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Spatial Variability of Soil Physical Properties Affecting Florida Citrus Production
Abstract
Spatial variability of citrus yield is very common in Florida, but very little has been done to identify the responsible soil factors. Our objective was to characterize variations in soil physical properties in sandy soils where citrus is produced in Florida. A citrus grove was divided into five productivity zones defined as "very poor," "poor," "medium," "good," and "very good" based on tree canopy volume. Six random soil samples were collected from each zone at depths of 0 to 15, 15 to 30, 30 to 45, and 45 to 60 cm. Samples were analyzed for soil color, particle size and sand size distribution, and water retention parameters. Although the coefficients of variation (1%-60%) were large for color, texture, and water retention, the values were generally higher in areas with large trees and vice versa. The trend was opposite for soil lightness and sand content, which were lower in areas of greater growth. Most of the soil properties had a large spatial variation with semivariogram range of 100 to 250 m and varied greatly with increased productivity. Differences between the productivity zones were even more prominent at greater soil depth. Characterization and quantification of spatial soil variability in citrus groves would assist in planning future soil sampling in new fields showing soil and/or tree growth variability. The selection of soil sampling sites should be based on the sampling interval suggested by the variogram range (<75 m) and include the depth (45-60 cm) influencing productivity.
... Hence, there is an emerging need for increased crop production efficiency, profitability, and environmental protection, but these cannot be achieved if the wild blueberry fields are managed as a single unit. However, spatially variable fertilizer application can overcome these problems and is more favorable economically compared with uniform rate application (Mann et al., 2010). ...
... The most popular approach to manage spatial variability within fields is the use of management zones (MZs), in which the field is subdivided into smaller areas that have relatively homogeneous attributes like landscape and soil condition, and this technique can be used to direct variable rate fertilizer application (Ferguson et al., 2003). Management zones can be based on soil survey maps (Wibawa et al., 1993) and remotely sensed maps of yield estimates (Boydell and McBratney, 2002); easily measured soil properties and fruit yield (Mann et al., 2010). The concept of MZs has been proposed as a solution to the problems associated with soil variability and its impact on the application of agricultural inputs in site-specific manner. ...
... Many researchers have attempted to characterize and quantify the spatial variation in soil properties, leaf nutrients and fruit yield to delineate MZs for different cropping systems (McBratney and Pringle 1999;Wong and Asseng, 2006;Li et al., 2008;Mann et al., 2010). However, to date little attention has been paid to wild blueberry production system. ...
The concept of management zones has been proposed as a solution to the problems associated with the soil variability to more efficiently apply agricultural inputs on a site‐specific basis. This study was designed to characterize and quantify the spatial variation in soil properties and wild blueberry fruit yield and to delineate management zones for site‐specific fertilization. Two wild blueberry fields were selected in central Nova Scotia, and a grid pattern (15×15 m) was established at experimental sites to collect soil and fruit yield samples. The soil samples were analyzed for ammonium nitrogen (NH 4 + ‐N), nitrate nitrogen (NO 3 ‐ ‐N), pH, electrical conductivity (EC), texture, and soil organic matter (SOM). The volumetric moisture content (q v) and ground conductivity data including horizontal coplanar geometry (HCP) and perpendicular coplanar geometry (PRP) were also recorded at the same grid points. The location of the sampling points were marked with a differential global positioning system (DGPS), and field boundary, bare spots, weeds, and grass patches were also mapped. The cluster analysis was performed to group the soil and fruit yield data into five zones termed avery poor,poor,medium,good,' anvery good' without prior knowledge of productivity potential with the internal homogeneity and external heterogeneity at a similarity level of greater than 70%. The coefficient of variation, geostatistical range of influence, and kriged maps suggested moderate to high variability of soil properties and fruit yield except soil pH and silt. The results of correlation matrix suggested significant relationships among the fruit yield and the soil properties. The results of ANOVA indicated that the fruit yield, HCP, PRP, q v , SOM, and inorganic nitrogen were significantly different in developed management zones except poor and very poor zones. The significant positive correlations of HCP and PRP with soil properties and fruit yield suggested that the ground conductivity data can be used to develop management zones for site‐specific fertilization. ortheastern North America is the world's leading producer of wild blueberry with over 86,000 ha under management, producing 112 million kg of fruit valued at $470 million annually (Yarborough, 2009). Wild blueberry fields are developed from native stands on deforested farmland by removing competing vegetation (Eaton, 1988). The crop is unique, as it is native to North America and has never been cultivated. Wild blueberries follow a two‐year production cycle where one year produces vegetative growth, followed by a year in which bloom, pollination, and fruit growth and development occur. The majority of fields are situated in naturally acidic soils that are low in nutrients and have high proportions of Submitted for review in June 2011 as manuscript number SW 9221; approved for publication by the Soil & Water Division of ASABE in November 2011. The authors are Aitazaz Ahsan Farooque,
... Spatial variability in soil physical and chemical properties, soil nutrient levels, water content, and yield has been well documented (Cambardella et al., 1994;Gaston et al., 2001;Wong and Asseng, 2006;Cemek et al., 2007, Mann et al., 2010. Cambardella (1994) described the field scale distribution and spatial trends for 28 soil properties in Central Iowa soils. ...
... Cemek (2007) described the spatial variability of pH, electrical conductivity (EC), exchangeable sodium percentage, and hydraulic conductivity, suggesting that hydraulic conductivity was the most variable, whereas the pH was the least variable factor. Mann et al. (2010) described the spatial variability soil physical properties at four sampling depths for Florida citrus. The authors also differentiated the productive and unproductive areas based on the spatial variability of soil properties. ...
... Many researchers have attempted to characterize and quantify the spatial variation in soil properties, leaf nutrients, and fruit yield for different cropping systems (McBratney and Pringle, 1999;Brouder et al., 2005;Wong and Asseng, 2006;Mann et al. 2010). However, to date, little attention has been paid to wild blueberry production system. ...
Crop management practices within wild blueberry fields are implemented uniformly, with inadequate attention being given to substantial variations in soil properties and fruit yield. These variations emphasize the need for precise and site-specific crop management based on proper characterization and quantification of spatial soil variability. The objective of this research was to characterize and quantify the spatial patterns of variability in soil properties and fruit yield. A grid pattern of sampling points was established at each experimental site to collect soil and fruit yield samples during 2009 to 2010 at North River and Carmal sites, Nova Scotia, Canada. The soil samples were collected from 0- to 15-cm depth at each grid point. These soil samples were analyzed for soil organic matter, pH, texture, electrical conductivity, and inorganic nitrogen (N). The volumetric moisture content (&thetas;v) was recorded at each grid point using time domain reflectometry. Ground conductivity readings were also recorded using dual EM conductivity meter at same selected grid points. Fruit yield was mapped using calibrated digital color photography during the crop year.
The coefficient of variation of the soil properties and fruit yield suggested moderate to high variability (coefficient of variation >15%) except for soil pH. The results of correlation analysis indicated that the values for inorganic N, soil organic matter, electrical conductivity, clay, and &thetas;v were generally higher in high-yielding areas and vice versa. The trend was opposite for sand and silt content, which were higher in low-yielding areas. Most of the soil properties had a large spatial variation with semivariogram range of 20 to 50 m and varied greatly within field. Kriged maps of soil properties and fruit yield also showed substantial variation within field. Characterization of spatial soil variability in wild blueberry fields would assist in planning future soil sampling in new fields showing soil and/or yield variability. The selection of soil sampling should be based on the sampling interval suggested by the variogram range (<20 m). Proper soil sampling can help in identifying yield-limiting soil properties and develop prescription maps for site-specific nutrient management to ameliorate unproductive areas and reduce environmental contamination.
... This imbalance of crop available nutrients can decrease profit margins, increase the cost of production and pose a serious threat to the environment. The spatially variable fertilizer use can solve this problem as contrast to uniform rate application of fertilizer (Mann et al., 2010). Spatially variable fertilization can accurately control the factors responsible for the variation in yield and soil properties, as the most important factors for yield variation are soil biological, physical and chemical properties (Ping et al., 2005). ...
... The development of MZs divides a big field into subfields having similar attributes, where a uniform rate of fertilization can be implemented (Ferguson et al., 2003). The MZs can be based on remotely sensed maps of yield estimates (Boydell and McBratney, 2002), soil survey maps (Schut K E Y W O R D S cluster analysis, prescription maps, site-specific fertilization, variable rate technology, wheat and Giller 2020), ground conductivity measured by a sensor (Farooque et al., 2012) and easily measured soil properties and yield attributes (Mann et al., 2010). ...
Understanding the variability in crop yield in relation to the spatial variations in soil properties can help to more efficiently apply agricultural inputs on a site‐specific basis. This study was designed to characterize and quantify the spatial variation in soil properties and wheat yield and to delineate prescription maps for site‐specific fertilization. A wheat field was selected at the research farm of the University of Agriculture Faisalabad, Pakistan and a grid pattern (30 m * 30 m) was established at the experimental sites to collect soil and crop yield samples at two sampling depths(0‐15 & 15‐30 cm). The soil samples were analyzed for soil nitrogen (N), phosphorus (P), potassium (K), pH, electrical conductivity (EC), soil organic matter (SOM) and saturation (S) at both sampling depths. The locations of the sampling points and field boundary were marked with a GPS reciever. Results of classical, geo‐statistical, and interpolated maps revealed significant spatial variability in soil properties and crop yield, demanding for site‐specific nutrient management. The cluster analysis was performed to group the soil and yield data into five productivity zones termed as ‘very low’, ‘low’, ‘medium’, ‘high’ and ‘very high’ without prior knowledge of productivity potential with the internal homogeneity and external heterogeneity at a similarity level of greater than 70%. Results of correlation matrix suggested significant relationships among the crop yield and the soil properties. Results of means comparison in different productivity zones indicated that the crop yield and soil properties were significantly different in developed management zones except ‘low’ and ‘very low’ zones. Significant variation of soil nutrients in very low to very high productivity zones at both sampling depths suggested that the soil nutrients in combination with soil pH and organic matter can be used to develop prescription maps for site‐specific fertilization. Furthermore, the results of this study emphasize the need to predict soil and crop variables using sensors, in future studies, to delineate prescription maps, as soil sampling and analysis is expansive and time consuming. Site‐specific fertilization based on soil and crop needs can improve economic and environmental efficiency.
... The spatial variability of soil properties within or among agricultural fields is inherent because of geologic and soil-forming factors (Mann et al., 2010). With the increase of cultivation years, the soil physical and chemical properties of newly reclaimed farmland would be changed through long-term irrigation and fertilization management and variation of environmental conditions. ...
... In recent years, it has been reported that the crop yield is affected by irrigation amount and spatial variabilities of soil properties (Cemek et al., 2007;Chen et al., 2015;Farooque et al., 2012;Grassini et al., 2011;Karasu et al., 2015;Mann et al., 2010;Stone et al., 2010;Wright et al., 1990). However, there is rarely research about the effects of soil physical and chemical properties on irrigation water productivity (IWP, yield per unit applied irrigation water). ...
Reasonable uses of water resources can promote the development of oasis agriculture and avoid re-desertification of reclaimed land. Therefore, knowledge of the distribution of irrigation water productivity and its relationship with soil properties is important to cope with water scarcity and ensure food security. A total of 118 sampling points were selected within the study area of 120 km ² (1 km × 1 km grid) in the oasis field of arid Northwest China, where the soil organic matter, total nitrogen, nitrate nitrogen, total phosphorus, available phosphorus, bulk density, soil water content, silt content, sand content and irrigation water productivity (IWP, yield per unit applied irrigation water) were analyzed. The traditional statistics, geostatistics and partial least squares methods (PLS) were used to investigate: i) the spatial distribution of soil properties and their changes with the cultivation years; ii) the relationship between IWP and soil properties; iii) the key driving factors of IWP from the soil factors. The results showed that soil organic matter, total nitrogen, total phosphorus, soil water content and silt content increased with the increase of cultivation years, and the correlations were significant except for total phosphorus, while the sand content showed the opposite trend; and there was no significant relationship between bulk density, available phosphorus and nitrate nitrogen with the cultivation years. IWP of oasis irrigated fields varied from 0.55 to 3.46 kg m ⁻³ , with a moderate spatial heterogeneity (CV = 40%). The average IWP was 1.48 kg m ⁻³ . Compared with the developed areas of water-saving agriculture, the IWP of oasis farmland still has great potential for improvement. The key driving factors of IWP are available phosphorus and nitrate nitrogen, and the IWP can be improved by increasing the application of phosphorus fertilizers and reducing the amount of nitrogen fertilizer in the oasis fields of arid Northwest China. The results provide scientific basis for improving IWP of oasis fields, and have a great significance to the sustainable development of water-saving agriculture in arid areas.
