainage systems and their impacts on crop productivity as a result of rainfall events. ...
In this paper we first compare three different methods of spatial interpolation, i.e., inverse distance weighting (IDW), thin plate splines (TPS), and kriging on weekly water table depth (WTD) measurements from 80 observation wells in two cranberry farms (Farm A and Farm B) located in Québec, Canada. We use the leave-one-out cross-validation approach to assess the performance of the methods. Second, we evaluate the influence of the density of measurement points over the interpolation error for the cited methods. Third, we assess the performance of drainage systems and their impacts on crop productivity as a result of cumulative rainfall. Results along with practical considerations show that TPS is the best interpolator for WTD and this superiority is maintained and further demonstrated through a sensitivity analysis of the methods to spatial sampling density, i.e., partitioning the data into subsets of 25, 50, and 75% of the dataset. However, the random approach for selecting these subsets shows an unexpected result; that is, the interpolation methods exhibit a higher performance in terms of the Pearson correlation (r) for the 25% data subset at Farm B. Meanwhile, the cumulative precipitation over a three-day period, the maximum time required to return the soil matric potential to the optimal value after a major rainfall event, had a steady influence on WTD and thus crop productivity in the studied farms. This influence is more apparent for Farm A, but a rather random effect is noted for Farm B. This study presents a water-management-based strategy that mitigates the supplementary cost and effort for sensor deployment in water table monitoring for cranberry production. It is therefore of practical interest to cranberry growers and decision-makers who aim to maximize yields through water-management-oriented strategies.
... While global view of fields have utility in long time-frame growth assessment, high resolution imagery to count individual cranberries can be prohibitively data-intensive over large regions. Toward the goal of real-time crop-monitoring, we adopt the efficient approach of stratified random sampling for representing large scale regions as in [40]. ...
Machine vision for precision agriculture has attracted considerable research interest in recent years. The goal of this paper is to develop an end-to-end cranberry health monitoring system to enable and support real time cranberry over-heating assessment to facilitate informed decisions that may sustain the economic viability of the farm. Toward this goal, we propose two main deep learning-based modules for: 1) cranberry fruit segmentation to delineate the exact fruit regions in the cranberry field image that are exposed to sun, 2) prediction of cloud coverage conditions and sun irradiance to estimate the inner temperature of exposed cranberries. We develop drone-based field data and ground-based sky data collection systems to collect video imagery at multiple time points for use in crop health analysis. Extensive evaluation on the data set shows that it is possible to predict exposed fruit's inner temperature with high accuracy (0.02% MAPE). The sun irradiance prediction error was found to be 8.41-20.36% MAPE in the 5-20 minutes time horizon. With 62.54% mIoU for segmentation and 13.46 MAE for counting accuracies in exposed fruit identification, this system is capable of giving informed feedback to growers to take precautionary action (e.g. irrigation) in identified crop field regions with higher risk of sunburn in the near future. Though this novel system is applied for cranberry health monitoring, it represents a pioneering step forward for efficient farming and is useful in precision agriculture beyond the problem of cranberry overheating.
... Some authors have applied these techniques to soil science to look for statistical self-similar series (Pozdnyakova et al., 2005;Kravchenko et al., 2002;Garcia Moreno et al., 2010;Morató et al., 2017). Likewise, other works (Tarquis et al., 2008;Morató et al., 2017) have studied wider scaling behaviors which cannot be captured in a consistent way by the MFA. ...
Soil spatial variability is a key point for the sustainable water management in agriculture. Fractal techniques provide proper tools to analyze soil spatial variability searching for statistical self-similarity patterns among different scales. Although they have been extensively applied to study the soil properties variability, its applicability for the soil water content (SWC) distribution is complicated because requires many data difficult to obtain with the typical point soil water sensors. Recently, a fiber optic distributed temperature sensor has been used to measure soil thermal properties which relate to SWC. These sensors provide large amount of data with high spatial and temporal resolution, thus filling the gap of point soil water sensors. In the present work, soil temperature was measured with a Distributed Temperature Sensing (DTS) and SWC was estimated by different fitting functions which have been studied with focus on spatial variability. Temperature was measured in a 133 m fiber optic cable laid in a sandy soil field plot. A The Active Heat Fiber Optic (AHFO) method was used, with 12 cm sampling resolution, and heat pulses (19,4 W/m during 2 min) were applied. The temperature data were correlated to SWC, considering the integration of temperature during the heat pulse Tcum, and then the datasets Tcum-SWC were fitted to the best fit statistical function (exponential, potential and polynomial). The results showed that the Tcum distribution presented a non-Gaussian pattern. Additionally, highly anti-persistent patterns have been detected for the larger spatial scaling lags. The function’s performance was different thus, the exponential function reproduced better the absolute moments of the temperature profile but it failed reproducing the non-Gaussian behavior.
... Because BlShV is a new disease problem for the cranberry industry, there was a need to determine impact of the virus on yield. Given the inherent variability in bed-wide cranberry yield (Pozdnyakova et al. 2005), we measured yield components on individual cranberry uprights, as conducted previously (Roper et al. 1993;Wells-Hansen and McManus 2016). For this, we classified uprights as recovered, healthy, and symptomatic based on fruit appearance and TAS-ELISA tests. ...
Blueberry shock virus (BlShV), an Ilarvirus sp. reported only on blueberry, was associated with scarring, disfigurement, and premature reddening of cranberry fruit. BlShV was detected by triple-antibody sandwich enzyme-linked immunosorbent assay and reverse-transcription polymerase chain reaction, and isometric virions of 25 to 28 nm were observed in cranberry sap. The virus was systemic, although unevenly distributed in plants. The coat protein of BlShV from cranberry shared 90% identity compared with BlShV accessions from blueberry on GenBank. Phylogenetic analysis of isolates of BlShV from cranberry collected from Wisconsin and Massachusetts did not indicate grouping by state. BlShV was detected in cranberry pollen, and seed transmission of up to 91% was observed. Artificial inoculation of cranberry flowers by pollination did not cause virus transmission. In some Nicotiana spp., rub inoculation of leaves with homogenized BlShV-positive cranberry flowers resulted in systemic infection. Cranberry plants recovered from symptoms the year after berry scarring occurred but continued to test positive for BlShV. The virus caused significant reduction in the average number of marketable fruit and average berry weight in symptomatic cranberry plants but recovered plants yielded comparably with healthy plants. Although recovery may limit the immediate economic consequences of BlShV, long-term implications of single- or mixed-virus infection in cranberry is unknown.
... Soil water potential monitoring usually consists of a single or a few monitoring stations, which may present logistical challenges to growers as soil conditions and indeed cranberry yields are rarely ever uniform in beds (Pozdnyakova et al. 2005). Since yield heterogeneity within a cranberry bed could vary with SWP, Gumiere et al. (2014) proposed a nonuniform approach to irrigation management, with specific irrigation programs developed for different zones within a single cranberry bed. ...
Recent research funding, as well as technological and management changes, has led to important scientific discoveries on irrigation and drainage of cranberry that could significantly impact on plant yield and water use. This paper integrates all this information into new proposed guidelines for irrigation and drainage management of cranberry. It explains the interaction of the different concepts, with the most recent ones published in this special issue. Cranberry yield is very sensitive to wet anaerobic conditions (soil matric potential >-4 kPa) or dry bed conditions (<-7 kPa) limiting capillary rise. It also appears that important water savings can be achieving by irrigating by a combination of overhead and subirrigation maintaining the top 15 cm of the bed within those soil matric potential limits and to meet an evapotranspiration demand up to 7.5 mm d⁻¹, provide frost and heat protection, and avoid salt accumulation, as this crop also appears sensitive to salinity stress. Finally, following plantings, soil properties appear to evolve dynamically and should be followed through profile observations, and combination of soil water potential and ground penetrating radar data, to identify potential yield limitations.
... However, only recent works on agricultural soils have studied the application of these methods to cases in which a measure along a transect is observed as a random signal. Pozdnyakova et al. (2005) evaluated the spatial variability of cranberry yield by applying a Generalized Structure Function, proving the influence of multiscale factors (nonlinear structure functions). Kravchenko (2008) approached the spatial features of environmental and agronomic variables using multifractal characteristics in a stochastic simulation. ...
The spatial variability of soil properties is a constant expected factor that must be considered in soil studies. This variability is composed of “functional” variations and random fluctuations or noise. Multifractal formalism is suitable for variables with self-similar distributions on a spatial domain. Multifractal analysis can provide insight into the spatial variability of soil parameters. In soil science, it has been quite popular to characterize the scaling property of a variable measured along a transect as a mass distribution of a statistical measure on a length domain of the studied transect. The analysed variable is divided into a number of self-similar segments, and the partition function and mass function are estimated. Based on these estimations, the multifractal spectrum (MFS) is calculated. Another technique that can be applied focuses on the variations of a measure by analysing the absolute differences in the soil property values at different scales, such as the Generalized Structure Function (GSF) and the Universal Multifractal Model (UMM). The aim of this study was to compare both types of multifractal methods on a set of soil physical properties measured on a common 1024 m transect across arable fields at Silsoe in Bedfordshire, East-Central England. The studied properties were total porosity (Porosity), gravimetric water content (GWC) and nitrous oxide flux (N2O flux). The results showed that when using both methods, the N2O flux exhibits a distinctive multifractal character, and weak multifractal characters are detected in the GWC and Porosity cases. Additionally, several parameters were calculated and discussed.