... However, studies that have measured sand fractions have found that different sand fractions affect soil properties and functions. Mann et al. (2010) compared productivity zones of citrus yield and found that total sand content differed by 1.6 % between very good and very poor zones while fine sand content was 6 % higher in the very good zones. Medinski et al. (2013) showed that in soil crust formation, sand particles greater than 200 µm enhanced infiltration while sand particles < 200 µm tended to decrease infiltration. ...
The soil particle size is commonly determined using the hydrometer and pipette, but visible and near-infrared (vis-NIR) or mid-infrared (MIR) spectroscopy is increasingly being used. We compared the results of these methods using 121 soil samples (> 80% sand) from the Central Sand Plains in Wisconsin. The hydrometer method overestimated the total sand content compared to the pipette. Vis-NIR and MIR spectroscopic performance changed when calibration laboratory data was obtained from the hydrometer and pipette method. Spectroscopic modeling of total sand content was very good (R2 = 0.91 to 0.94), but results were affected by the laboratory calibration method (i.e., models calibrated by hydrometer also overestimated total sand content). The MIR predictions of sand content, calibrated with the pipette, provided the best results.
... Additionally, rootstock selection must consider the shallow soil depths and flooding tolerance (Bauer et al., 2005). Lastly, flatwood soils are prone to leaching, requiring careful nutrient management and irrigation strategies (Mann et al., 2010;Syvertsen et al., 1989;Syvertsen and Smith, 1996). ...
Florida's citrus industry is currently threatened by huanglongbing (HLB, citrus greening), a disease associated with the phloem-limited bacteria Candidatus Liberibacter asiaticus and vectored by the Asian citrus psyllid (Diaphorina citri). Since its discovery, HLB has decimated the citrus industry; destroying millions of trees and reducing Florida's once proud industry to a fraction of what once existed. Huanglongbing was first identified in Florida in 2005 and within 10 years, spread to every citrus-growing county in the state. The Indian River district (IRD) growing region, located on the central east coast of the state, where most of its famous fresh grapefruit industry is located, was one of the first regions affected by HLB. This review discusses the current literature on the impact of HLB on grapefruit production in the Indian River growing region. The roles of fertilization and the challenges of the soils native to this area are analyzed as well as the impact that HLB has on the roots of affected trees.
... However, studies that have measured sand fractions have found that different sand fractions affect soil properties and functions. Mann et al. (2010) compared productivity zones of citrus yield and found that total sand content differed by 1.6 % between very good and very poor zones while fine sand content was 6 % higher in the very good zones. Medinski et al. (2013) showed that in soil crust formation, sand particles greater than 200 µm enhanced infiltration while sand particles < 200 µm tended to decrease infiltration. ...
Sand (2,000–50 µm), silt (50–2 µm) and clay (<2 µm) have been determined by sedimentation methods (i.e., hydrometer and pipette), but increasingly laser diffraction and visible and near-infrared (vis-NIR) or mid-infrared (MIR) spectroscopy are being used. It is necessary to understand the comparability and limitations of these methods. We compared these different methods using 121 soil samples from the Central Sand Plains of Wisconsin, for total sand content as well as five sand fractions (very coarse, coarse, medium, fine, and very fine). Spectroscopic methods are not hindered by assumptions of sphericity or constant particle density; yet these model-based methods require calibration by laboratory methods that do rely on these assumptions. Vis-NIR and MIR spectroscopic model predictions of total sand content differed when calibration laboratory data were obtained from the hydrometer and pipette method. The laser method was not able to accurately measure sand fractions and the fractions were poorly to moderately predicted by vis-NIR (R² = 0.01 to 0.61) but slightly better using MIR (R² = 0.23 to 0.71). The hydrometer method overestimated the total sand content compared to the pipette; the laser method was only moderately correlated to both pipette and hydrometer. Spectroscopic modeling of total sand content was very good (R² = 0.91 to 0.94), but results were affected by the laboratory calibration method and models calibrated with reference hydrometer data overestimated total sand content. We conclude that MIR predictions of sand content provide the best results, but models must be calibrated with the pipette method for soils with high sand contents.
... The average percentage of SOM before planting the cover crops was 2.320%, which was not as high as required for optimum potato cultivation, e.g., >3.5%, which enhances the soil's ability to mineralize nitrogen, which is an asset for potato cultivation [22]. Soil pH ranged from 5.8 to 7.0 with an average of 6.31, close to the range of pH of healthy soil [23]. ...
Soil chemical properties can be improved by incorporating crop residues in soil and letting it decompose. This study explored the use of incorporating residues of cover crops for improvements in soil chemical properties including soil organic matter (SOM), soil pH, and the selected soil macro- and micronutrients in greenhouse and field trials. Factors of interest included (i) cover crops and their combinations and (ii) methods of crop termination and incorporation in soil (disc, mow + disc, glyphosate, roller crimper). The greenhouse trial showed up to a 20% higher amount of SOM accumulated in soils incorporated with crop residues. Buckwheat (3.12%) and phacelia (3.12%) produced significantly different and larger SOM than that of the control treatment that received no crop residues (p ≤ 0.05). The soil pH of the brown mustard treatment was also significantly affected by the experimental treatments (p ≤ 0.05). The incorporation of crop residues did not affect soil phosphorous (P) or potassium (K) concentrations, except for brown mustard, with significantly higher values of P and K than the control treatment. Calcium (Ca) was significantly higher in the soil of phacelia + pea treatment (p ≤ 0.05). Buckwheat + pea produced a higher concentration of Ca (1028 mg/kg) followed by buckwheat alone (1006 mg/kg). Analysis of variance (ANOVA) calculated on the results of the field trial showed that the mix treatment that had a mixture of four cover crops significantly increased the SOM content. Buckwheat produced the highest (2.95%) SOM, then brown mustard and timothy. This study concludes that, irrespective of the tillage incorporation methods, the residues from cover crops are a potential source of improvement in soil health, and this practice may promote sustainable agriculture in conditions similar to those in this study.
... [9] These characteristics can be a direct result of soil forming factors and their interactions. [10] Soil spatial variability is an important determinant of efficiency of farm inputs and yield [11] as well as crop management and design and effectiveness of field research trials. [12] These variations differed among soil properties and may reflect the impacts of plant, soil fauna, precipitation, and management practices adopted in the area. ...
Soil is a suitable place for vegetation and plant growth while this valuable investment is not preserved, shortage of food, and erosion and damage natural resources will be respected. Soil is a heterogeneous, diverse and dynamic system and investigation of its temporal and spatial changes are essential. Salt accumulation in soils, affecting agricultural productivity, environmental health, and the economy of the coastal farmers. The present study was undertaken to evaluate the salinity level in different soil layer and to provide better understanding of spatial variability of soil salinity and some nutrients during dry period, in south coastal region of Bangladesh. Soil samples were collected from 30 different locations of Taltali Upazila in Barguna district. From each spot, soil samples were collected from two soil depths (0-5 cm and 5-10 cm.). The results revealed that in the 0-5 cm and 5-10 cm soil depth the EC value varied from 1.62 to 8.44 dS/m and 0.09 to 3.15 dS/m with mean value of 4.69 and 1.52 dS/m, respectively. Soil salinity is, therefore, developed within a very thin top layer of the soil, below which the salinity level is comfortable for crop growth. The top soil layer pH value was very low and strongly acidic (4.18 in 0-5 cm soil depth and 4.62 in 5-10 cm soil depth) indicating acidic nature of the soil. Phosphorus content was found higher in subsurface soil (5-10 cm soil depth) whereas sulfur content was higher in surface soil (0-5 cm soil depth). The sodium, potassium, HCO 3-and Cl-ion content was found higher in surface soil than subsurface soil. The sodium content was found several folds higher than the potassium content. The results are, therefore, clearly evidenced that top soil is very much sensitive to salt stress. The shallow rooted crops are more susceptible with this salinity. Deep rooted crops are may be suitable for growing in the coastal saline soils which contribute in agricultural management and ecological restoration in southern coastal region of Bangladesh.
... And it developed and presented successful results in describing spatial Variability. Mann et al. (2010), when studying soils in Florida, showed that the mean range of soil particles were 1665, 1010, and 2,486 meters for sand, silt, and clay respectively. Akbas (2014) when studying some soil properties in Turkey, for two surface depths 0-20 cm and subsurface 20-40 cm found that the soil color had a low Coefficient of Variation for the surface soils in the dry and wet state ranging between 6.9-6.4%, and for subsurface soils ranged between 6.1-7.8. ...
... These soils are appropriately named flatwood soils, and are characterized as being sandy with a slowly permeable subsurface layer [17]. Additionally, the soils have low fertility, low C.E.C., poor drainage, and high pH [18]. Due to these soil characteristics, the Indian River region is prone to leaching of applied fertilizers, and thus subject to reduced nutrient availability [6]. ...
Growers in Florida face unique challenges regarding maintaining proper citrus nutrition. Poor draining soils with low fertility, low C.E.C., and high rates of leaching are common in this region. In response to these challenges, interest has grown in products labeled as soil conditioners. Using a completely randomized experimental design, this greenhouse study tested the effects of 5 different combinations of a traditional fertilizer (TF) and a new soil conditioner (SC) on lemon and orange seedling physiology. Eight-month-old ‘Bearss’ lemon and ‘Valencia’ sweet orange grafted on sour orange rootstocks were employed, and five repetitions were used for each treatment. Plant biomass (dry weight), height, stem diameter, chlorophyll content, stomatal conductance and nutrient uptake were analyzed after 120 days of treatment. The results show that SC has a positive impact upon both chlorophyll levels and stomatal conductance values in both orange and lemon seedlings. However, based on dry weight growth data, we can only conclude that the SC was effective for orange seedlings at 50% TF and 0.5% SC. Based on this short 120-day evaluation, the SC achieved positive growth promotion for orange (50% TF) but not for lemon seedlings.
... For instance, soil nutrient content influences crop growth rate and nutrient uptake, while pH modifies crop nutrient acquisition (Bedada et al., 2014;Gosal et al., 2018). Similarly soil physical properties including soil aggregation, bulk density, and available water capacity affect root growth, biomass accrual, and plant water availability (Mann et al., 2010;Zaki et al., 2018). In unmanaged ecosystems, the soil's biota are important determinants of nutrient supply and soil tilth, and thus, contribute to plant production dynamics. ...
Elucidating relationships between the soil food web, soil processes, and agroecosystem function is a critical step toward a more sustainable agriculture. Soil and crop management practices can alter these relationships, and their effects can persist even after imposing new management practices. In 2005, the Cornell Organic Grain Cropping Systems Experiment was established in central New York. Four cropping systems that varied in fertilizer inputs, tillage practices, and weed control were compared: High Fertility, Low Fertility, Enhanced Weed Management, Reduced Tillage. Two crop rotation entry points were included in the experiment. In June 2017, the entire experimental site (plots and alleyways) was plowed and seeded with sorghum sudangrass [Sorghum bicolor (L.) Moench x S. sudanense (Piper) Stapf] as part of a uniformity trial to assess legacy effects of past management practices. Prior to initiating the uniformity trial, soil samples were collected and analyzed for soil health indicators. Soil samples were also collected to assess soil invertebrate abundance and community structure 34 and 70 days after planting. Sorghum sudangrass and weed biomass were sampled at the end of the uniformity trial in September 2017. Legacy effects of past management that were observed during the uniformity trial were associated with differences in nutrient inputs and soil disturbance, as well as the preceding crop. The High Fertility system had greater soil phosphorus than the Low Fertility system, and in one of the two crop rotation entry points, soil aggregate stability and soil respiration were greater in the Reduced Tillage system compared to the Enhanced Weed Management system. The Enhanced Weed Management cropping system also had a soil invertebrate community characterized by more disturbance tolerant taxa. Weed biomass varied by crop rotation entry point, but not cropping system. Sorghum sudangrass biomass was greater in the Reduced Tillage system than the Low Fertility system, and the entry point that had greater weed biomass also had greater sorghum sudangrass biomass. Piecewise structural equation modelling (SEM) was used to test relationships between response variables and showed that soil phosphorus, soil aggregate stability, and soil respiration explained variation in abundance of some invertebrates, and that aggregate stability, soil respiration, soil moisture, weed biomass, and a select group of invertebrates affected sorghum sudangrass biomass production. Overall our findings show that soil invertebrates can mediate the relationship between soil health indicators and crop productivity, and provide support for including direct measurements of soil invertebrates in soil health assessments.
... Soil particle size distribution (PSD) is useful for understanding soil conditions, because it is related to aggregation and structure (Keller and Dexter, 2012), hydraulic conductivity and permeability (Wosten and Vangenuchten, 1988) and crop productivity (Mann et al., 2010). The organic matter (OM) and total nitrogen (TN) are major determinants and indicators of soil fertility and quality, which are closely related to crop productivity (Moral and Rebollo, 2017). ...
Opencast coal mining activities have significant effects on the reconstructed soil properties, including its physical , chemical and biological properties. To assess the effects of opencast mining and associated dumping activities on reconstructed soil properties, the spatial variability of soil properties, i.e., soil particle size distribution (PSD), organic matter (OM) and total nitrogen (TN), in an inner dump after dumping and before reclamation in Antaibao opencast coalmine of Shanxi Pingshuo was analyzed using multi-fractal and joint multi-fractal methods. Soil samples at 78 sampling sites at the depths of 0-20 cm and 20-40 cm were collected using an auger. The singularity spectra of soil properties in the study area were analyzed, and three multi-fractal parameters (i.e., D 1 , D 2 and Δα) were calculated. The singularity spectra and multi-fractal parameters can quantitatively characterize the spatial variability of soil PSD, OM and TN. The variability of soil PSD, OM and TN at the depth of 20-40 cm was higher than that at the depth of 0-20 cm. At same soil depth, the sand and clay contents showed relatively high spatial variability, and the silt content, OM and TN exhibited relatively low spatial variability. The spatial variability in sand content, OM and TN between 0 and 20 cm and 20-40 cm depths had a relatively high correlation, while the clay and silt contents at the depths of 0-20 cm and 20-40 cm showed a relatively low correlation. Multi-fractal and joint multi-fractal methods exhibited more advantages in characterizing spatial variability of reconstructed soil properties. This study provided a theoretical basis for the soil sampling optimization of in the opencast coalmine dump in the Loess area of China.
... Despite the higher value of tree fruits compared with annual row crops, little precision agriculture research exists for tree fruit systems . Research of precision agriculture for Florida citrus (Citrus sp.) has addressed nutrient spatial variability (Mann et al., 2011c;Schumann, 2010;Zaman et al., 2005;Zaman and Schumann, 2006), soil physical properties (Mann et al., 2010(Mann et al., , 2011b, and citrus management zone delineation (Mann et al., 2011a). Work regarding deciduous tree fruit like apples and orchards has addressed spatial variability (Turker et al., 2011;Vega et al., 2013) and zone-based management in Greece (Aggelopoulou et al., 2011a;Aggelopoulou et al., 2010Aggelopoulou et al., , 2011bAggelopoulou et al., , 2013Papageorgiou et al., 2013). ...
Advances in precision agriculture technologies provide opportunities to improve the efficiency of agricultural production systems, especially for high-value specialty crops such as fresh apples ( Malus domestica ). We distributed an online survey to apple growers in Washington, New York, and Michigan to elicit stakeholder perceptions of precision agriculture technologies. Findings from this study demonstrated that growers are willing to adopt precision agriculture technologies when they receive results from applied research projects and are engaged with active extension programs. The availability of customized services and purchasing and rental options may minimize the effects of the economies of size that create barriers to adopting increasing access to technologies. Finally, respondents deemed collaborative efforts between industry and academic institutions crucial for adapting the innovation to better address the needs of growers.
... The lowest yield was found in plants where weeds were irradiated with the cultivator (3.81 ton ha -1 ) and the rotavator (4.71 ton ha -1 ). The higher yield in the wood chip mulch was due to improved physical soil properties (Kirandeep et al., 2010) and availability of optimum moisture and temperature (Shirgure et al., 2003) which, in turn, produced higher growth with high fruit yield (Shirgure, 2012). The reason for the lowest yield in the cultivator and the rotavator is soil degradation, erosion and nutrient loss, because the nitrogen present in the soil is in organic form. ...
Going towards sustainable fruit culture, it is very important that soil fertility should maintain by adopting the proper weeds control and adding the organic matter. This research was carried out to identify the most suitable weed control method for Kinnow orchard. The plants of Kinnow at the age of 6 years grown under drip irrigation were selected. Weeds of orchard were managed by mulching (wood chip and black polyethylene), mechanical (cultivator and rotavator) and chemical (glyphosate) methods. Mechanical weed control was taken as control treatment. The plants where wood chip mulching was used for weed control showed maximum increase in shoot length (24.00 cm), chlorophyll contents (84.00 SPAD value) and yield (11.88 ton ha-1) followed by the plants where weeds were controlled with chemical control while lowest shoot length (12.00 and 12.33 cm), chlorophyll contents (41.00 and 42.67 SPAD value) and yield (3.80 and 4.70 t) was achieved in cultivator and rotavator treatment respectively. Wood chip mulch also improved soil physical properties by reducing bulk density (1.49 Mg m-3), soil penetration resistance (785.33 kPa) and by enhancing soil organic matter (1.17%), hydraulic conductivity (34.50 mm/hr) and soil microbial biomass carbon (159.68 g kg-1) followed by glyphosate control while lowest values of soil physical properties were obtained in cultivator and rotavator weed control. Wood chip mulch also reduced weed dry weight (45.25 g) and efficiently controlled weeds (95.66%) as compared to other weed control methods. In this case, wood chip mulch performed better in all studied parameters than plastic mulch, rotavator, cultivator and glyphosate weed control. So, wood chip mulch should be recommended to citrus grower for improving soil physical properties and yield.
RESUMO-Na fruticultura sustentável, é muito importante manter a fertilidade do solo adotando-se o controle adequado de plantas daninhas e adicionando-se matéria orgânica. Assim, esta pesquisa foi realizada para identificar o modo de controle de plantas daninhas mais adequado para pomares de tangerina Kinnow. Foram selecionadas plantas de Kinnow com idade de 6 anos, cultivadas sob irrigação por gotejamento. As plantas daninhas do pomar foram submetidas a controle por meio de cobertura (com cavacos de madeira ou com polietileno preto), controle mecânico (cultivador e enxada rotativa) e métodos químicos (glifosato). O controle mecânico de plantas daninhas foi adotado como tratamento de controle. As plantas onde foi utilizada a cobertura com cavacos de madeira para controle Copyright: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original author and source are credited. Planta Daninha 2018; v36:e018168903 SAJID, M. et al. Optimum weed control method increases the yield of kinnow by improving the physical properties of soil 2 das plantas daninhas mostraram aumento máximo em comprimento da parte aérea (24,00 cm), teores de clorofila (valor do índice SPAD de 84,00) e rendimento (11,88 ton ha-1), seguidas pelas plantas nas quais o controle de plantas daninhas foi realizado quimicamente, em comparação com as plantas com controle por meio do cultivador e da enxada rotativa, nas quais houve menor comprimento da parte aérea (12,00 e 12,33 cm), menores teores de clorofila (valores de SPAD de 41,00 e 42,67) e menor rendimento (3,80 e 4,70 t), respectivamente. A cobertura com cavacos de madeira também melhorou as propriedades físicas do solo, reduzindo a densidade do solo (1,49 Mg m-3) e a sua resistência à penetração (785,33 kPa); em contrapartida, houve aumento do teor de matéria orgânica do solo (1,17%), da condutividade hidráulica (34,50 mm/hr) e do carbono da biomassa microbiana do solo (159,68 g kg-1). O segundo melhor resultado foi obtido pelo controle com glifosato, enquanto os menores valores das propriedades físicas do solo foram observados no controle de plantas daninhas com o uso do cultivador e da enxada rotativa. A cobertura com cavacos de madeira também reduziu a massa seca das plantas daninhas (45,25 g) e exerceu controle eficiente delas (95,66%), em comparação com outros métodos de controle. Nesse caso, a cobertura com cavacos de madeira obteve melhor desempenho no controle de plantas daninhas, em todos os parâmetros estudados, do que a cobertura plástica, a enxada rotativa, o cultivador e o glifosato. Assim, recomenda-se a cobertura com cavacos de madeira para que os citricultores consigam melhorar as propriedades físicas do solo e o rendimento. Palavras-chave: citrinos, cobertura do solo, glifosato, controle mecânico, propriedades físicas do solo, rendimento.
... The lowest yield was found in plants where weeds were irradiated with the cultivator (3.81 ton ha -1 ) and the rotavator (4.71 ton ha -1 ). The higher yield in the wood chip mulch was due to improved physical soil properties (Kirandeep et al., 2010) and availability of optimum moisture and temperature (Shirgure et al., 2003) which, in turn, produced higher growth with high fruit yield (Shirgure, 2012). The reason for the lowest yield in the cultivator and the rotavator is soil degradation, erosion and nutrient loss, because the nitrogen present in the soil is in organic form. ...
Going towards sustainable fruit culture, it is very important that soil fertility should maintain by adopting the proper weeds control and adding the organic matter. This research was carried out to identify the most suitable weed control method for Kinnow orchard. The plants of Kinnow at the age of 6 years grown under drip irrigation were selected. Weeds of orchard were managed by mulching (wood chip and black polyethylene), mechanical (cultivator and rotavator) and chemical (glyphosate) methods. Mechanical weed control was taken as control treatment. The plants where wood chip mulching was used for weed control showed maximum increase in shoot length (24.00 cm), chlorophyll contents (84.00 SPAD value) and yield (11.88 ton ha-1) followed by the plants where weeds were controlled with chemical control while lowest shoot length (12.00 and 12.33 cm), chlorophyll contents (41.00 and 42.67 SPAD value) and yield (3.80 and 4.70 ton) was achieved in cultivator and rotavator treatment respectively. Wood chip mulch also improved soil physical properties by reducing bulk density (1.49 Mg m-3), soil penetration resistance (785.33 kPa) and by enhancing soil organic matter (1.17%), hydraulic conductivity (34.50 mm/hr-1) and soil microbial biomass carbon (159.68 g kg-1) followed by glyphosate control while lowest values of soil physical properties were obtained in cultivator and rotavator weed control. Wood chip mulch also reduced weed dry weight (45.25 g) and efficiently controlled weeds (95.66%) as compared to other weed control methods. In this case, wood chip mulch performed better in all studied parameters than plastic mulch, rotavator, cultivator and glyphosate weed control. So, wood chip mulch should be recommended to citrus grower for improving soil physical properties and yield.
... As informações obtidas por meio do mapeamento da variação dos atributos do solo são utilizadas como subsídio para o planejamento das práticas de manejo para a conservação do solo e da água (Mello et al., 2006;Silva et al., 2008), produção de culturas agrícolas (Sanchez et al., 2005;Mann et al., 2010), estudos relacionados à gênese (Camargo et al., 2008), delineamento amostral (Montanari et al., 2005), estudos de emissão de CO2 (Panosso et al., 2009;Brito et al., 2010) ...
O conhecimento da variação espacial dos atributos do solo é primordial para o gerenciamento do
sistema agrícola. O objetivo do trabalho foi mapear, em uma área de 3,4 ha em Seropédica, RJ, a
condutividade elétrica aparente, susceptibilidade magnética e os teores dos elementos radioativos tório
e urânio do solo, medidos por sensores geofísicos in situ, e os teores de argila, ferro, carbono orgânico
e umidade, e capacidade de troca catiônica do solo, medidos em laboratório, em 130 pontos amostrais.
Comparou-se krigagem ordinária com krigagem universal utilizando as coordenadas geográficas x e y
e a elevação como covariáveis. Os dois métodos de krigagem produziram mapas com padrão de
distribuição espacial e índices de incerteza semelhantes. Outrossim, os padrões de dependência e
distribuição espacial foram similares entre os atributos geofísicos e os de laboratório, evidenciando o
potencial da geofísica para o mapeamento de atributos do solo.
... NS = non-significant at 0.05 P level. productivity zones at 0 to 30 cm agrees with the variation in soil chemical and physical properties along the productivity gradient (Mann et al., 2009(Mann et al., , 2010b. As a result of the poor performance of the very good productivity zone soils at the lower two depths, sorghum shoot weight was also compared at the cumulative 0-to 30-cm depth, where the best three zones (very good, good, and medium) had the highest shoot weights followed by the poor and very poor productivity zones. ...