... 이러한 (Dexter, 1988 (Gimenez et al., 1997;Taina et al., 2008). 토양 이미지로부터 공극 구조 다양성 을 정량화 하는 방법엔 여러 가지가 있으나 이 중 배열 엔트 로피 (Configuration entropy)와 프랙탈 (fractal) 분석이 많 이 적용되어왔다 (Chun et al., 2008;Gimenez et al., 1997;Pozdnyakova et al., 2005) (Chun et al., 2008;Gimenez et al., 1997;Kravchenko et al., 2011 Table 1. Summary of physical properties from natural (N_c and N_f) and anthropogenic soil (A_c and A_f) sites at different depths: 1) N_c and N_f -a. ...
Human influence on soil formation has dramatically increased with human civilization and industry development. Increase of anthropogenic soils induced researches on the anthropogenic soils; classification, chemical and physical characteristics of anthropogenic soils and plant growth from anthropogenic soils. However there have been no comprehensive analyses on soil pore or physical properties of anthropogenic soils from 3 dimensional images in Korea. The objectives of this study were to characterize physical properties of anthropogenic paddy field soils by depth and to find differences between natural and anthropogenic paddy field soils. Soil samples were taken from two anthropogenic and natural paddy field soils; anthropogenic (A_c) and natural (N_c) paddy soils with topsoil of coarse texture and anthropogenic (A_f) and natural (N_f) paddy soils with topsoil of fine texture. The anthropogenic paddy fields were reestablished during the Arable Land Remodeling Project from 2011 to 2012 and continued rice farming after the project. Natural paddy fields had no artificial changes or disturbance in soil layers up to 1m depth. Samples were taken at three different depths and analyzed for routine physical properties (texture, bulk density, etc.) and pore properties with computer tomography (CT) scans. The CT scan provided 3 dimensional images at resolution of 0.01 mm to calculate pore radius size, length, and tortuosity of soil pores. Fractal and configuration entropy analyses were applied to quantify pore structure and analyze spatial distribution of pores within soil images. The results of measured physical properties showed no clear trend or significant differences across depths or sites from all samples, except the properties from topsoils. The results of pore morphology and spatial distribution analyses provided detailed information of pores affected by human influences. Pore length and size showed significant decrease in anthropogenic soils. Especially, pores of A_c had great decrease in length compared to N_c. Fractal and entropy analyses showed clear changes of pore distributions across sites. The topsoil layer of A_c showed more degradation of pore structure than that of N_c, while pores of A_f topsoil did not show significant degradation compared with those of N_f. These results concluded that anthropogenic soils with coarse texture may have more effects on pore properties than ones with fine texture. The reestablished paddy fields may need more fundamental remediation to improve physical conditions.
... Soil chemical properties are important indicators for soil quality, soil fertility, and soil health. However, soil chemical properties are expensive to measure or predict continuously over a region because they are highly heterogeneous across spatial scales (Pozdnyakova et al., 2005). Traditional measurement of soil chemical properties is time consuming and expensive (Bellon-Maurel and McBratney, 2011) and produces harmful waste (Ryu et al., 2001). ...
This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.
... In the last few years, apart from the grain production, precision farming method has been also available in other crops, especially in horticultural crops. However, high value horticultural crops have also been investigated such as citrus (Zaman, Schuman, 2006;Ye et al., 2007;Aggelopoulou et al., 2010), olive (Lopez-Granados et al., 2004;Aggelopoulou et al., 2010), apples (Best et al., 2008;Aggelopoulou et al., 2010), grapes (Bramley, Hamilton, 2004;Taylor, 2004;Bramley, 2005;Aggelopoulou et al., 2010), cranberries (Pozdnyakova et al., 2005;Aggelopoulou et al., 2010) and tomatoes (Pelletier, Upadhyaya, 1999;Aggelopoulou et al., 2010). It is often claimed that precision agriculture can offer a great deal to the production of high value crops, and it is also easier to pay for the investment than for lower value crops. ...
Precision horticulture and spatial analysis applied to orchards are a growing and evolving part of precision agriculture technology. The aim of this study was to produce yield and pomological properties maps in order to determine the relationship between these parameters and apparent soil electrical conductivity (EC a) characteristics in different apple varieties, as well as analyze spatial variation in an apple orchard. This study was carried out at an apple orchard in the Faculty of Agriculture of Ankara University's Haymana Research Station. In this work, apparent soil electrical conductivity values were acquired by using EM38 sensor. These values were used to produce maps and compared with yield and pomological characteristic maps using classical statistics and spatial analyst methods. As a result, the highest value of non-linear regression between EC a and yield was determined in the 'Red Chief' (R 2 = 0.94) while the highest calculated value for yield in cross-sectional area were found to be in 'Jonagold' with R 2 = 0.44. The other indexes such as coefficient of variation and Moran's I index were used to determine variability and autocorrelation in each value.
... These studies have sometimes linked imagery methods to ground-based measurements. Other studies have mainly focused on spatial variation of soil pH (Davenport et al., 2003) or yield estimation from field sampling units (Pozdnyakova, Gim enez, & Oudemans, 2005). However, either using remote sensing method or ground-based variation studies, cranberry yield has been shown to be spatially variable within and between fields . ...
Spatial interpolation methods are required for analysing the effects of soil hydraulic properties on irrigation management. This study was conducted to determine which interpolation methods are best suited to map these properties. During the summer of 2012 we mapped the spatial variability of soil physical properties, soil matric potential, water table depth and yield of two cranberry fields located near Quebec City, Canada. Three spatial interpolation methods, inverse distance weighting (IDW), thin plate splines (TPS) and kriging with external drift (KED), were compared by means of cross-validation. The best interpolation method for a given property was used to produce maps and perform HYDRUS 1D simulations for the purpose of irrigation management. Results show that even in highly constructed fields, such as for cranberries, spatial patterns of soil hydraulic properties exist. The TPS method was the best interpolation method based on the cross-validation analyses and generated maps. Spatial variability of crop yield showed a strong relationship with soil hydraulic properties and simulations suggest that irrigation can be reduced by 75% when accounting for the spatial variability of soil hydraulic properties.
... In addition, soil OM promotes good soil structure and soil health. However, soil OM is highly variable across scales, and it is difficult to measure or predict continuously over a region (Pozdnyakova et al., 2005). Traditional measurement of soil carbon based on chemical oxidation or dry combustion is time consuming and expensive (Bellon-Maurel and McBratney, 2011). ...
This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.
... This analysis measures spatial dependence in a range of moments and provides parameters which characterize scaling behavior (Tennekoon et al., 2003). This method has been applied to a spatial distribution of soil hydraulic conductivity (Tennekoon et al., 2003) and spatial distribution of crop yields (Kravchenko, 2008;Pozdnyakova et al., 2005), but not soil structure from images. ...
Soil structure plays an important role in ecological system, since it controls transport and storage of air, gas, nutrients and solutions. The study of soil structure requires an understanding of the interrelations and interactions between the diverse soil components at various levels of organization. Investigations of the spatial distribution of pore/particle arrangements and the geometry of soil pore space can provide important information regarding ecological or crop system. Because of conveniences in image analyses and accuracy, these investigations have been thrived for a long time. Image analyses from soil sections through impregnated blocks of undisturbed soil (2 dimensional image analyses) or from 3 dimensional scanned soils by computer tomography allow quantitative assessment of the pore space. Image analysis techniques can be used to classify pore types and quantify pore structure without inaccurate or hard labor in laboratory. In this paper, the last 50 years of the soil image analyses have been presented and measurements on various soil scales were introduced, as well. In addition to history of image analyses, a couple of examples for soil image analyses were displayed. The discussion was made on the applications of image analyses and techniques to quantify pore/soil structure.
... Georeferenced soil sampling and laboratory analysis permit us to quantify the variability of soil properties (Adamchuk et al., 2007) and, together with interpolation methods, are used to describe their spatial variation (Pozdnyakova et al., 2005). These techniques are mathematical formulations of the variation of soil properties that serve to minimize prediction error for the observed variables and provide confidence in predictions for the un-sampled locations (Corstanje et al., 2006). ...