Citrus production in Florida is commonly affected by a high degree of spatial variability of soils. Therefore, this study developed rapid indicator crop bioassays to evaluate the relationships between indicator crops and citrus production at various soil depths. A citrus grove was divided into five productivity zones based on existing tree canopy volume using GIS software ("very poor," "poor," "medium," "good," and "very good"). Visual ratings of percentage cover were collected from each zone using a 1-m 2 quadrant. Six random soil samples were collected between the tree rows from each productivity zone at four depths (0 to 15, 15 to 30, 30 to 45, and 45 to 60 cm). Greenhouse bioassay experiments used sorghum and radish crops grown in soil sampled from four depths. Overhead photographs of potted radish plants were captured periodically with a SLR digital camera to calculate leaf area by image processing. Shoot weights, shoot length, root weights, and leaf nutrient concentrations were measured at harvest (56 and 21 days after germination for sorghum and radish, respectively). Germination, shoot length, and shoot weight of sorghum and radish were significantly affected by the productivity zone. Sorghum (0 to 30 cm), radish (0 to 45 and 0 to 60 cm) and weed cover were strongly correlated (r ≈0.50 to 0.60***) with citrus yield and canopy volume at the lower two depths. The strong relationships (r > 0.50***) of sorghum and radish shoot weights and weed cover with soil properties at greater depths demonstrated the important role of cumulative root zone depth of 60 cm in differentiating citrus productivity. These results revealed that citrus production in poor areas of the grove was limited by the shallow depth of productive soil, and citrus productivity could be successfully mapped using indicator crop bioassays with soil samples taken at multiple depths.
... As alterações nos atributos químicos de solos irrigados são função do tipo de íons e sua valência presentes na solução do solo, pH do solo e da floculação de partículas de argilas (LIMA et al., 2009;ASSIS et al., 2010). O desequilíbrio e interação desses fatores podem causar dispersão da fração argila, de forma variável, haja vista que, a distribuição dos atributos do solo apresenta-se de forma heterogênea na paisagem por influência dos fatores de formação do solo, ocasionando em pequena amplitude de espaço interferir na produtividade das culturas (MANN et al., 2010;ZUCOLOTO et al., 2011). ...
The landscape configuration favors the understanding of the behavior of textural attributes being influenced to a lesser extent by the strand segments because the migration of sediments, water and nutrients meet the surface geological processes and to weathering. This study aim to characterize the spatial variability
of texture in an irrigated area by center pivot in different positions in the landscape in the Crystalline, state GO region. Soil samples were taken at regular intervals of 10 m, shaped mesh, totaling 180 sampling points in different positions in the landscape. Samples were collected at depths of 0.00 to 0.10 m, 0.10-0.20 0.20-0.30 m
me for the determination of clay, silt and sand. The data were submitted to descriptive statistics, geostatistics and kriging interpolation. The results demonstrate that migration occurred particles followed the flow of water present in causing the selective transport of sediment, which would imply higher erosion sediments, water and nutrients. The shed forms conditioned the movement and concentration of soil particles. The migration of clay to subsurface soil layers is related to the genesis of the soil and carrying of. = A configuração da paisagem favorece o entendimento do comportamento dos atributos texturais, sendo influenciada em menor escala pelos segmentos de vertente, pois a migração de sedimentos, água e nutrientes obedecem aos processos geológicos superficiais e ao intemperismo. Diante disso, objetivou-se com esse trabalho caracterizar a variabilidade espacial da textura em uma área irrigada por pivô central em diferentes posições na paisagem da região de Cristalina-GO. Foram realizadas amostragens do solo em intervalos regulares de 10 m, em forma de malha, totalizando 180 pontos, em diferentes posições da paisagem. As amostras foram coletadas s nas profundidades de 0,00-0,10 m, 0,10-0,20 m e 0,20-0,30 m para
a determinação dos teores de argila, silte e areia. Os dados foram submetidos à análise estatística descritiva, geoestatística e interpolação por krigagem. Os resultados demonstram que a migração de partículas seguiu o fluxo de água na vertente causando o transporte seletivo de sedimento, o que implica em maior erosão de sedimentos, água e nutrientes. As formas da vertente condicionaram o movimento e a concentração das
partículas do solo. A migração de argila para camadas subsuperficiais do solo é relacionada com a gênese do solo e ao carreamento de partículas pela água.
... (a) Based on experimental studies in citrus orchards, bulk density is a physical soil property that increases with soil depth with typical values ranging from 1410 to 1720 kg/m 3 at soil profiles of 0-0.6 m (Mann et al. 2010). Higher bulk density occurs when soil particles are pressed together reducing the overall pore space. ...
The water flow and the mass transport of agrochemicals in the unsaturated and saturated zone were simulated in the extended alluvial basin of Keritis river in Crete, Greece (a predominantly flat and most productive citrus growing area) using the hydrological model MIKE SHE. This model was set up based on information on land use, geology, soil structure, meteorological data, as well as groundwater level data from pumping wells. Additionally, field measurements of the soil moisture at six different locations from three soil depths (0.1, 0.2, and 0.3 m) were used as targets to calibrate and validate the unsaturated flow model while for saturated condition, groundwater level data from three well locations were used. Following the modeling approach, the agrochemical mass transport simulation was performed as well, based on different application doses. After the successful calibration processes, the obtained 1D modeling results of soil moisture-pressure related to soil depth at different locations were used to design a proper and cost-effective irrigation programme (irrigation timing, frequency, application rates, etc.) for citrus orchards. The results of the present simulation showed a very good correlation with the field measurements. Based on these results, a proper irrigation plan can be designed at every site of the model domain reducing the water consumption up to 38 % with respect to the common irrigation practices and ensuring the citrus water productivity. In addition, the effect of the proposed irrigation scheduling on citrus yield was investigated. Regarding the agrochemical concentration in the groundwater for all dose cases was below the maximum permissible limit. The only exception was for the highest dose in areas where the water table is high. Thus, this modeling approach could be used as a tool for appropriate water management in an agricultural area estimating at each time and location the availability of soil water, contributing to a cost-effective irrigation plan.
... As informações obtidas por meio do mapeamento da variabilidade dos atributos dos solos são utilizadas no planejamento das práticas de manejo para conservação do solo e da água (Mello et al., 2006;Silva et al., 2008;Sanchez et al., 2009;Vaezi et al., 2010), produção de culturas agrícolas (Sanchez et al., 2005;Siqueira et al., 2010b;Mann et al., 2010), estudos relacionados à gênese (Camargo et al., 2008a), otimização amostral (Montanari et al., 2005), emissão de CO 2 (Panosso et al., 2009;La Scala Júnior et al., 2010;Brito et al., 2010;Mendonça et al., 2011;Merbold et al., 2011). Pode-se identificar, por meio deste mapeamento, áreas com maior homogeneidade dos atributos dos solos. ...
One of needs of modern agriculture is the prediction of spatial variability of soil properties at more detailed scales for sustainable management and optimization of management practices. The mathematical model associated with knowledge of variability of soil attributes and mapping of relief forms has helped in agricultural planning. In this regard the aim of this study was to characterize the spatial variability of physical and chemical properties of Oxisols and Ultisols using numerical classification and the digital elevation model. Two distinct landforms: convex for the Oxisol (158 ha) and linear for the Ultisol (172 ha). 53 samples from the Oxisol and 57 samples from the Ultisol were taken. Multivariate analysis of clusters of attributes studied from their euclidean distances was performed. This analysis by dendograms along with digital elevation models for different soils characterized was more homogeneous in Ultisol groups, and less homogeneous for the Oxisol in convex landform. These quantitative methods showed that the landforms conditioned the spatial pattern of soil attributes.
... The inverse distance weighting (IDW) (Weber and Englund 1992;Gotway et al. 1996) and splines ( Laslett et al. 1987;Webster and Oliver 2001) methods are commonly used to spatially interpolate soil properties. Based on regionalized variable theory (Matheron 1963), the geostatistical interpolation method known as kriging (Goovaerts 1999) is also widely applied to map various soil properties (Chien et al. 1997;Liu et al. 2008;Wang et al. 2009;Mann et al. 2010;Bai and Wang 2011;Liu et al. 2011). Theoretically, kriging can provide the best linear unbiased estimates and information on the distribution of the estimation error (Webster and Oliver 2001). ...
Soil pH plays an important role in biogeochemical processes in soils. The spatial distribution of soil pH provides basic and useful information relevant to soil management and agricultural production. To obtain an accurate distribution map of soil pH on the Loess Plateau of China, 382 sampling sites were investigated throughout the region and four interpolation methods, i.e., inverse distance weighting (IDW), splines, ordinary kriging, and cokriging, were applied to produce a continuous soil pH surface. In the study region, soil pH values ranged from 6.06 to 10.76, with a mean of 8.49 and a median of 8.48. Land use type had a significant effect (p < 0.01) on soil pH; grassland soils had higher pHs than cropland and forestland soils. From a regional perspective, soil pH showed weak variation and strong spatial dependence, indicated by the low values of the coefficient of variation (0.05) and the nugget-to-sill ratios (<0.25). Indices of cross-validation, i.e., average error, mean absolute error, root mean square error, and model efficiency coefficient were used to compare the performance of the four different interpolation methods. Kriging methods interpolated more accurately than IDW and splines. Cokriging performed better than ordinary kriging and the accuracy was improved using soil organic carbon as an auxiliary variable. Regional distribution maps of soil pH were produced. The southeastern part of the region had relatively low soil pH values, probably due to higher precipitation, leaching, and higher soil organic matter contents. Areas of high soil pH were located in the north of the central part of the region, possibly associated with the salinization of sandy soils under inappropriate irrigation practices in an arid climate. Map accuracy could be further improved using new methods and incorporating other auxiliary variables, such as precipitation, elevation, terrain attributes, and vegetation types.
... In recent years, geostatistical analysis has provided a set of statistical tools for a description of spatial patterns, spatial prediction, and uncertainty assessment (Chien et al., 1997;Goovaerts, 1999;Mann et al., 2010;Wang et al., 2010). A number of studies have investigated the spatial variability of soil chemical properties by geostatistical methods with different spatial dependence in different areas, but these results are applicable only at local scales. ...
An understanding of spatial variability for soil chemical properties is essential in optimizing fertilization and sustaining yield. The objective of this study was to investigate the spatial variability of soil chemical properties in a 10-year-old jujube trees hillslope (2,725 m2) on the Loess Plateau of China. A total of 250 soil samples were collected at two depths (0-10 and 10-30 cm) in August 2010. Soil organic carbon (SOC), total nitrogen (TN), total phosphorus, pH, and electrical conductivity were determined for all soil samples, and data were analyzed by the classic and geostatistical methods. Classic statistics showed that soil organic carbon, TN, total phosphorus, and electrical conductivity had moderate variability, and pH had low variability. Soil organic carbon and TN in the lower position were significantly greater than in the upper and middle positions (P < 0.01), whereas pH in the middle slope was significantly greater than the other positions (P < 0.01). The geostatistical statistics results showed that the soil chemical properties were defined by the exponential or spherical models. The nugget-to-sill ratios indicated that all soil chemical properties had moderate dependences. The results of cross-validation showed that spatial prediction maps from ordinary kriging method were reliable in the study slope (the kriged reduced mean square error higher than 0.99 and the kriged average error close to 0). These results and maps could provide useful information for the development and application of precision agriculture on the Loess Plateau.
Introduction: Iran is one of the most important countries in citrus (oranges) production. Citrus fruits are grown in different soils with a wide range of physical, chemical and fertility properties in the country, although some restrictions in the cultivated lands cause yield loss. In this regard, the present study was conducted to investigate the effect of physical, chemical and soil fertility characteristics on citrus yield in important areas under cultivation, the regression relationships of characteristics with yield, and the rating of soil and land
parameters.
Materials and Methods: The 138 oranges orchards (118 orchards for rating and 20 orchards for validation) were selected in Fars, Mazanderan, Kerman and Guilan provinces. In each garden, a questionnaire was completed, a soil pedon was studied and soil samples were taken to carry out the appropriate physicochemical
analyses. The selected soil and land characteristics were soil salinity (EC), exchangeable sodium percentage (ESP), pH, gypsum content, soil calcium carbonate (TNV), organic carbon (OC), clay, sand, silt, gravel, and soil available phosphorus and potassium contents. From the whole obtained data, 20 data were considered for validation purpose and the remaining data were used for modeling based on stepwise multivariate and simple regression methods. In these equations, the relationship between yield, as dependent variable, with soil and land characteristics, as independent variables, was investigated. Finally, land characteristics rating was obtained by
the FAO method and the proposed crop requirements table was evaluated using the validation dataset.
Results and Discussion: The results of descriptive statistics analysis showed that the variance values for available potassium, sand, clay, gravel and TNV were high and for pH and OC and gypsum were negligible. Therefore, most soil properties have a wide range of variation which could be related to the fact that oranges are
grown in a wide range of soil types. The value of TNV varied between 10 and 33.3%. The presence of carbonate in soil reduces the availability of macro- and micronutrient elements in direct and indirect manners. The average of EC in the studied orchards was 5.4 dS.m-1
. Minimum, maximum and average of ESP were 1.7, 28 and 10.7,
respectively. The lowest and highest salinity and sodicity were observed in Mazandaran and Kerman soils, respectively. Maximum, minimum and average percentage of gypsum were 12, 0.36 and 3.54%, respectively. The highest amount of gypsum was observed in Bam and Shahdad regions of Kerman province and the lowest
gypsum content was observed in Mazandaran and Guilan provinces. The soil pH varied from 6.63 to 8.8 with the average of 7.8. The soil OC values were between 0.05 and 3.53% and its average was 0.89%, showing the fact that the most studied soils were poor in organic matters. The average of soil available phosphorus and potassium
in the studied orchards for citrus was less than the critical level. The average, minimum and maximum of available potassium were 224, 100 and 360 mg.kg-1 , respectively. The mean, minimum and maximum amounts of available phosphorus were 21.6, 8 and 45.9 mg.kg-1 , respectively. According to the multivariate regression
model, among soil properties, EC, ESP, TNV, gypsum, gravel, available phosphorus and potassium were selected by the model. The determination coefficient of the model was 0.95, indicating that these properties have the greatest effect on citrus yield. Simple regression equations demonstrated that TNV, gypsum, EC, ESP, sand, clay, gravel, available potassium and phosphorous had the highest correlation (R2 > 0.6); and soil OC and pH had the lowest correlation (R2<0.2) with yield. The equations also revealed that soil EC, ESP, gypsum, TNV and
gravel percentage had the greatest effect in yield loss, and soil organic carbon, absorbed phosphorus and potassium had the greatest effect on increasing citrus yield. As stated in equations, reported permissible and critical thresholds for effective soil properties on citrus yield, were 2.4 dS.m-1 for EC, 5 for ESP, 1.5% for gypsum, 20% for TNV, 22 mg.kg
-1 for available phosphorus, 280 mg.kg-1 for available potassium, 110 cm for soil depth, and >2 m for groundwater level. Finally, evaluating the proposed crop requirements table with validation dataset fitted between citrus yield and soil index, resulted in the determination coefficient value of 0.79, denoting the acceptable accuracy of proposed table.
Conclusion: Overall results showed that the main land limiting characteristics for orange production were soil salinity and sodicity, high amount of soil calcium carbonate and gypsum. Among unsuitable physical and fertility properties of soil, salinity and sodicity are the most effective factors affecting yield reduction. Consequently, proper management practices such as introducing cultivars compatible with these soil conditions, soil remediation and leaching operations to reduce soil salinity and sodicity are necessary. Furthermore, in most areas under orange cultivation such as Fars and Kerman provinces, the soil calcium carbonate content is more
than the critical level for plant growth. In addition, the averages of soil available phosphorus and potassium were less than the critical levels, which should be considered for nutrient management of orchards. The proposed table of crop requirements seems to be accurate enough to conduct land suitability studies for orange varieties, especially cultivars grown in the north and south of the country.
Introduction: Iran is one of the most important countries in citrus (oranges) production. Citrus fruits are grown
in different soils with a wide range of physical, chemical and fertility properties in the country, although some
restrictions in the cultivated lands cause yield loss. In this regard, the present study was conducted to investigate
the effect of physical, chemical and soil fertility characteristics on citrus yield in important areas under cultivation,
the regression relationships of characteristics with yield, and the rating of soil and land parameters.
Materials and Methods: The 138 oranges orchards (118 orchards for rating and 20 orchards for validation)
were selected in Fars, Mazanderan, Kerman and Guilan provinces. In each garden, a questionnaire was completed,
a soil pedon was studied and soil samples were taken to carry out the appropriate physicochemical analyses. The
selected soil and land characteristics were soil salinity (EC), exchangeable sodium percentage (ESP), pH, gypsum
content, soil calcium carbonate (TNV), organic carbon (OC), clay, sand, silt, gravel, and soil available phosphorus
and potassium contents. From the whole obtained data, 20 data were considered for validation purpose and the
remaining data were used for modeling based on stepwise multivariate and simple regression methods. In these
equations, the relationship between yield, as dependent variable, with soil and land characteristics, as independent
variables, was investigated. Finally, land characteristics rating was obtained by the FAO method and the proposed
crop requirements table was evaluated using the validation dataset.
Results and Discussion: The results of descriptive statistics analysis showed that the variance values for
available potassium, sand, clay, gravel and TNV were high and for pH and OC and gypsum were negligible.
Therefore, most soil properties have a wide range of variation which could be related to the fact that oranges are
grown in a wide range of soil types. The value of TNV varied between 10 and 33.3%. The presence of carbonate
in soil reduces the availability of macro- and micronutrient elements in direct and indirect manners. The average
of EC in the studied orchards was 5.4 dS.m
-1
. Minimum, maximum and average of ESP were 1.7, 28 and 10.7,
respectively. The lowest and highest salinity and sodicity were observed in Mazandaran and Kerman soils,
respectively. Maximum, minimum and average percentage of gypsum were 12, 0.36 and 3.54%, respectively. The
highest amount of gypsum was observed in Bam and Shahdad regions of Kerman province and the lowest gypsum
content was observed in Mazandaran and Guilan provinces. The soil pH varied from 6.63 to 8.8 with the average
of 7.8. The soil OC values were between 0.05 and 3.53% and its average was 0.89%, showing the fact that the
most studied soils were poor in organic matters. The average of soil available phosphorus and potassium in the
studied orchards for citrus was less than the critical level. The average, minimum and maximum of available
potassium were 224, 100 and 360 mg.kg
-1
, respectively. The mean, minimum and maximum amounts of available
phosphorus were 21.6, 8 and 45.9 mg.kg
-1
هیرشنكاخ و بآ )يزرواشك عیانص و مولع(
دلج35 ، هرامش3 ، دادرم– رویرهش1400، ص. 407-395
, respectively. According to the multivariate regression model, among
soil properties, EC, ESP, TNV, gypsum, gravel, available phosphorus and potassium were selected by the model.
The determination coefficient of the model was 0.95, indicating that these properties have the greatest effect on
citrus yield. Simple regression equations demonstrated that TNV, gypsum, EC, ESP, sand, clay, gravel, available potassium and phosphorous had the highest correlation (R
2
> 0.6); and soil OC and pH had the lowest correlation
(R
2
<0.2) with yield. The equations also revealed that soil EC, ESP, gypsum, TNV and gravel percentage had the
greatest effect in yield loss, and soil organic carbon, absorbed phosphorus and potassium had the greatest effect
on increasing citrus yield. As stated in equations, reported permissible and critical thresholds for effective soil
properties on citrus yield, were 2.4 dS.m
-1
for EC, 5 for ESP, 1.5% for gypsum, 20% for TNV, 22 mg.kg
for
available phosphorus, 280 mg.kg
-1
for available potassium, 110 cm for soil depth, and >2 m for groundwater level.
Finally, evaluating the proposed crop requirements table with validation dataset fitted between citrus yield and soil
index, resulted in the determination coefficient value of 0.79, denoting the acceptable accuracy of proposed table.
Conclusion: Overall results showed that the main land limiting characteristics for orange production were soil
salinity and sodicity, high amount of soil calcium carbonate and gypsum. Among unsuitable physical and fertility
properties of soil, salinity and sodicity are the most effective factors affecting yield reduction. Consequently,
proper management practices such as introducing cultivars compatible with these soil conditions, soil remediation
and leaching operations to reduce soil salinity and sodicity are necessary. Furthermore, in most areas under orange
cultivation such as Fars and Kerman provinces, the soil calcium carbonate content is more than the critical level
for plant growth. In addition, the averages of soil available phosphorus and potassium were less than the critical
levels, which should be considered for nutrient management of orchards. The proposed table of crop requirements
seems to be accurate enough to conduct land suitability studies for orange varieties, especially cultivars grown in
the north and south of the country.
The delineation of management zones (MZs) has been suggested as a solution to mitigate adverse impacts of soil variability on potato tuber yield. This study quantified the spatial patterns of variability in soil and crop properties to delineate MZs for site-specific soil fertility characterization of potato fields through proximal sensing of fields. Grid sampling strategy was adopted to collect soil and crop data from two potato fields in Prince Edward Island (PEI). DUALEM-2 sensor, Time Domain Reflectometry (TDR-300), GreenSeeker were used to collect soil ground conductivity parameter horizontal coplanar geometry (HCP), soil moisture content (θ), and normalized difference vegetative index (NDVI), respectively. Soil organic matter (SOM), soil pH, phosphorous (P), potash (K), iron (Fe), lime index (LI), and cation exchange capacity (CEC) were determined from soil samples collected from each grid. Stepwise regression shortlisted the major properties of soil and crop that explained 71 to 86% of within-field variability. The cluster analysis grouped the soil and crop data into three zones, termed as excellent, medium, and poor at a 40% similarity level. The coefficient of variation and the interpolated maps characterized least to moderate variability of soil fertility parameters, except for HCP and K that were highly variable. The results of multiple means comparison indicated that the tuber yield and HCP were significantly different in all MZs. The significant relationship between HCP and yield suggested that the ground conductivity data could be used to develop MZs for site-specific fertilization in potato fields similar to those used in this study.
Accurate information about the spatial distribution of surface and sub-surface soil characteristics is needed for land resource management applications. Soil maps that supply such information, particularly in the arid areas, are scarce. The prediction of soil characteristics using terrain attributes was investigated. The study area was divided into smaller sub-watershed (facets). The sub-watersheds were classified into 15 classes
using certain criteria. Compound clusters of slope, curvature and compound topographic index were used to select the location of 29 soil pits within two classes of the watershed subdivisions. Ten surface and sub-surface soil attributes were determined in the field. A 30-m resolution DEM was used to derive terrain parameters, which were used to predict soil characteristics within the two classes using different statistical techniques. Within small watershed subdivisions, multiple linear regression models accurately predicted
the soil attributes for the sampled watershed subdivisions. The same prediction model was applied for un-sampled watershed subdivisions of the same class. Compared with estimates derived from a detailed conventional soil map, the model predicted soil attributes for un-sampled watershed subdivisions with reasonable accuracy and provided better representation of the spatial distributions of surface and sub-surface soil attributes. The outputs of the model are presented in digital form that is suitable for many land resources modelling and management activities.
Studies were carried with an objective to identify the best date of planting to get optimum growth, flowering and seed production of snapdragon. The experiment was conducted for two years under mid-hill conditions of Himachal Pradesh. Planting was done at an interval of 15 day starting from September 17 in both the years with planting dates as; September 17, October 2, October 17, November 1, November 16 and December 1. The maximum plant height(91.83 cm), plant spread(36.68 cm), number of stems per plant(6.08), stem length(82.00 cm), early flower bud formation(70.18 days) and flowering(107.35 days) with maximum duration of flowering(39.97 days), number of flowers per stem(31.70), number of pods per stem(30.87), seeds per pod(390.76), seed yield per plant(8.44 g) and 1000-seed weight(0.13 g) were recorded when planting was done on September 17. However, pod formation was earliest(182.48 days) in December 1 planting.
Soil and yield variability is a common problem in Florida citrus production; however, only limited progress has been made in charactering and identifying major yield-limiting properties. This study characterized the variability in soil chemical and biological properties. To achieve this, a citrus grove was divided into five productivity zones based on tree canopy volume (0-1.84, 1.84-3.67, 3.67-5.51, 5.51-7.34, and 7.34-9.18 m(3) m(-1) row for very poor, poor, medium, good, and very good, respectively) using geographic information system soft ware. Six random soil samples were collected from each productivity zone at four depths (0-15, 15-30, 30-45, and 45-60 cm) to analyze various soil properties. Soil organic matter, Mehlich I extractable P, K, Ca, and Mg, and oxalate-extractable Fe and Al varied greatly along the productivity gradient, and their differences among zones were most prominent in the lower soil depths. These soil properties had a common pattern of spatial distribution (range 150-200 m) that matched the variation in fruit yield. The results of discriminant function and partial least squares regression analyses revealed that the productivity within the grove differed in the 0- to 60-cm soil depth, signifying that greater root zone depths could better explain the differential productivity of citrus groves than the 0- to 15-cm layer, which is the depth used presently for fertilizer recommendations. Collectively, our results suggest that soil sampling depths for citrus should be 0 to 60 cm and sampling should be done based on the variogram range of easily measured soil properties.