An experiment was carried out in order to study the spatial variability of soil fertility variables in
an irrigated mature olive tree (Olea europea cv. ‘Zard’) orchard. The orchard is located in the
Tarom area of Zanjan Province (48° 56′ to 50° 5′ E and 36° 47′ to 37° 36′ N) a n d i s under olive
with trees planted 7×7 m. Soil parameters - including K, P, Na, Cl, EC and OM - were determined in soil samples from 0-60 cm depth in late February 2011. A regular 98×98 m sampling grid was established and the intersection points were georeferenced. The data were analyzed using both classical statistics and geostatistical methods. Maps were created as a basis for orchard soil sitespecific management. Interpolations were realized according to thresholds and standard deviation of every parameter. Estimates were used to draw variation maps of each soil fertility component based on Kriging method. High geo-distribution variation was detected. The results showed that an important area is menaced by K deficiency. Indeed, in this area soil K was revealed to be under the 70 ppm threshold level. The
geostatistical analysis indicated different spatial distribution models and spatial dependence levels
for the soil properties. Sodium and OM were strongly distributed in patches. Phosphorous was
moderately spatial dependent, and K did not follow a spatial correlated distribution.
... and green band of CIR images and the NDVI for the five dates. The results indicated that three images obtained at and after peak growth produced higher r 2 values (0.64, 0.66 and 0.61) than the other two early season images (0.39 and 0.37). The yield maps generated from the three best images agreed well with a yield map from the yield monitor data.Pozdnyakova, Gimenez and Oudemans (2005) conducted a study on spatial analysis of cranberry yield at three scales with two support sizes. The yield datas calculated were fitted to either spherical (SS and LS) or exponential (MS) semivariogram models. The results indicated that the spatial properties of cranberry yield at MS were better defined in cranberry fields with more tha ...
Multispectral aerial and satellite remote sensing plays a major role in
crop yield prediction due to its ability to detect crop growth
conditions on spatial and temporal scales in a cost effective manner.
Many empirical relationships have been established in the past between
spectral vegetation indices and leaf area index, fractional ground
cover, and crop growth rates for different crops through ground
sampling. Remote sensing-based vegetation index (VI) yield models using
airborne and satellite data have been developed only for grain crops
like barley, corn, wheat, and sorghum. So it becomes important to
validate and extend the VI-based model for tuber crops like potato,
taking into account the most significant parameters that affect the
final crop yield of these crops. This research involved developing and
validating yield models for potato crop in southern Idaho fields using
high-resolution airborne and satellite remote sensing. High-resolution
multispectral airborne imagery acquired on three dates throughout the
growing season in 2004 was used to develop a VI-based statistical yield
model by integrating the area under the Soil Adjusted Vegetation Index
(SAVI) curve. The model was developed using hand-dug samples collected
in two center pivots based on soil variability and crop growth patterns
to account for variability in the leaf area duration and yields. The
three-date Integrated SAVI (ISAVI) model developed was then validated
using 2005 spot yield samples collected from two center pivot fields and
also tested for 2004 and 2005 whole field data over dozens of center
pivot fields. The three- date model was applied using 2004 and 2005
satellite images and tested. The eight-date ISAVI yield model was also
extended to satellite images to estimate the potato yield. The overall
yield estimation using the eight-date ISAVI model was better than the
three-date model as the image inputs covered the complete growth cycle
of the crop from emergence to harvest. Actual Evapotranspiration was
also used as another independent variable in the model to improve the
yield predictions. The actual ET was calculated using canopy reflectance
based crop coefficient method for all the spot yield locations in 2004
and regressed with actual yield. Both actual yield and ET correlated
very well. Multiple linear regression analysis was performed using two
independent variables, namely, ISAVI and actual ET to predict the actual
potato yield. The results showed a significant improvement in the
correlation and the new model developed was validated using 2004 and
2005 whole field data. The results showed a reasonable RMSE and low MBE
as well as a good linear correlation for both the years and a great
improvement over yield estimated using only the three-date ISAVI in the
simple linear regression model. A spatial variability analysis was also
performed at different scales using airborne and satellite images to
understand the typical spatial correlation within potato fields.
... The semivariogram remains as a standard method to quantify spatial structure of soil properties (17). Spatial variability of each soil measurement was thus described by an experimental variogram derived from measurements taken on the spatial test area. ...
To implement compaction-monitoring technologies (i.e., continuous compaction control and intelligent compaction), robust and versatile specifications are needed. These specifications require field calibration of the various machine sensor outputs to in situ soil compaction measurements. The goal of this study was to provide insights into (a) the nature of compaction-monitoring measurements, (b) how the measurements are related to soil properties determined from in situ compaction control tests, and (c) how compaction-monitoring technology may be addressed in specifications for using the technology in practice. To accomplish this goal, testing was conducted on one-dimensional test strips with several nominal moisture contents for developing statistical regression models that relate machine drive power and compaction meter value data to engineering and index properties of soil. In addition, a two-dimensional test area with variable lift thickness and moisture content was constructed and tested by using both compaction-monitoring technology and in situ devices (e.g., nuclear moisture-density gauge, portable failing weight deflectometer). The spatial distribution of the data was investigated. The significance of this research is that it represents the first documented field calibration of both one-dimensional and two-dimensional tests areas on similar soils and introduces a new approach to generating pass-fail criteria based on compaction-monitoring technology.
... NSR < 0.25 indicated strong spatial dependence, 0.25 < NSR < 0.75 indicated moderate spatial dependence, and NSR > 0.75 indicated weak spatial dependence [18]. Several other authors have also mentioned NSR-based classification when discussing spatial dependence [19][20][21][22][23][24][25]; however none of the papers mentioned NSR as well as distribution map patterns together to discuss spatial dependence. EC 1:1 spatial distributions maps were produced by using Surfer version 8 [26] to determine the spatial coverage distribution of EC 1:1 or hot and cold spots in the land application site. ...
Knowledge of spatial variability is important for management of land affected by various anthropogenic activities. This study was conducted at West Mesa land application site to determine the spatial variability of electrical conductivity (EC1:1) and suggest suitable management strategy. Study area was divided into five classes with EC increasing from class I to V. According to the coefficient of variation (CV), during 2009 and 2010, EC1:1 values for different classes were low to moderately variable at each depth. Semivariogram analysis showed that EC1:1 displayed both short and long range variability. Area coverage of classes I and II were much higher than classes III, IV, and V during 2009. However, during 2010 area coverage decreased from 26% to 14.91% for class II, increased from 12.11% to 22.97%, and 10.95% to 20.55 for classes IV and V, respectively. Overall area under EC1:1≥ 4 dS/m increased during 2009. Soil EC map showed EC classes IV (4.1–5 dS/m) and V (>5.1 dS/m) were concentrated at northwest and southeast and classes I and II were at the middle of the study plot. Thus, higher wastewater should be applied in the center and lower in the northwest and southwest part of the field.
... The semivariogram method has been largely used to quantify selfsimilar SSR by extracting a fractal dimension (D) based on the Hurst Index. Even though this index is one aspect of the known structure function (SF) and is widely used in the turbulence context, it has not been used to evaluate soil properties (Pozdnyakova et al., 2005). SF focuses on the absolute values of the differences that occur in arbitrarily large or small data, and it represents an excellent tool to illustrate soil roughness variability, as explained in the Materials and methods section of this study. ...
Soil surface roughness (SSR) is a parameter highly suited for the study of soil susceptibility to wind and water erosion. The development of a methodology for quantifying SSR has typically been based on field techniques to obtain data, rather than on the indexes used for interpreting soil roughness variability. One of the most used indexes to evaluate SSR is the random roughness (RR), easily calculated from the heights obtained with a pin meter. The RR index was obtained from soil elevation measurements collected at the intersections of a 2 × 2-cm2 grid in a 100 × 400-cm2 plot from three different types of soil. SSR values for all soil types were obtained after passing three different tillage tools (chisel, tiller, and roller) through three types of soils at field conditions. The RR index was calculated using the standard deviation (SD) of the lines parallel to the direction of tillage. Lines were 20 mm apart.Since RR assumes vertical random roughness without correlation, the variability of SSR was assessed using structure function (SF) to complement the study. Therefore, the main objective of this analysis was to better illustrate the variability of SSR in relation to spatial distribution. The SF was highly sensitive to soil roughness variability and depended on the tillage tool treatments and soil types, thereby illustrating the origin of the soil roughness variability, either from the soil itself or from the tillage tool used. We also demonstrate that the concept of a generalised Hurst exponent derived from the SF improves our ability to differentiate among the cases.
... Whelan and McBratney (2001) included a comprehensive review of the available literature (Table 3), highlighting the spatial structure of the observed variability. The variogram analyses cited had ranges from 22 m (for soil moisture) to 180 m (for soil P). Pozdnyakova et al. (2005) measured and analyzed yields in cranberry bogs at three scales representing within-and between-field variability. The results of their variogram analysis indicated that at both spatial scales a spherical model provided a good fit with ranges of about 3.5 m within fields and 2300 m between fields. ...