This study was designed to characterize and quantify variability in soil properties and fruit yield to develop management zones for variable rate fertilization. A wild blueberry fields was selected in central Nova Scotia, and a grid pattern (15x15 m) was established at experimental site to collect soil and fruit yield samples. The soil samples were analyzed for ammonium nitrogen (NH4+-N), nitrate nitrogen (NO3--N), pH, electrical conductivity (EC), texture and soil organic matter (SOM). The volumetric moisture content (θv) and ground conductivity data were also recorded at the same grid points. The location of the sampling points were marked with a DGPS, and field boundary, bare spots, weeds and grass patches were also mapped. The coefficient of variation, geostatistical range of influence and kriged maps suggested moderate to high variability of soil properties and fruit yield except soil pH and silt. The cluster analysis was performed to group the soil and fruit yield data into five zones termed as 'very poor', 'poor', 'medium', 'good' and 'very good' without prior knowledge of productivity potential with the internal homogeneity and external heterogeneity at a similarity level of greater than 70%. The results of ANOVA indicated that the fruit yield, HCP, PRP, θv, SOM and inorganic nitrogen were significantly different in developed management zones except poor and very poor zones. The significant positive correlations of HCP and PRP with soil properties and fruit yield suggested that the ground conductivity data can be used to develop management zones for site-specific fertilization.
Variability in soil properties is a complication for fertilization, irrigation, and amendment application. However, only limited progress has been made in managing soil variability for uniform productivity and increased water-use efficiency. This study was designed to ameliorate the poor-productivity areas of the variable sandy soils in Florida citrus groves by using frequent small irrigations and applying organic and inorganic soil amendments. Two greenhouse experiments were set up with sorghum and radish as bioassay crops in a randomized complete block design (RCBD). The factors studied were two soil-productivity classes (very poor and very good), two water contents (50% and 100% of field capacity), two amendments (phosphatic clay and Fe humate), and two amendment rates (10 and 25 g kg–1 for sorghum and 50 and 100 g kg–1 for radish). Amendments applied at 50 and 100 g kg–1 increased the water-holding capacity (WHC) of poor soil by 2- to 6-fold, respectively. The lower rates (10 and 25 g kg–1) of amendments were not effective in enhancing sorghum growth. The higher rates (50 and 100 g kg–1) doubled the radish growth as compared to the control. The results indicate that rates greater than 50 g kg–1 of both amendments were effective in improving water retention and increasing productivity. Irrigation treatment of 100% of field capacity (FC) increased the sorghum and radish growth by about 2-fold as compared with the 50%–water content treatment. The results suggest that the root-zone water content should be maintained near FC by frequent small irrigations to enhance water availability in excessively drained sandy soils. In addition, application of soil amendments in the root zone can enhance the water retention of these soils. Furthermore, managing variable sandy soils with WHC-based irrigation can increase water uptake and crop production in the poor areas of the grove.
Site-specific crop management based on major yield-limiting soil properties requires an understanding of relationships between citrus production and soil characteristics. To explore these relationships, a 10-ha citrus grove was divided into five productivity zones based on tree canopy volume. These productivity zones were termed as "very poor," "poor," "medium," "good," and "very good." Soil samples were collected from each productivity zone at 15-cm intervals to the lowest diagnostic horizon. Magnitudes of organic matter, cation exchange capacity, extractable nutrients, silt-plus-clay content, and soil-water retention were less for the very low productivity zone than the other four zones. The clay fractions of four higher productivity zones were dominated by hydroxy-interlayered vermiculite and kaolinite, whereas only quartz was detected to 180-cm depth in the very poor zone. The very low silt-plus-clay content and absence of clay-sized phyllosilicates in the rooting zone help to explain the very low moisture and nutrient retention of the very poor productivity soil. The white color (10YR8/1) of the very poor productivity soil E horizon (15-180 cm) serves as readily observable indicator for problems it poses for citrus growth. The predictive models developed using partial least squares explained 45% to 58% and 54% to 71% variance for yield and canopy volume, respectively. The yield in the poor areas of the grove was overpredicted (47%) when the soil properties of the top 15-cm layer were used; however, yield was better predicted with the soil properties up to 60-cm depth. Therefore, the depth used for fertilizer and irrigation recommendations should be up to 0 to 45 or 0 to 60 cm. These predictive models can be used for site-specific crop management of the citrus groves showing variable tree growth.
Determination of soil color is useful to characterize and differentiate soils. The color of soil materials can be measured in the laboratory by using diffuse reflectance spectrophotometers. The spectral reflectance data given by these apparatuses are easily converted to three figures (`tristimulus values') that define the color perceived by the human eye. In turn, tristimulus values can be converted to the Munsell notation or the parameters of other color systems. Modern, commercially available spectrophotometers not only allow a quick measurement of reflectance but usually provide color data in different systems. If care is taken in obtaining homogeneously granulated or powdered soil samples, and in preparing the white reflectance standards, high accuracy and precision are obtained. Small differences in soil color can then be used to identify and study differences in soil compositional properties. For this purpose, several `color indices' calculated from the color data can also be used.
This document integrates information that was presented at the Sand Land Workshop, 2003, dealing with vegetable production on mineral soils of southwest Florida. The intent of this document is to review those challenges facing vegetable growers dealing with soils, water management, and nutrients. Certain available strategies are evolving to efficiently produce vegetables in southwest Florida on variable mineral soils and are reviewed herein. This document is SL-233, one of a series of the Department of Soil and Water Sciences, UF/IFAS Extension. Original publication date October 2005. SL-233/SS452: Management of Soil and Water for Vegetable Production in Southwest Florida (ufl.edu)
Numerous techniques of management zone delineation have been studied; however, few comparisons between techniques exist in the literature. The objectives of this study were: (i) to determine how consistently two management zone delineation techniques (a soil- color-based management zone (SCMZ) technique and a yield-based management zone (YBMZ) technique) characterize regions of high, medium,andlowgrainyield;and(ii)tocomparetherelativeaccuracies with which the two management zone delineation techniques characterize the grain yield within low, medium, and high productivity potential management zones. This study was conducted for three site years in northeastern Colorado. Management zones were delineated before planting. The SCMZ technique used: (i) bare-soil imagery, (ii) topography, and (iii) farmer's experience. The YBMZ relied on: (i) bare-soilimagery,(ii) soilorganicmatter,(iii) cationexchangecapacity, (iv) soil texture, and (v) the previous season's yield map. Grain yields ranged from 6.9 to 15.5 Mg ha 21 across all site years. Grain yields were significantly different between SCMZ zones for all site years. Grain yield in the SCMZ high zones were up to 1.88 Mg ha 21 higher than YBMZhighzones.ArealagreementsfortheSCMZtechniquewere37, 41, and 45% for Site Years I, II, and III. Based on the approaches used in this study to classify grain yield patterns, the SCMZ technique was found to be relatively better than the YBMZ technique.
2007. Delineating management zones to apply site-specific irrigation in the Venice lagoon watershed. Abstract The aim of this work was to develop a protocol to identify management zones (MZs) to apply variable rate irrigation (VRI) in the Venice Lagoon Watershed. In a 12.5-ha field cultivated with sugarbeet, sparse soil analysis, electromagnetic induction (EMI) scans and a multispectral radiometer (MSR) system were applied to gather information on spatial variability. To identify the MZs, unsupervised fuzzy c-means clustering was applied to 7 different combinations of thematic maps. Preliminary results suggest that the combination of EMI and MSR could be an efficient and cost-effective method for delineating MZs to apply site-specific irrigation.
This document integrates information that was presented at the Sand Land Workshop, held at the Dallas B. Townsend Hendry County Extension Center, LaBelle, Florida, 2003, dealing with the production of sugarcane on the mineral soils of southwest Florida. This document reviews challenges facing sugarcane growers dealing with soils, water management, and nutrients. Effective strategies, which are evolving to efficiently produce sugarcane in southwest Florida on mineral soils, are reviewed in this publication. This document is SL 230, a fact sheet of the Soil and Water Science Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Original publication date October 2005.
The relationships between Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) surface reflectance values and constituents (total iron, organic matter, TiO2, Al2O3, and SiO2) of samples representative of three important soil types from central Brazil [Terra Roxa Estruturada (STE), Latossolo Vermelho-Escuro (SLE), and Areia Quartzosa (SAQ)] were analyzed. End member spectra for green vegetation (GV), nonphotosynthetic vegetation (NPV), water (W), and the three soil types were selected by inspecting scatter plots derived from the principal components analysis (PCA) of 140 AVIRIS bands. They were then used to compose a six end member unmixing model to characterize the spectral reflectance variations associated with the different scene components, the spatial distribution of the soil types, and the effects of spectral mixing on the spectral-chemical composition relationships. Finally, regression equations fitted to soil constituents and their highly correlated spectral bands were used to produce maps showing the chemical variability in the scene for areas dominated by the presence of exposed soils, as indicated by the results from the unmixing model. The results showed a very good agreement between the spatial variability of the soil types and of the soil constituents. The largest squared correlation results were obtained for Fe2O3, TiO2, and Al2O3, but the relationships were affected in the transition from the red to the near-infrared interval by the presence of nonsoil residues (e.g., senescent vegetation or litter) over the soil surfaces. In comparison with the light and loamy sand SAQ, the dark-red clay STE and SLE presented higher contents of Fe2O3, Al2O3, and TiO2, and consequently lower overall reflectance in the scene, because of the presence of greater amounts of opaque minerals. The prediction of these constituents from remote sensing data and their close association with the spatial distribution of the different soil types demonstrate the importance of the present investigation for soil mapping and soil erosion studies.All Rights Reserved.
Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability has had limited success. In this article, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16-ha field in Iowa using three years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over three years. The root mean square error was 286 kg ha-1 representing approximately 12% of the three-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water run on into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.
The relationships between Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) surface reflectance values and constituents (total iron, organic matter, TiO2, Al2O3, and SiO2) of samples representative of three important soil types from central Brazil [Terra Roxa Estruturada (STE), Latossolo Vermelho-Escuro (SLE), and Areia Quartzosa (SAQ)] were analyzed. End member spectra for green vegetation (GV), nonphotosynthetic vegetation (NPV), water (W), and the three soil types were selected by inspecting scatter plots derived from the principal components analysis (PCA) of 140 AVIRIS bands. They were then used to compose a six end member unmixing model to characterize the spectral reflectance variations associated with the different scene components, the spatial distribution of the soil types, and the effects of spectral mixing on the spectral-chemical composition relationships. Finally, regression equations fitted to soil constituents and their highly correlated spectral bands were used to produce maps showing the chemical variability in the scene for areas dominated by the presence of exposed soils, as indicated by the results from the unmixing model. The results showed a very good agreement between the spatial variability of the soil types and of the soil constituents. The largest squared correlation results were obtained for Fe2O3, TiO2, and Al2O3, but the relationships were affected in the transition from the red to the near-infrared interval by the presence of nonsoil residues (e.g., senescent vegetation or litter) over the soil surfaces. In comparison with the light and loamy sand SAQ, the dark-red clay STE and SLE presented higher contents of Fe2O3, Al2O3, and TiO2, and consequently lower overall reflectance in the scene, because of the presence of greater amounts of opaque minerals. The prediction of these constituents from remote sensing data and their close association with the spatial distribution of the different soil types demonstrate the importance of the present investigation for soil mapping and soil erosion studies.All Rights Reserved.
mostcommonwaystodescribetherelationshipbetween twoormore variables. Correlationcoefficients area mea- Intercorrelation among soil properties can result in multicollinear- sure of the degree of linear association between any two ity problems regarding relationships between soil properties and crop yield. The objective of this study was to compare statistical methods variables when other variables are fixed. The regression of examining relationships between cotton (Gossypium hirsutum L.) model can indicate the extent by which the dependent yield,quality,andsoilproperties.Soilandplantsampleswerecollected variable can be predicted by the independent variables; from 1-ha grids on an irrigated production cotton field in Texas from the strength of prediction is expressed as r2, known as 1998 through 2000. Ordinary least square regression (OLS), partial variation in dependent variable explained by indepen- least square regression (PLS), and principal component regression dentvariablesorcoefficientofdetermination.Theinter- (PCR) were compared as methods for quantifying relationships be- correlation between soil properties does not violate the tween cotton yield or quality and soil properties. The PLS method assumptionsoftheclassicalregressionmodeling,butcan eliminated multicollinearity problems and resulted in the coefficient cause multicollinearityproblems that may resultin large estimations with meaningful signs compared with their associations variances of parameter estimates, unexplainable signs
within each study site to determine if trends in soil physical properties Of the five soil forming factors, relief (topography) corresponded to the productivity potential of the management zones. Overall, this study showed that soil physical properties exhibited sig- can be most readily assessed. Changes in field topogra- nificant spatial variability across production fields. The trends ob- phyinfluence thedistribution ofsoil propertiesand crop served for the measured soil physical properties corresponded to the productivity across a field. Multi-fold variations in crop productivity potential of the management zones. Utilizing site-specific yields within a field have been reported in several stud- management zones could help manage the in-field variability of yield- ies. Crop yields ranged from 1.0 to 6.7 Mg ha1 in a field limiting soil physical properties. in east-central Alberta, Canada (Goddard and Grant, 2001). Low, medium, and high organic matter zones were found to correspond with top, middle, and bottom S
The majority of pedotransfer function (PTFs) published for estimating water retention characteristics (WRC) use data on soil texture, bulk density, and organic matter content (OM) as predictors. For soil hydrological modeling on a regional scale, in particular the derivation of appropriate values for a PTF parameterization can be difficult where organic C data are missing. Assuming the indirect interdependency between OM and bulk density, a new PTF has been developed that estimates the WRC using only soil texture and bulk density data. To achieve a regression-based reproduction of the correlations, a calibration was chosen that connects the parameters of the van Genuchten equation with the data on bulk density and soil texture, using linear and nonlinear relationships. More than 90% of the variability in measured soil water contents was explained by the new model. The validity of the PTF was tested with a data set of 147 measured WRCs (r(2) = 0.94). Compared with another frequently used PTF model, which uses the organic C content as an additional predictor, the new model provided comparable or slightly better predictions of the WRCs.
Soil color is a soil property that may be used as an interpreting index in estimating processes and properties. Quantifying color allows one to obtain information for rapidly estimating the related processes in soils. CIELAB color parameters L*, a*, and b* of ground (air-dried and sieved) soil samples and aggregate surfaces of four soil profiles formed in calcareous colluviums in northern Turkey were analyzed. Values of color parameters measured in ground and intact soils were compared and related to soil properties by the regression technique. Results revealed that the L* value obtained with a ground soil sample was a significant predictor of organic matter in A horizons (p < 0.001). Although calcic horizons yielded the highest L* values, no significant relationship was obtained between calcium carbonate contents and lightness of soils in any of horizons studied. The parameters of b* could adequately be used to quantify dithionite-extractable iron oxide contents in soils studied. The results further showed that the CIELAB color parameters obtained with ground samples were more informative than that of aggrevated samples in relating color parameters to soil properties.
Topographical land features shape the spatial variability of soils and crop yields, especially in dryland cotton (Gossypium hirsutum L.). The objectives of this study were to (i) quantify the relationships between cotton lint yields vs. derived topographical attributes in combination with measured soil physical properties, and (ii) quantify the relationships between measured soil physical properties and derived topographical attributes. The dominant soil of the study area was classified as Vaiden soil series (very-fine, smectitic, thermic Aquic Dystruderts). More than 4500 elevation point data were recorded in a 42-ha field using a real-time kinematic-global positioning system (RTK-GPS) used in a geographic information system (GIS) to derive topographic (slope, curvature and aspect) and hydrologic attributes (wetness index, flow direction, flow length, flow accumulation, and sediment transport index). Surface (0-17 cm) sand, clay, saturated hydraulic conductivity (K-s), bulk density (p(b)), water content at seven equilibrium pressure levels ranging from -0.01 to -1.5 MPa, and 2-yr cotton lint yield data were measured from sites selected based on classified normalized difference vegetation index (NDVI). Stepwise linear regression indicated that cotton lint yield variability was explained by soil properties (65% in 2001 and 58% in 2002), and topographic and hydrologic attributes (40 and 21%), as well as their combined effects (82 and 72%). Elevation, flow direction, sediment transport index, percentage sand content, and volumetric water content (0,) at -0.001 MPa pressure explained most of the lint yield variation. Overall, statistical analysis indicated that higher elevation areas generally yielded lower (r = -0.50, P < 0.01) and may experience water stress earlier in the season, as compared with lower elevation areas. We expect that once these features are derived and interpreted, they will have a long-lasting impact on cotton management under dryland conditions.
The information provided in the 2008 2nd edition is still sound for healthy citrus trees under Florida production conditions. Much of the information provided in this document on nutrients, application methods, leaf and soil sampling and irrigation scheduling are also effective for huanglongbing (HLB) affected citrus trees. However, research conducted since HLB was detected in Florida in 2005 has established changes in many production practices, including nutrient rates, irrigation scheduling, soil pH management, and use of Citrus Under Protective Screen (CUPS). Changes to the 2nd edition of SL253 will appear in boxes similar to this one at the beginnings of chapters 2, 6, 8, 9, and 11.
Florida sugarcane (Saccharum L.) fields, particularly those on mineral soils, often exhibit areas of localized reduced growth and poor development. These areas can be very extensive and comprise up to 25% of a field. The causes for this poor growth and subsequent yield reductions are not thoroughly understood. In this study, soil samples collected at 15-cm increments to 90 cm from areas of reduced and normal ("good") sugarcane growth were analyzed for pH, organic matter (OM), microbial populations and nutrient composition. Calcium, Mg, P, K, Fe, Zn, Cu, Mn, Al, and Na concentrations on Mehlich I extracts and inorganic N (NH4 and NO3) was determined on KCl extracts. Populations of bacteria, fungi, and actinomycetes were assayed by plate counts. Soil pH, OM, Ca, Mg and/or K were significantly higher for the areas of good sugarcane growth. No consistent significant differences were detected for the other nutrients analyzed. Nutrients, OM, and microbial populations were reduced with increasing depth of soil profile, but pH was unaffected. Although moisture appears to be an important factor in the areas of reduced growth, a lower or higher water table was not consistently associated with low yielding areas in the fields. Results indicate soil amendments may improve growth of sugarcane in the poor areas, but the authors believe a complex of factors is involved and more extensive research is necessary to better identify solutions.
Understanding the growth pattern of fibrous, orange tree [Citrus sinensis (L.) Osbeck] roots enables proper fertilizer placement to improve nutrient uptake efficiency and to reduce nutrient leaching below the root zone. The objective of this study was to develop relationships defining citrus fibrous root length density (FRLD) as a function of soil depth, distance from the tree trunk, and tree size. Root systems of 18 trees with tree canopy volumes (TCV) ranging from 2.4 to 34.3 m3 on two different rootstocks and growing in well-drained sandy soils were sampled in a systematic pattern extending 2 m away from the trunk and 0.9 m deep. Trees grown on Swingle citrumelo [Citrus paradisi Macf. x Poncirus trjfoliata (L.) Raf.] rootstock had significantly greater FRLD in the top 0.15 m than trees on Carrizo citrange (C. sinensis x P. trifoliata). Conversely, Carrizo citrange had greater FRLD from 0.15 to 0.75 m below the soil surface. FRLD was significantly greater for 'Hamlin' orange trees grown on Swingle citrumelo rootstock at distances less than 0.75 m from the tree trunk compared with those on Carrizo citrange. Fibrous roots of young citrus trees developed a dense root mat above soil depths of 0.3 m that expanded both radially and with depth with time as trees grow and TCV increased. Functional relationships developed in this study accounted for changes in FRLD with increase in tree size.
A 10-ha citrus grove located near Wauchula in Hardee County, Florida, USA was examined for spatially correlated yield reduction and soil heterogeneity. Tree canopy volumes were measured and mapped in the grove using an ultrasonic array and DGPS receiver in order to describe spatial productivity. Fruit yield was mapped from harvester trucks equipped with DGPS receivers and data loggers. The grove was classified into five productivity zones representing very good, good, medium, poor, and very poor citrus growth areas based on the canopy volume. Soil samples were collected from 30 locations at four 15-cm depth increments to 60 cm and analyzed for their chemical and physical properties. Soil properties showed a continuous trend along the productivity gradient and depth. A greenhouse bioassay with radish as an indicator crop was used to compare the productivity of five zones at four depths. Overhead photographs of the potted radish plants were captured periodically with a SLR digital camera and leaf area was calculated by image processing. Shoot dry weights were also measured. Analysis of variance showed that differences between the productivity zones were pronounced at the lower two depths for leaf area and shoot dry weights. The growth curves for leaf area indicated the differences between productivity zones started appearing in the later stages of growth. Regression analysis showed that the amount of citrus canopy volume spatial variation explained by radish growth increased with soil depth. The results of the bioassay showed that citrus yield in poor areas of the grove were limited by the shallow depth of productive soil. The less productive portions of the grove that is unsuitable for deep-rooted perennial crops like citrus could be successfully mapped with soil samples taken at multiple depths. Topsoil sampling alone, as commonly practiced, proved to be unsuitable for defining the variable citrus productivity of these soils, but may be adequate for shallow-rooted annual crops.
Spatial patterns of crop yield differ from year to year because of spatial and temporal interactions that occur within a field. A clear understanding of spatial soil-water uptake by plant roots is fundamental to understand yield variability and to make management recommendations that maximize profit or minimize environmental impacts. The objective of this study was to investigate variations in water relations within and between soil map units in a field in order to explain spatial distribution of soybean yield. This research was conducted in a 20-ha field in Boone County, Iowa, in 2000. Spatial distribution of soil water was investigated in 30 sites across field using a tube-access TDR probe. Aerial digital photos were taken three times during the growing season to investigate the relationship between plant canopy and resulting yield. Results showed that soybean yield was greatly reduced in the field compared to an average year, probably due to the occurrence of a drier than normal year The yield variation was about 24%, likely due variation in soil water during pod filling. Soil water balance calculations for selected sites showed that plants likely experienced water stress in mid-July, but the level of stress increased dramatically later in the season and reached its maximum at the end of August. The sites exposed to earlier water stress exhibited lower yield. There was a good correlation (r2 ≥ 0.48) between plant available soil water and yield for any date during the reproductive phase of the soybean crop. The soil water relations were able to explain more than 48% of yield variability in 30 sites. However, the vegetation index did not correlate well with yield for any of the dates on which remotely sensed images were taken. This poor relationship indicated the variable drought stress that dominated yield variability occurred after full canopy was reached and primarily affected pod numbers, not canopy biomass.
This book is not, in any case, in total defiance of the Wise Old Mans admonition, for it is not an entirely new book. Rather, it is an outgrowth of a previous treatise, written a decade ago, entitled "Soil and Water: Physical Principles and Processes." Though that book was well enough received at the time, the passage of the years has inevitably made it necessary to either revise and update the same book, or to supplant it with a fresh approach in the form of a new book which might incorporate still-pertient aspects of its predecessor without necessarily being limited to the older books format or point of view.
This study investigates how carbon sources of soil microbial communities vary with soil depth. Microbial phospholipid fatty acids (PLFA) were extracted from 0–20, 20–40 and 40–60 cm depth intervals from agricultural soils and analysed for their stable carbon isotopes (δ13C values). The soils had been subjected to a vegetation change from C3 (δ13C≈−29.3‰) to C4 plants (δ13C≈−12.5‰) 40 years previously, which allowed us to trace the carbon flow from plant-derived input (litter, roots, and root exudates) into microbial PLFA. While bulk soil organic matter (SOM) reflected ≈12% of the C4-derived carbon in top soil (0–20 cm) and 3% in deeper soil (40–60 cm), the PLFA had a much higher contribution of C4 carbon of about 64% in 0–20 cm and 34% in 40–60 cm. This implies a much faster turnover time of carbon in the microbial biomass compared to bulk SOM. The isotopic signature of bulk SOM and PLFA from C4 cultivated soil decreases with increasing soil depth (−23.7‰ to −25.0‰ for bulk SOM and −18.3‰ to −23.3‰ for PLFA), which demonstrates decreasing influence of the isotopic signature of the new C4 vegetation with soil depth. In terms of soil microbial carbon sources this clearly shows a high percentage of C4 labelled and thus young plant carbon as microbial carbon source in topsoils. With increasing soil depth this percentage decreases and SOM is increasingly used as microbial carbon source. Among all PLFA that were associated to different microbial groups it could be observed that (a) depended on availability, Gram-negative and Gram-positive bacteria prefer plant-derived carbon as carbon source, however, (b) Gram-positive bacteria use more SOM-derived carbon sources while Gram-negative bacteria use more plant biomass. This tendency was observed in all three-depth intervals. However, our results also show that microorganisms maintain their preferred carbon sources independent on soil depth with an isotopic shift of 3–4‰ from 0–20 to 40–60 cm soil depth.