We examined the spatial structure of fruit yield, tree size, vigor, and soil properties for an established pear orchard using
Moran’s I, geographically weighted regression (GWR) and variogram analysis to determine potential scales of the factors affecting spatial
variation. The spatial structure differed somewhat between the tree-based measurements (yield, size and vigor) and the soil
properties. Yield, trunk cross-sectional area (TCSA) and normalized difference vegetation index (NDVI, used as a surrogate
for vigor) were strongly spatially clustered as indicated by the global Moran’s I for these measurements. The autocorrelation between trees (determined by applying a localized Moran’s I) was greater in some areas than others, suggesting possible management by zones. The variogram ranges for TCSA and yield
were 30–45m, respectively, but large nugget variances indicated considerable variability from tree to tree. The variogram
ranges of NDVI varied from about 14–27m. The soil properties copper, iron, organic matter and total exchange capacity (TEC)
were spatially structured, with longer variogram ranges than those of the tree characteristics: 31–95m. Boron, pH and zinc
were not spatially correlated. The GWR analyses supported the results from the other analyses indicating that assumptions
of strict stationarity might be violated, so regression models fitted to the entire dataset might not be fitted optimally
to spatial clusters of the data.
... Introduction value horticultural crops have also been investigated such as citrus (Zaman and Schuman 2006;Ye et al. 2007), olives (López-Granados et al. 2004), apples Best and Zamora 2008), grapes (Bramley andHamilton 2003, Taylor 2004;Bramley 2005), cranberries (Pozdnyakova et al. 2005) and tomatoes (Pelletier and Upadhyaya 1999). It is often claimed that precision agriculture can offer a great deal to the production of high value crops, and it is also easier to pay for the investment than for lower value crops. ...
We describe the yield and quality of apples from a 0.8ha apple orchard located in northern Greece over two growing seasons
and consider the potential for site-specific management. The orchard has two apple cultivars: Red Chief (main cultivar) and
Fuji (pollinator). Yield was measured by weighing all fruit harvested from groups of five adjacent trees and the position
of the central tree was recorded by GPS. Apple quality at harvest was evaluated from samples of the two cultivars in both
years for which fruit mass, flesh firmness, soluble solids content, juice pH and acidity of the juice were determined. The
variation in tree flowering was also measured in the spring of the second season using a stereological sampling procedure.
The results showed considerable variability in the number of tree flowers, yield and quality across the orchard for both cultivars.
The number of flowers was strongly correlated with the final yield. These data could potentially be used to plan precise thinning
and for early prediction of yield; the latter is important for marketing the fruit. Several quality characteristics, including
fruit juice soluble solids content and acid content were negatively correlated with yield. The general patterns of spatial
variation in several variables suggested that changes in topography and aspect had important effects on apple yield and quality.
KeywordsFlowering-Fruit quality-Intrinsic random function-k (IRF-k) kriging-
Malus domestica
-Precision horticulture-Spatial variation-Stereology
... NSR < 0.25 indicated strong spatial dependence, 0.25 < NSR < 0.75 indicated moderate spatial dependence, and NSR > 0.75 indicated weak spatial dependence [18]. Several other authors have also mentioned NSR-based classification when discussing spatial dependence19202122232425; however none of the papers mentioned NSR as well as distribution map patterns together to discuss spatial dependence. EC 1:1 spatial distributions maps were produced by using Surfer version 8 [26] to determine the spatial coverage distribution of EC 1:1 or hot and cold spots in the land application site. ...
Knowledge of spatial variability is important for management of land affected by various anthropogenic activities. This study was conducted at West Mesa land application site to determine the spatial variability of electrical conductivity (EC1:1) and suggest suitable management strategy. Study area was divided into five classes with EC increasing from class I to V. According to the coefficient of variation (CV), during 2009 and 2010, EC1:1 values for different classes were low to moderately variable at each depth. Semivariogram analysis showed that EC1:1 displayed both short and long range variability. Area coverage of classes I and II were much higher than classes III, IV, and V during 2009. However, during 2010 area coverage decreased from 26% to 14.91% for class II, increased from 12.11% to 22.97%, and 10.95% to 20.55 for classes IV and V, respectively. Overall area under EC1:1≥ 4 dS/m increased during 2009. Soil EC map showed EC classes IV (4.1–5 dS/m) and V (>5.1 dS/m) were concentrated at northwest and southeast and classes I and II were at the middle of the study plot. Thus, higher wastewater should be applied in the center and lower in the northwest and southwest part of the field.
... Fruit in each category then were weighed. Sampling locations to measure the impact of fairy ring on yield and fruit rot were placed with sufficient distance between them to assume statistical independence (9). In 2006, 180 sampling plots were established inside (n = 90) and outside (n = 90) of rings in fairy-ring-infested fields across the entire study region. ...
Fairy ring is a disease of cultivated cranberry common in the eastern growing regions of the United States, especially New Jersey and Massachusetts. Rings may persist for many years, and current control recommendations are costly and largely ineffective. The goal of this study was to accurately assess the impact of this disease on cranberry, a long-lived, high-value, perennial crop. The rate of fairy ring expansion, rate of formation of new rings, and distribution of rings across three cultivars were determined using a geographical information system (GIS) database that incorporated aerial and satellite imagery. Ring growth rates, estimated from imagery collected over a 10-year period in cv. Ben Lear, averaged 0.455 m in radius per year. Rings were observed in 'Ben Lear' three times more frequently than in either 'Early Black' or 'Stevens' cultivars. Direct sampling showed that estimates for yield within rings were 22 to 68% less than unaffected areas of the field for cv. Ben Lear. These estimates included the effects of fruit rot, which was elevated within rings to 18 to 29% of the total harvest. The impact on yield of 'Stevens' and 'Early Black' was lower than in 'Ben Lear'. Most cranberry cultivars are clonal and variation in fruit morphology within rings, particularly in 'Ben Lear', prompted an analysis of vine genotype. Areas affected by fairy ring in 'Ben Lear' showed an increase in genetic diversity at least 0.4 to 4 times that of unaffected areas. Therefore, it appears that fairy ring not only directly reduces yield but also can increase the host genetic diversity. This likely is due to increased seedling establishment resulting from seed drop when fruit decompose. Because seedlings typically yield less than the parental cultivar, the increase in genetic diversity also may contribute to long-term reduction of productivity in a cranberry field.
Cover crops (CC) are promoted to enhance soil structure and aggregation, but little is known about their impact on the spatial variability of soil microstructure. This study was conducted to assess the effects of medium-term (4-yr) annual CC systems, including oat (Avena sativa L.), winter cereal rye (rye; Secale cereale L.), oilseed radish (OSR; Raphanus sativus L. var. oleoferus Metzg. Stokes), as well as a mixture of OSR and winter cereal rye (OSR + Rye), against a control (no cover crop, no-CC), on topsoil microstructure utilizing 3D semivariance analysis, as influenced by soil depth (0–5 and 5–10 cm) in a tile-drained Orthic Humic Gleysol in Ridgetown, Ontario, Canada. Replicate undisturbed topsoil core samples were collected manually in plexiglass tubes from each plot. All soil cores were dried at 40 °C, then imaged at 20 μm pixel size using GE MS8-130× CT scanner (120kv, 0.5 mm Cu filter). The whole core greyscale imagery (representing radiodensity on the Hounsfield Scale) was subjected to a three-phase segmentation (solids, voids, and remaining soil matrix). For the whole soil and the soil matrix imagery, 16-bit histograms and 3D orthogonal semivariograms were generated. Directional anisotropy and four semivariogram parameters close to the origin, including the ratio between the values of the total variance and the semivariance at first lag (RVF), the ratio between semivariance values at second and first lag (RSF), first derivative near the origin (FDO), and second derivative at the third lag (SDT), were calculated to explain spatial variability in the soil microstructure. The results exhibited lower radiodensities for whole soil samples from the 0–5 cm than 5–10 cm layers. All CCs, other than oats, showed lower radiodensities than the control (no-CC). For both the whole soil and soil matrix, more variability was encountered in the vertical than the horizontal dimensions of all soils. Comparing the whole soil and the soil matrix in different CC systems and layers, more anisotropy was pronounced in the soil matrix than the whole soil imagery. The highest and the lowest anisotropy, calculated for the whole soil and soil matrix of different CC systems, were observed in oat and rye plots, respectively. There were no significant differences among CCs systems in terms of RVF, RSF, and SDT values, suggesting similarity of the structure at the scale slightly above image resolution. The results illustrated that soils under different CC systems showed only significant differences in the whole soil for FDO values (P ≤ 0.08). All four near-origin semivariogram parameters showed significant differences between 0 and 5 and 5–10 cm layers in both the whole soil and the soil matrix, suggesting more complexity in the microstructure of the uppermost soil layers.
Soil organic carbon influences several landscape ecological processes, and soils are becoming recognized as a mechanism to mitigate the negative impacts of climate change. There is a need to define methods and technologies for addressing soils’ spatial variability as well as the time and cost of sampling soil organic carbon (SOC). Visible and near-infrared spectroscopy have been suggested as a sampling tool to reduce inventory cost. We sampled nineteen ranch properties totaling 17,347 ha across Oklahoma and Texas in 2019 to evaluate the effectiveness and accuracy of a handheld reflectometer (Our Sci, Ann Arbor, MI, USA) (370–940 nm) and existing remote sensing approaches to estimate SOC in semi-arid grazing lands. Our data suggest that the Our Sci Reflectometer estimated soil organic carbon with a precision of approximately (±0.3% SOC); however, it was least accurate at higher carbon concentrations. The Our Sci reflectometer, although consistently accurate at lower SOC concentrations, was still less accurate than a model built using only remote sensing and digital soil map data as predictors. Combining the two data sources was the most accurate means of determining SOC. Our results indicated that the Our Sci handheld Vis-NIR reflectometer tested may have only limited applications for reducing inventory costs at scale.
Due to global climate change, Korea is facing severe droughts that affect the planting and early vegetative periods of upland crops. Soybean and adzuki bean are important legume crops in Korea, so it is critical to understand their adaptations to water stress. This study investigated the changes in root morphological properties in soybean and adzuki bean and quantified the findings using fractal analysis. The experiment was performed at the National Institute of Crop Science in Miryang, Korea. Soybeans and adzuki beans were planted in test boxes and grown for 30 days. The boxes were filled with bed soil with various soil moisture treatments. Root images were obtained and scanned every two days, and the root properties were characterized by root length, depth and surface area, number of roots, and fractal parameters (fractal dimension and lacunarity). Root depth, length and surface area and the number of roots increased in both crops as the soil moisture content increased. The fractal dimension and lacunarity values increased as the soil moisture content increased. These results indicated that the greater the soil moisture, the more heterogeneous the root structure. Correlation analysis of the morphological properties and fractal parameters indicated that soybean and adzuki bean had different root structure developments. Both soybean and adzuki bean were sensitive to the amount of soil moisture in the early vegetative stage. Soybean required a soil moisture content greater than 70% of the field capacity to develop a full root structure, while adzuki bean required 100% of the field capacity. These results would be useful in understanding the responses of soybean and adzuki bean to water stress and managing irrigation during cultivation.
We present the results of an experimental and computational pilot study of cranberry crop yield prediction using low-power microwave sensing and machine learning. We simulated backscattered radiation from cranberry canopies using plane-wave illumination with frequency content from 300 to 2400 MHz. The computational canopy domains allowed for variable soil moisture and cranberry yield in terms of cranberry mass per 1 ft
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of canopy surface area. We collected experimental field data with a prototype open-ended waveguide sensor operating between 600 and 1300 MHz. We measured experimental microwave signals by placing our sensor directly on top of cranberry-crop bed canopies in central Wisconsin and recording reflection coefficients across the operating band. We implemented a machine learning approach to map the microwave reflection coefficients to yield. The mapping procedure involves dimensionality reduction with principal component analysis, supervised learning with linear discriminant analysis, and error-correcting output codes. The idealized computational results demonstrate the feasibility of discriminating three cranberry volume fractions that are representative of central Wisconsin field conditions, at frequencies below 2 GHz. Performance evaluations of the machine learning algorithm applied to the measured field data indicated that, in 81% of test cases, the predicted crop yield had less than 8% error. Most importantly, the average yield prediction error was less than 1.3%. These pilot study results provide strong evidence that machine learning enables accurate cranberry yield estimation when trained with
in situ
(field) microwave backscattered signals.
Tobacco streak virus (TSV) has been detected in cranberry plants in Wisconsin, New Jersey, and Massachusetts, and is associated with berry scarring symptoms. In the current study, cv. Mullica Queen plants that produced scarred, symptomatic, TSV-positive fruit in one year produced nonscarred, asymptomatic, TSV-positive fruit in subsequent years, consistent with the “recovery” phenomenon previously documented in other ilarvirus-woody plant interactions. In field trials, fruit set and berry weight were significantly reduced (P < 0.05) in symptomatic, TSV-positive cranberry shoots but not in recovered, TSV-positive shoots compared with healthy, TSV-negative shoots. Likewise, return bloom in the first year following berry scarring was not negatively impacted in recovered shoots. Detection of TSV in various plant parts throughout the growing season was more variable in symptomatic shoots than in recovered shoots. Of all plant parts tested, TSV detection was lowest in berries at the time of harvest in both sympt...
Precision agriculture is the management of spatial and temporal variability of the fields using ICT. The application in horticultural crops was developed in the last ten years. Data are collected from different sources (yield and quality, soil properties, remote sensing), stored to GIS data bases, analysed using geostatistical methods to develop management zones and decision support systems are used to assist farmers to the management. Variable rate application systems were developed to apply inputs according to the real requirements of the plants in the management zones. Although variability is a well established fact, farmers' adoption is rather slower than expected because the system is complicated and in many cases profitability is not well demonstrated. Additionally, environmental benefits cannot take direct monetary values for the farmer. PA has a wider application impact like precise movement and management of farm machinery, development of efficient mechanization and fleet management as well development of farm management information systems.
The statistical properties of impurity transport of a tokamak edge plasma embedded in a dissipative drift-wave turbulence are investigated using structure function analysis. The impurities are considered as a passive scalar advected by the plasma flow. Two cases of impurity advection are studied and compared: A decaying impurities case (given by a diffusion-advection equation) and a driven case (forced by a mean scalar gradient). The use of extended self-similarity enables us to show that the relative scaling exponent of structure functions of impurity density and vorticity exhibit similar multifractal scaling in the decaying case and follows the She–Lévêque model. However, this property is invalidated for the impurity driven advection case. For both cases, potential fluctuations are self-similar and exhibit a monofractal scaling in agreement with Kolmogorov–Kraichnan theory for two-dimensional turbulence. These results obtained with a passive scalar model agree also with test-particle simulations.
A rapid and easy way for estimating soil organic matter (OM) concentration is useful for soil survey and classification of soil horizons. This paper investigates the prediction of soil OM for Korean soils using the VIS/NIR spectroscopy. A total of 580 samples from 61 Korean soil series were collected from 2009 to 2011. Reflectance from visible to near-infrared spectrum (500 to 2500 nm) was acquired using the ASD Field Spec Pro. The spectra were resampled to 4 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil OM. Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. The Regression rules model showed better results than PLSR, with lower error on the validation data. Although the prediction for Andisols has a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification found by Cubist are mainly based on absorbance at wavelengths of 850 and 2320 nm, which mainly correspond to organic absorption bands.
Avocado sunblotch viroid (ASBVd) causes an important disease of avocado, Persea americana. Symptoms of avocado sunblotch were first observed in the avocado germplasm collection at the National Germplasm Repository in Miami in the early 1980s; however, the extent of infection was unknown. An ASBVd-specific reverse transcription polymerase chain reaction (RT-PCR) protocol was developed in 1996 and used to screen every tree in the collection. Surveys in 1996 and 2000 found that although 23 newly infected trees were detected, the proportion of ASBVd-positive accessions remained unchanged at 19%. However, in a 2009 survey, 50 newly infected trees were detected for an overall infection rate of 21%. Results of spatial analyses indicate that for the older plantings, the effective range of spread increased more than threefold during the 13 year span, while in the newer plantings, the pattern of infection indicates a reintroduction of the viroid rather than natural spread. Despite strict sanitization procedures in field and greenhouse operations, ASBVd infections have increased in the USDA collection. Although genetic diversity in the collection would be reduced, eliminating all ASBVd-positive plants may be necessary to ensure that other accessions in the collection do not become infected.
In order to better understand the spatial distributions of soil trophic groups and the potential significance of these distributions to ecosystem functioning we initiated a study to describe the within-site variability of nematode feeding groups in a row-crop ecosystem. Soil cores were removed from a 48-ha corn (Zea mays) field in the U.S. Midwest prior to spring planting, and nematodes were identified by phenotypic criteria to four groups: bacterivores, fungivores, omnivores/predators, and plant parasites. Within-site variability was high for all groups; population counts spanned two orders of magnitude, with coefficients of variation ranging from 40-130% (n = 115-138 soil samples). Probability distributions were strongly lognormal. Geostatistical analysis showed that a major part of this variability was spatially dependent; variograms suggest that 70-99% of sample population variance was related to spatial autocorrelation over our geographic range of 6-80 m, except for the parasitic group, for which we detected no autocorrelation to 1200m. Maps of nonparasitic feeding groups across the field showed large multi-hectare areas of low to moderate population densities, with sub-hectare clusters of high-density populations towards one end of the site. Individual feeding groups were only weakly correlated with one another across the field (Kendall's @t @
Geophysical data rarely show any smoothness at any scale, and this often makes comparison with theoretical model output difficult. However, highly fluctuating signals and fractual structures are typical of open dissipative systems with nonlinear dynamics, the focus of most geophysical research. High levels of variability are excited over a large range of scales by the combined actions of external forcing and internal instability. At very small scales we expect geophysical fields to be smooth, but these are rarely resolved with available instrumentation or simulation tools; nondifferentiable and even discontinuous models are therefore in order. We need methods of statistically analyzing geophysical data, whether measured in situ, remotely sensed or even generated by a computer model, that are adapted to these characteristics. An important preliminary task is to define statistically stationary features in generally nonstationary signals. We first discuss a simple criterion for stationarity in finite data streams that exhibit power law energy spectra and then, guided by developments in turbulence studies, we advocate the use of two ways of analyzing the scale dependence of statistical information: singular measures and qth order structure functions. In nonstationary situations, the approach based on singular measures seeks power law behavior in integrals over all possible scales of a nonnegative stationary field derived from the data, leading to a characterization of the intermittency in this field. In contrast, the approach based on structure functions uses the signal itself, seeking power laws for the statistical moments of absolute increments over arbitrarily large scales, leading to a characterization of the prevailing nonstationarity in both quantitative and qualitative terms. We explain graphically, step by step, both multifractal statistics which are largely complementary to each other. 45 refs., 13 figs., 2 tabs.
The spatial structure of soil properties has been examined on a Typic Torrifluvent soil at The University of Arizona Experiment Station at Marana. Nine hundred samples from nine transects were collected in straight lines (100 locations for each transect), at 20-, 200-, and 2,000-cm intervals. All samples were at a 50-cm depth. Variables include 0.1 and 15 bar water con-tent, available water, surface area, particle size distribution, pH, EC, bulk density, and moisture content in the field seven days after irrigation. Autocorrelation functions were evaluated for each parameter and found to be correlated over space with patterns of three basic types: typical, random, or with a large zone of influence. Generalizations were difficult, but the cal-culated zone of influence was strongly dependent on distance between samples, with larger intervals tending to give greater values. In a few cases, this could partially be explained on the basis of larger standard deviations measured on longer tran-sects. Results indicate future difficulty in assigning scale lengths by parameter or soil.
High levels of residual soil NO3-N can contaminate ground water by leaching through the soil. Our objective was to reduce the level and spatial variability of residual soil NO3-N while maintaining optimum corn (Zea mays L.) production by variable rate N fertilizer application. The experiment was located on a 60-ha sprinkler-irrigated corn field in central Nebraska and included four N management practices: uniform rate, variable rate (VRAT), variable rate at 75% of recommended amount (VRAT @ 75%), and variable rate plus 10% (VRAT + 10%). VRAT @ 75% decreased the amount of residual NO3-N in the soil while maintaining similar grain yield to the other treatments, indicating over-application of N with treatments receiving the recommended rate. Increasing the recommended rate by 10% (VRAT + 10%) did not increase corn yield or residual soil NO3-N. Based on multifractal spectrum, no consistent pattern of spatial variability of soil NO3-N was observed for each treatment across years. Spatial variability in corn grain yield was much lower than that for soil NO3-N, indicating noneffectiveness of using soil NO3-N spatial distribution for variable rate N application unless some areas in the field are severely N deficient. Variable rate N application did not reduce variability of residual soil NO3-N or corn grain yield as compared with uniform N. Multifractal analysis quantitatively characterized the extent and pattern of spatial and temporal variability in corn grain yield and residual soil nitrate.
In large yield trials, variation in soil fertility (or, more generally, yield potential) can result in substantial heterogeneity within blocks and, thus, poor precision in treatment estimates. Precision may be improved using statistical analyses in which this spatial variation is accounted for in estimation of treatment or entry means. Three such types of spatial analysis are trend analysis, the Papadakis method, and analyses based on correlated errors models (which account for spatial variation through correlations between yields of neighboring plots). We reviewed the theory and empirical performance of these spatial analyses and compared them with the classical analyses. The classical analyses can be justified solely on the basis of randomization; spatial analyses depend on the model specified for the variation in yield potential. Performance depends on the polynomial used to describe yield potential in trend analysis, on the neighboring plots used to estimate fertility in the Papadakis analysis, and on the correlation structure in the correlated errors models. Empirical comparisons were based on data from 11 corn (Zea mays L.) yield trials and 1 soybean (Glycine max L.) trial, each showing evidence of heterogeneity within blocks. In comparison with the classical randomized blocks analysis, precision tended to be best for the trend and the trend plus correlated errors analyses, with the Papadakis method intermediate. Ranking of entries differed across analyses, because each analysis adjusts for spatial variation in a different way. Using a spatial analysis technique can improve precision, but selecting the most appropriate analysis for a given data set can be hard.
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Recent climatic records from the Greenland ice-core project (GRIP) ice core show that the climate proxy temperatures δ18O (18O/16O ratios) display sharp gradients and large fluctuations over all observed scales. We show that these variations are scale invariant over the range ≈400 yr to ≈40 kyr. The fluctuations corresponding to these scales are studied using multifractal analysis techniques. We estimate universal multifractal indices which characterize all the fluctuations for all the scales in this range and which are close to those obtained for turbulent temperatures at much shorter time scales. We speculate briefly on the origin of these common features intervening all over the observed range from seconds to kyr.
1] We analyzed the scaling properties of the hydraulic conductivity K at three sites in Northern America: MADE, Borden, and Cape Cod. We found that K at all sites exhibits multifractality (fractal and multiscaling) in both the vertical and horizontal directions, though the multiscaling was within a range smaller than that of the maximum distance between measurements. In the vertical direction, the K data for MADE, Borden, and Cape Cod were multiscaling from 0.15 to 1.35 m, 0.05 to 0.5 m, and 0.15 to 0.9 m, respectively. They were multiscaling in the horizontal direction from 9 to 45 m, 1 to 10 m, and 1 to 17 m, respectively. The multiscaling was also anisotropic. Evidence of scaling was poorest for the horizontal direction of the MADE site, and it spanned half an order of magnitude. Such results compel one to treat multifractality in the horizontal direction of MADE as supported by heuristic arguments, rather than by pure statistical evaluation. We fitted a multifractal model to the data and estimated its parameters. We found the underlying statistics of all data to be non-Gaussian, and the model capable of reproducing the probability distribution of K data, especially the negative skewness of log K. We also generated two-dimensional isotropic multifractal fields illustrating the role of the parameters of the selected multifractal model.
Yield data over 6 years (1993–1998) were investigated for spatial and temporal trends from a 7-ha field growing winter wheat and oil seed rape. The data were combined into two maps, which characterised the spatial and temporal variability recorded over those years. Techniques were developed to show the maps in either the single crop form for winter wheat, or multiple crops that included the oil seed rape data. The two maps were then combined into a single classified management map, which denoted three categories, each with different characteristics that can have an impact on the way the field is managed. These categories were; high yielding and stable, low yielding and stable, and unstable. The spatial and temporal trends in the single crop were more stable than those in the multiple crops. In percentage terms, with a single crop, the proportions of these three classes were 55, 45 and 0%, respectively. For the multiple crops, the proportions were 58, 39 and 3%, respectively. The economic significance of these areas was assessed by the production of a gross margin map and further analysis showed that the categories returned 741, 691 and 644 £/ha, respectively.
A multifractal method of analysis, initially developed in the framework of turbulence and having had developments and applications in various geophysical domains (meteorology, hydrology, climate, remote sensing, environmental monitoring, seismicity, volcanology), has previously been demonstrated to be an efficient tool to analyse the intermittent fluctuations of physical or biological oceanographic data (Seuront et al., Geophys. Res. Lett., 23, 3591-3594, 1996 and Nonlin. Processes Geophys., 3, 236-246, 1996). Thus, the aim of this paper is, first, to present the conceptual bases of multifractals and more precisely a stochastic multifractal framework which among different advantages lead in a rather straightforward manner to universal multifractals. We emphasize that contrary to basic analysis techniques such as power spectral analysis, universal multifractals allow the description of the whole statistics of a given field with only three basic parameters. Second, we provide a comprehensive detailed description of the analysis techniques applied in such a framework to marine ecologists and oceanographers; and third, we illustrate their applicability to an original time series of biological and related physical parameters. Our illustrative analyses were based on a 48 h high-frequency time series of in vivo fluorescence (i.e. estimate of phytoplankton biomass), simultaneously recorded with temper- ature and salinity in the tidally mixed coastal waters of the Eastern English Channel. Phytoplankton biomass, which surprisingly exhibits three distinct scaling regimes (i.e. a physical-biological-physical transition), was demonstrated to exhibit a very specific heterogeneous distribution, in the framework of universal multifractals, over smaller (500 m) scales dominated by different turbulent processes as over intermediate scales (10-500 m) obviously dominated by biological processes.
The primary goal of our research is to develop key elements of a precision agriculture program applicable to high-value woody perennial crops, such as cranberries. These crop systems exhibit tremendous variability in crop yields and quality as imposed by variations in soil properties (water availability and nutrient deficiency) that lead to crop stress (disease development and weed competition). Some of the variability present in the growing environment results in persistent yield losses as well as crop-quality reductions. We are using state-of-the-art methodologies (GIS, GPS, remote sensing) to identify and map spatial variations of the crop. Through image-processing methods (NDVI and unsupervised classification), approximately 65% of the variation in yield was described using 4-m multispectral satellite data as a base image.
High levels of residual soil NO3-N can contaminate ground water by leaching through the soil. Our objective was to reduce the level and spatial variability of residual soil NO3-N while maintaining optimum corn (Zea mays L.) production by variable rate N fertilizer application. The experiment was located on a 60-ha sprinkler-irrigated corn field in central Nebraska and included four N management practices: uniform rate, variable rate (VRAT), variable rate at 75% of recommended amount (VRAT @ 75%), and variable rate plus 10% (VRAT + 10%). VRAT @ 75% decreased the amount of residual NO3-N in the soil while maintaining similar grain yield to the other treatments, indicating over-application of N with treatments receiving the recommended rate. Increasing the recommended rate by 10% (VRAT + 10%) did not increase corn yield or residual soil NO3-N. Based on multifractal spectrum, no consistent pattern of spatial variability of soil NO3-N was observed for each treatment across years. Spatial variability in corn grain yield was much lower than that for soil NO3-N, indicating noneffectiveness of using soil NO3-N spatial distribution for variable rate N application unless some areas in the field are severely N deficient. Variable rate N application, did not reduce variability of residual soil NO3-N or corn grain yield as compared with uniform N. Multifractal analysis quantitatively characterized the extent and pattern of spatial and temporal variability in corn grain yield and residual soil nitrate.
The effects of nitrogen availability on N cycling and N use efficiency (NUE) were examined in natural and fertilized loblolly pine stands on the upper coastal plain of South Carolina. Indices of N availability, based on potential rates of N mineralization, ranged from 1.6 to 11 kg@?ha^-^1 (8 wk)^-^1 in the stands, and concentrations of N in foliage, wood, fine roots, and needlefall increased with greater N availability. Litterfall dry masses, net aboveground production, and litterfall nitrogen were all positively correlated with N availability, while indices of NUE decreased with increased N availability. Mechanisms that could explain increased NUE in low-N sites were examined in the field and in a phytotron study. First, nitrogen retranslocation on a per needle basis did not change significantly across the N-availability gradient, and thus could not account for the change in NUE efficiency. For the stands as a whole, however, substantially more N was retranslocated at the highest levels of N availability. Second, an increase in N uptake efficiency could not account for an increase in NUE with low N availability, since phytotron-grown seedlings fertilized with N had significantly higher rates of N uptake per unite root mass and lower root: shoot ratios than N-limited seedlings. Third, net production per unit N within pine seedlings was significantly higher in the N-limited plants, suggesting that an increase in the amount of carbon fixed per unit of tissue N could account for the observed increased in NUE in loblolly pine stands at low levels of N availability.
Highly variable productivity among Vaccinium macrocarpon (Ait.) Pursh 'McFarlin' bogs in Washington has been noted by growers. The fruiting habits of 12 Washington 'McFarlin' bogs, ranging from 5.7-28.4 t/ha productivity were characterized. Uprights from each bog were characterized using RAPD markers, and then used in a greenhouse pollination experiment to determine if variation in fruiting and fertility phenotypes could be associated with RAPD profiles. Fifteen RAPD profiles were identified, and genetic heterogeneity was high among the 12 bogs. An association between RAPD profiles and reproduction characteristics was observed. The most frequent (30%) RAPD profile appeared to represent the 'true' 'McFarlin', since it was abundant in higher-yielding bogs and its profile was identical to 'McFarlin' samples from other growing regions. A unique RAPD profile was also identified which exhibited high yield characteristics, but did not appear to be related to 'McFarlin'. The Washington 'McFarlin' bogs examined are composed of a diverse array of genotypes with variable fruiting phenotypes, indicating the variability in production has a genetic component.
Shapiro and Wilk's (1965) W statistic arguably provides the best omnibus test of normality, but is currently limited to sample sizes between 3 and 50. W is extended up to n = 2000 and an approximate normalizing transformation suitable for computer implementation is given. A novel application of W in transforming data to normality is suggested, using the three-parameter lognormal as an example.
Characterizing spatial and temporal variability is important in variable rate (VRAT) or long-term studies. This study was conducted to compare spatial variability of soil nitrate in a VRAT nitrogen (N) application study and temporal variability of soybean (Glycine max L.) yield in a long-term organic vs. inorganic study. In the VRAT study, conventional uniform N application was compared with variable rate and variable rate minus 15% N. In the long-term experiment, soybean yields under organic (manure application), fertilizer, and fertilizer plus herbicide systems were studied from 1975 to 1991. Semivariograms were estimated for soil nitrate in the VRAT and for soybean yield in the long-term study. The slope of the regression line of log semivariogram vs. log lag (h, distance or year) was used to estimate the fractal dimension (D), which is an indication of variability pattern. The intercepts (log k) of the log-log lines, which indicate extent of variability, were also compared between treatments. There was no significant effect of the N treatments on the D-values in the VRAT study. The extent of spatial variability for residual soil nitrate became significantly less after imposing N application regimes. The variable rate N application had lower log k-values than uniform application indicating reduced soil nitrate variability with VRAT N application. In the long-term study, all three management systems had similar D and log k-values for soybean yield indicating similar temporal yield variability for the three systems. The three management systems used did not change temporal effects on soybean yield. Rainfall during July and August accounted for 65% of variability in soybean grain yield. Fractal and covariance analyses can be effectively used to compare treatments or management systems for spatial or temporal variability.
The concept of a universal multifractal, a generalization of a monofractal, is a recently developed scaling model for natural phenomena characterized by irregularity. Presented herein is an effort to use universal multifractal concepts to deal with spatial variations of hydraulic conductivity K, which have a significant effect on contaminant transport in the subsurface. Structure function analyses of four K data sets show that some vertical variations of K display multifractal structures, while others are consistent with monofractal behavior. In order to make multifractal concepts more useful, multifractal noise is introduced and defined as the increments of a multifractal. It is concluded that the multifractal formalism of Schertzer and Lovejoy [1987] has provided a rather general approach for modeling In K variations in the vertical. With the exception of horizontal variations of the Borden data, all results fell within the domain of universal multifractal behavior, which includes the monofractal case. Parameters were well-defined in an empirical sense and easy to calculate, indicating a robust formalism. Results were consistent with the recent finding of Liu and Molz [1997, also Non-Gaussian and scale-variant behavior in hydraulic conductivity distributions, submitted to Water Resources Research, 1997, hereinafter referred to as submitted paper] that K variations display increasing heterogeneity at decreasing scales.
The majority of cultivated cranberry varieties were selected from native populations in the 1800s and early 1900s from sites in Massachusetts, New Jersey, and Wisconsin. Since their initial selections 100-150 years ago, varietal identities have become increasingly confused; primarily the result of there being a paucity of qualitative markers to effectively distinguish among varieties. Random amplified polymorphic DNA (RAPD) technology has the potential for allowing a more definitive classification of varieties and was used in this study to characterize 22 cranberry varieties. Twenty-two decamer primers amplified 162 scorable DNA fragments, of which 66 (41%) were polymorphic. On the basis of these 66 silver-stained RAPDs (ssRAPDs), 17 unique profiles were identified rather than the expected 22. Fourteen varieties had unique ssRAPD profiles, while the remaining 8 were represented by 3 ssRAPD profiles. Permuational analyses of the data suggest that the observed ssRAPD profile duplications are examples of varietal misclassification. Further analyses identified 2 ssRAPD markers that were found only in Eastern varieties (from Mass. and N. J.) and not in Wisconsin varieties. With varieties differing on average by 22 bands, ssRAPDs are shown to be effective in varietal identification and the assessment of genetic diversity in cranberry.
The permeabilities and dispersivities of geologic media are known to vary with the scale of observation. Particularly well documented is the consistent increase in apparent longitudinal dispersivity with the mean travel distance of a tracer. This has been previously interpreted by the author to imply that the permeabilities of many geologic media scale, on the average, according to the power-law semivariogram gamma(s) = c square root of s where c is a constant and s is distance. Tracer test data support this conclusion indirectly at least over scales from 10 cm to 3,500 m. The present paper cites evidence for such behavior over scales from 10 cm to 45 km based directly on permeability and transmissivity data. The paper then investigates theoretically the implications of such power-law behavior on the equivalent permeability of a block of rock having a characteristic length (support scale) L. It predicts that the equivalent isotropic permeability should generally decrease with L in one-dimensional media, increase with L in three-dimensional media, and show no systematic variation with L in two-dimensional media. This prediction appears to be consistent with observations.
Spatial pattern of the incidence of strawberry leaf spot, caused by Mycosphaerella fragariae (Ramularia brunnea), was quantified on commercial strawberry farms in Ohio. For each planting of strawberry, one or two transects were randomly chosen, and the proportion of leaflets (out of 15) with leaf spot was determined from N = 29 to 87 evenly spaced sampling units. Based on a likelihood ratio test, the beta-binomial distribution described the frequency of diseased leaflets better than the binomial in 93% of the 59 data sets over 3 years. Estimates of mean incidence ranged from 0.0009 to 0.82, with a median of 0.05. Estimates of the beta-binomial aggregation parameter, theta, ranged from 0 to 1.06, with a median of 0.20. Moreover, the estimate of the slope of the binary power law, fitted to the variance data for the 59 data sets, was significantly (P < 0.01) greater than one, indicating that heterogeneity, and hence the pattern of disease incidence at the spatial scale of the sampling units or smaller, was dependent on mean incidence. Spatial autocorrelation and Spatial Analysis by Distance IndicEs (SADIE) analyses detected significant positive association of disease incidence among sampling units in approximately 40% of the data sets, indicating that disease clusters extended beyond the borders of the sampling units in these fields. Collectively, the results show that strawberry leaf spot was characterized by relatively tight clusters of disease (based on theta) that extended beyond the borders of the sampling units in a little under half of the data sets (based on correlations). The information on heterogeneity was used to develop fixed and sequential sampling curves to precisely estimate disease incidence. The sequential-estimation procedure was evaluated using statistical bootstrap methods and performed well over the range of disease incidences encountered.
Means varied from 0.6 to 16.3 propagules/cm 3 , and frequency counts data for populations were best described by a negative binomial probability distribution. Variance-to-mean ratios varied from 2.5 to 12.5, k values from the negative binomial distribution varied from 0.11 to 6.97, and Lloyd's index of patchiness varied from 1.12 to 20.17. All indices of dispersion indicated varying degrees of aggregation of propagules. The greatest aggregation was observed in orchards where mean propagule densities were lowest. However, spatial lag correlation analysis did not detect clusters with similar populations in most of the orchards
Population assessments of dormant spores of Phytophthora cactorum in apple orchard soils yielded three clear distribution gradients. Populations at the bottoms of slopes were relatively high, declined with increasing distance up slopes, and strongly correlated with soil moisture content. Populations decreased with increasing distance from the tree trunk, becoming close to nil outside the tree-row herbicide strip. There was also a sharp decline in P. cactorum populations with increasing depth with approximately 50 and 70% of propagules in the top 3 and 6 cm of soil, respectively. In the absence of organic substrates, propagule numbers declined significantly after 18 months at or near the soil surface, but remained constant at 7- to 10-cm depth, indicating continual renewal of surface populations to maintain the steep depth gradient. Fallen apple leaves, fruit, and petals were all naturally colonized by P. cactorum in the field. Surface amendments with inoculated leaves in the fall resulted in a substantial increase in soil populations measured the following spring, both in microplots and directly beneath mature apple trees. Large quantities of earthworm castings (1.45 kg/m2 from May to September) were collected from the soil surface beneath apple trees. These contained relatively high populations of P. cactorum at densities comparable with those in the surface layers of soil and were likely to have contributed to the steep vertical gradient observed.
In selecting an appropriate tillage system for a specific crop and field, it is desirable to choose one that maximizes yield for the field as a whole; however, various areas in a field often give different responses to the same tillage system. The theory of regionalized variables was used to evaluate corn left bracket Zea mays L. right bracket grain yield response to the following four tillage systems (tandem disking, tandem disking followed by bedding, in-row-subsoiling and bedding, and chisel plowing). These treatments were randomized, replicated three times, and stripped across the 198-m-long field of Norfolk soil (Typic Paleudults), which had a tillage pan and variable depth to B horizon. Grain was harvested in adjoining 15. 2-m-long plots in each strip. Analysis of directional semivariograms for relative corn grain yield in the along-the-row direction, showed a different variance structure for each tillage treatment.
Quantifying the spatial variability of crop yields and yield-affecting factors are important issues in precision agriculture. Topography is frequently one of the most important factors affecting yields, and topographical data are much easier to obtain than time and labor-consuming measurements of soil properties. In this study, yield variability and the relationships between yields and terrain slopes were analyzed using theories of multifractal and joint multifractal measures. Corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yield data from 1994 to 1998 were collected via yield monitors from a central 6.6 ha section of an agricultural field in eastern Indiana. Slopes were derived from a field terrain map using a GIS. Multifractal analysis of yield and slope maps revealed that both yield and slope distributions were multifractal measures. Hence, joint multifractal analysis was applied to evaluate the effect of slope on crop-yield spatial variability. Joint multifractal analysis facilitated (i) the ability to differentiate between yield distributions corresponding to field locations with high and low slopes, and (ii) the ability to make inferences about slope distributions that affect grain yield the most. Multifractal analysis revealed that during four growing seasons with moderate and dry weather conditions, larger yields were observed at low slope locations while a wide range of yield values was observed at sites with moderate and high slopes. During the wet growing season, lower yields prevailed at locations with low slopes. Joint multifractal theory was useful for the study of yield/topography relationships and was an applicable tool for the analysis of spatially distributed data.
Multifractal formalism was utilized to study variability of different soil properties, including soil-test P and K, organic matter content, pH, Ca and Mg contents, and cation exchange capacity. Data from 1752 samples collected from a 259-ha agricultural field in central Illinois were used in the study. Based on the theory of multifractals a set of generalized fractal dimensions, D(q), and an f(α) spectrum were computed for each of the studied soil properties. The D(q) curves were fitted with a three-parameter mathematical function, which produced excellent fitting results with the coefficient of determination between measured and fitted values higher than 0.98 for all the studied data sets. We analyzed precision produced by the inverse distance interpolation procedure with different power to distance values and found the optimal power value to be related to one of the studied multifractal parameters. For the studied data, the multifractal parameter was the only data property that could be used as an a priori indicator of an optimal power value. The research demonstrated, first, that multifractal parameters reflected many of the major aspects of soil data variability and provided a unique quantitative characterization of the data spatial distributions and, second, that multifractal parameters night be useful for choosing an appropriate interpolation procedure for mapping soil data.
Soil variation has often been considered to be composed of‘functional’ or ‘systematic’ variation that can be explained, and random variation (‘noise’) that is unresolved. The distinction between systematic variation and noise is entirely scale dependent because increasing the scale of observation almost always reveals structure in the noise. The white noise concept of a normally distributed random function must be replaced to take into account the nested, autocorrelated and scale‐dependent nature of unresolved variations. Fractals are a means of studying these phenomena. The Hausdorff‐Besicovitch dimension D is introduced as a measure of the relative balance between long‐ and short‐range sources of variation; D can be estimated from the slope of a double logarithmic plot of the semivariogram. The family of Brownian linear fractals is introduced as the model of ideal stochastic fractals.
Data from published and unpublished soil studies are examined and compared with other environmental data and simulated fractional Brownian series. The soil data are fractals because increasing the scale of observation continues to reveal more and more detail. But soil does not vary exactly as a Brownian fractal because its variation is controlled by many independent processes that can cause abrupt transitions or local second order stationarity. Estimates of D values show that soil data usually have a much higher proportion of short‐range variation than landform or ground water surfaces. The practical implication is that interpolation of soil property values based on observations from single 10 cm auger observations will be unsatisfactory and that some method of bulking or block kriging should be used whenever longrange variations need to be mapped.
Current agricultural practices are aimed at maximizing productivity while minimizing the area of cultivated land. This is especially important in cranberry production because strict federal guidelines curtail development of new cranberry acreage on wetlands. A major component of this research is focused on the chronic effects of phytophthora, root rot (PRR) because of the difficulties in detection and the significant impact on yields. PRR causes a reduction in root mass, which results in reduced canopy biomass and alters the spectral reflectance characteristics of the canopy. Detection of acute cases of PRR using color-infrared (CIR) aerial photography is straightforward from apparent bare soil on May images; however, the level of detectable chronic infection is unknown. The objectives of this study are to investigate the relationships between soil characteristics, spectral properties of the crop surface, and the severity of Phytophthora effects on cranberries. Soil, pathogen, and crop data were entered in a GIS and the relationships among the factors were studied using geostatistical methods and surface maps of the relevant GIS layers. These maps were then compared and incorporated with the data derived from remotely sensed images (CIR aerial photographs-May, 2001 and July, 2001). The spatial pattern of stressed vegetation was fairly consistent through 5 years and corresponded to spread of PRR chronic injury and low yield. The disease develops in surface depressions with low infiltration rates, which have high soil water content during predictive power for the yield and vine density, whereas late-season (July) images are more correlated with PRR and soil infiltration rate.
Fine scale variations and microscale spatial patterns of fungal biomass, bacterial biomass and soil properties were examined in a hardwood forest in southern Ohio. Two 0.5×2.0 m macroplots and four 10×10 cm microplots were established 0.25 m upslope and downslope of the base of a red oak (Quercus rubra) tree in a mixed oak forest in southeastern Ohio. These plots were sampled in a regular grid pattern for bacterial and fungal biomass, organic C, pH and moisture. Semivariance analysis was used to determine the degree of spatial autocorrelation among the samples from each plot. Significant linear/sill models for microbial and soil variables were produced for the two macroplots and the majority of the microplots. The proportion of variation accounted for by spatial pattern ranged from 0 to 100% for microbial variables, depending on location and variable. Patterns of variation for all variables measured in all plots were mapped using kriging. From the semivariance analysis, the kriged maps and path analysis, it became apparent that the spatial patterns of microbial biomass and organic C were condensed upslope of the tree base and expanded downslope below the tree base. Overall, the results indicate that considerable spatial variability in fungal and bacterial biomass exists at the 1–10 cm scale, and that this variability can be adequately managed by sampling in a pattern which takes into account both random and non-random (spatial) components of variation.
Control of fairy ring disease of cultivated cranberry
- Zuckerman B.M.
Applications of geostatistics in soil science
- Zhang R.