Given the potential for high amounts of variability in yield-affecting soil factors, some soil property or properties may serve as a basis for site-specific soil management. The objectives of this study were to determine the variability of selected soil properties and to determine the relationships between these soil properties and soybean [Glycine max (L.) Merr.] yield. Soil samples were collected from the center point of 0.5-ha grids in three fields and were analyzed for soil-test-extractable Ca, Mg, K, P, pH in water, and texture. Relative elevation, slope, aspect, and soybean yield were also determined at these points. Coefficient of variation, Pearson correlation coefficients, and principal component (PC) analysis coupled with stepwise regression were used to analyze the data. Two of the three fields had medium to high P and K values but low yields while the third field had low P and K values but relatively higher yields, suggesting factors other than P and K levels were affecting yield. Soil variability, with the exception of pH, was highest in the North field. Potassium in this field exhibited evidence of high amounts of small-scale spatial or temporal variability. Across all three fields, pH had the lowest amount of variability while variability in soil fertility varied from year to year and field to field. Fertility parameters had to be considered with other soil factors to determine their relationship to yield. Topography-yield relationships varied from field to field. Areas with higher clay content in all three fields had higher yield, suggesting clay could be used as a basis for site-specific soil management.
Irrigator-Pro is an expert system that prescribes irrigation for corn (Zea mays L.), cotton (Gossypium hirsutum L.) and peanut (Arachis hypogaea). We conducted an experiment in 2007 to evaluate Irrigator-Pro as a tool for variable rate irrigation of peanut using a site-specific center pivot irrigation system. Treatments were irrigation of whole plots based on the expert system, irrigation of individual soils within plots based on the expert system, irrigation of individual soils within plots based on tensiometers, and rainfed. Treatments were assigned to large plots with lengths that were 45° along the travel distance of the pivot and widths of 18.3 m along its span. Experimental design was randomized complete block and there were four replicates of each treatment. In-season data collection from all plots included normalized difference vegetative index (NDVI), canopy temperature, soil water potential, and cumulative water applied. Peanut yield was determined with a yield monitor. Irrigation applications began earlier in the season for plots managed with the expert system than for plots managed with tensiometers on all soils and total water applied was generally higher for both treatments using Irrigator Pro than for the tensiometer treatment. Rainfed yields were approximately 50% of irrigated yields. There were no differences between the three irrigation scheduling methods treatments for NDVI, canopy temperature, or yield. Using Irrigator Pro to prescribe water on a by-soil map unit basis did not reduce variability compared to using the model to prescribe irrigation on a whole plot basis.
Florida citrus trees must be irrigated to reach maximum production due to low soil water-holding capacity. In a highly urbanizing state with limited water resources, improved understanding of soil water uptake dynamics is needed to optimize irrigation volume and timing. The ob- jectives of this study were: (i) estimate mature citrus daily evapotrans- piration (ETc) from changes in soil water content (u), (ii) calculate citruscropcoefficients(Kc)fromETcandreferenceevapotranspiration (ETo), (iii) determine the relationship of soil water stress coefficient (Ks )t ou, and (iv) evaluate how ETc was related to root length density. In a 25-mo field study using mature 'Hamlin' orange (Citrus sinensis (L.) Osbeck) trees, ETc averaged 1137 mm yr 21 , and estimated Kc ranged between 0.7 and 1.1. Day of year explained more than 88% of the variation in Kc when u was near field capacity. The value of Ks decreased steadily from 1.0 at field capacity (u 5 0.072 cm 3 cm 23 )t o approximately 0.5 at 50% available soil water depletion (u 5 0.045 cm 3 cm 23 ). Roots were concentrated in the top 15 cm of soil under the tree canopy (0.71 to 1.16 cm roots cm 23 soil), where maximum soil water uptake was about 1.3 mm 3 mm root 21 d 21 at field capacity, decreasing quadratically as u decreased. Estimating daily plant water uptake and resulting soil water depletion based on root length density distribution would provide a reasonable basis for a citrus soil water balance model.
Phosphorus retention in sandy coastal plain soils can be low enough that surface water quality is jeopardized by agricultural P loadings. Objectives of this study were to: (i) determine if discrete morphological characteristics could effectively differentiate sandy soils with respect to P retention and (ii) test the efficacy of a rapid chemical assessment of relative P adsorption (single-point isotherm) for sandy materials. Soil samples from 96 surface and subsurface horizons of randomly selected Alaquod, Quartzipsamment, and Paleudult pedons were used. These pedons had previously been described, sampled, and characterized as part of the Florida Cooperative Soil Survey Program. Two groups of uncoated Quartzipsamments (<5% silt plus clay) were distinguished: those having 'clean' (coating-free) and 'slightly coated' grains. Eluvial horizons from Alaquods were also designated as clean because of a dominance of coating-free sand grains. Single-point P adsorption isotherms were obtained for all samples, and multipoint adsorption and desorption isotherms for 21 of the 96 samples. Single-point isotherms effectively arrayed sandy material with respect to relative P adsorption. Sand-grain coatings significantly enhanced P adsorption and resistance to desorption. All clean samples readily desorbed P regardless of origin or amount adsorbed. Thus clean sands pose a greater hazard for P leaching than sands with grain coatings. Clay content was closely related to P adsorption, but silt content was not. The P-retention distinction between clean and other Quartzipsamments is more marked than the 'uncoated vs. coated' USDA family criterion, the latter being confounded by the inclusion of silt content.
The clay mineralogy of Coastal Plain soils with sandy epipedons was studied with respect to taxonomic class, horizon, and the presence or absence of sand grain coatings. Relative estimates of clay-sized minerals were based on x-ray diffraction peak height ratios after calibration with standards and normalization. Results suggest that pedogenic processes that promote the destabilization of grain coatings (ie podzolization, chemical reduction) also promote the loss or redistribution of hydroxy-interlayered minerals associated with these coatings. -from Authors
An improved instrument for determining water potential by measuring dewpoint depression has been developed. Theoretical considerations show the instrument to have a sensitivity of 0.75 μV bar−1 and a change in sensitivity with temperature of 0.45% C−1. Without compensation for changes in Peltier cooling, sensitivity, and heat dissipation with temperature, errors of ±6% result from changing the temperature of the sensor between 20 and 50°C. Performance of the instrument appears to agree well with the theory developed for the technique.
Soil water tension was measured 22 times through a 50 day period, in a transect with 48 sets of tensiometers at five depths. General variability and temporal stability of this property as well as the spatio-temporal dependence of its variability were evaluated. The whole data set was divided into subsets according to location and time of measurement. The variability of soil water tension could be explained by a temporal trend (macro variation) and a random-like micro-variation. The tension values increased similarly for all locations along the measured period, and exhibited strong temporal stability, which was higher at the top layers. It was possible to identify and model spatio-temporal dependence of the variability. The spatial variograms for the sub-sets presented higher uncertainties, but were very similar to each other. Temporal dependence was clearly identified for transformed data, although larger differences were found between sub-sets. Very good agreement was found between the actual and the estimated data using the product–sum model for the spatio-temporal dependence. The procedure seems suitable for combining the spatial and temporal scales to expand the description of the variability dependence of soil hydraulic properties.
Analysis and interpretation of soil survey data are very important for effective management of agricultural fields. In this study, kriging and cokriging methods were applied to estimate the spatial distribution of soil properties from available large-scale survey data of Taiwan. The data were derived from soils in a 10-km2 area divided into 250 m × 250 m node intervals. The soil properties examined included the extractable P, Ca, Mg, and Fe contents, the sum of exchangeable bases (SEB), %sand, %silt, and %clay. The sum of exchangeable bases and particle-size distribution were regarded as the primary and auxiliary variables, respectively, in the cokriging procedure. The ratio of nugget to total variation was about 57 to 80%, indicating that the spatial correlation of the tested soil properties at the large scale was moderately (cross-)dependent. The estimated spatial distributions of the soil properties by kriging, under decreasing sampling densities, all correlated significantly (P < 0.1%) with those obtained from original data. Furthermore, with the over-sampled particle-size distribution, the overall estimation of SEB quality by cokriging was superior to that by kriging. The results suggested that by kriging and cokriging, the existing sampling density could be decreased under the large-scale sampling interval by nearly half and that sufficient spatial information about the soil properties could still be retained. The information obtained could be used to improve the long-term sampling designs of soil surveys in Taiwan. It also may be useful for identifying the appropriate sampling densities for these scales of soil surveys.
To provide reliable estimates for mapping soil properties for precision agriculture requires intensive sampling and costly laboratory analyses. If the spatial structure of ancillary data, such as yield, digital information from aerial photographs, and soil electrical conductivity (EC) measurements, relates to that of soil properties they could be used to guide the sampling intensity for soil surveys. Variograms of permanent soil properties at two study sites on different parent materials were compared with each other and with those for ancillary data. The ranges of spatial dependence identified by the variograms of both sets of properties are of similar orders of magnitude for each study site. Maps of the ancillary data appear to show similar patterns of variation and these seem to relate to those of the permanent properties of the soil. Correlation analysis has confirmed these relations. Maps of kriged estimates from sub-sampled data and the original variograms showed that the main patterns of variation were preserved when a sampling interval of less than half the average variogram range of ancillary data was used. Digital data from aerial photographs for different years and EC appear to show a more consistent relation with the soil properties than does yield. Aerial photographs, in particular those of bare soil, seem to be the most useful ancillary data and they are often cheaper to obtain than yield and EC data.
Florida growers have planted citrus groves at varying spacings to improve resource efficiency and to optimize fruit production
for maximum economic return. Four commercial groves with different row spacings and tree ages were scanned with a Durand-Wayland
ultrasonic system to measure and map tree volumes and to examine the effect of row spacings and tree ages on ultrasonic measurements.
The ultrasonically measured volumes (UVs) were compared with manually measured tree volumes (MVs) of 30 trees in each grove
to examine the performance of the ultrasonic system.
The ultrasonic system measured tree volumes reliably in different groves with an average prediction accuracy (APA) >90%, and
correlation with manual measurement of R2=0.95–0.99. Standard error of prediction and root mean square errors were relatively higher in widely spaced old groves than
closely spaced young groves. The ultrasonically sensed tree volume map showed substantial variation in canopy volumes (0–240m3tree−1) within the grove. Therefore, the use of ultrasonic systems is a better option to quantify and map each tree volume rapidly
(real-time) for planning site-specific management practices accurately in commercial groves and for estimating fruit yield.
Site-specific soil management can improve profitability and environmental protection of citrus groves having large spatial
variation in soil and tree characteristics. The objectives of this study were to identify soil factors causing tree performance
decline in a variable citrus grove, and to develop soil-specific management zones based on easily measured soil/tree parameters
for variable rate applications of appropriate soil amendments. Selected soil properties at six profile depths (0–1.5m), water
table depth, ground conductivity, leaf chlorophyll index, leaf nutrients and normalized difference vegetation index were compared
at 50 control points in a highly variable 45-ha citrus grove. Regression analysis indicated that 90% of spatial variation
in tree growth, assessed by NDVI, was explained by average soil profile properties of organic matter, color, near-infrared
reflectance, soil solution electrical conductivity, ground conductivity and water table depth. Regression results also showed
that soil samples at the surface only (0–150mm) explained 78% of NDVI variability with NIR and DTPA-extractable Fe. Excessive
available copper in low soil organic matter areas of the grove apparently induced Fe deficiency, causing chlorotic foliage
disorders and stunted tree growth. The semivariograms of selected variables showed a strong spatial dependence with large
ranges (varied from 230m to 255m). This grove can be divided into different management zones on the basis of easily measured
NDVI and/or soil organic matter for variable rate application of dolomite and chelated iron to improve tree performance.
Water content is a fundamental property affecting plant growth, transport and transformation of soil nutrients, and water and energy budgets in the soil–plant system. Its spatial variation has important implications for all these processes. In this study, spatial variation in soil water contents was evaluated at pressures of −0.033 (θ−0.033 MPa), −0.10 (θ−0.10 MPa), and −1.50 MPa (θ−1.50 MPa) at the soil depth from surface to the lower and of a plow layer in a Typic Ustifluvent in an 8.5 ha level area (1–2% slope). Topsoil (0.0–0.30 m) and subsoil (0.31–0.60 m) were sampled based on a regular grid spacing of 25 m-by-25 m. Variables θ−0.033 MPa, θ−0.10 MPa, and θ−1.50 MPa were measured with a pressure plate apparatus. Results were evaluated and compared with conventional statistics and geostatistics. Coefficient of variation (CV) for soil water content increased as soil water pressure decreased in both topsoil and subsoil. Semivariance analysis and ordinary block kriging-produced surface maps for θ−0.033 MPa, θ−0.10 MPa, and θ−1.50 MPa showed that topsoil and subsoil were similar in spatial variation of water content at all three corresponding soil water pressures. The geostatistical range values were between 298 m (θ−0.10 MPa in topsoil) and 486 m (θ−0.10 MPa in subsoil), both indicating a strong spatial dependence of soil water content. The semivariograms for θ−0.033 MPa, θ−0.10 MPa, and θ−1.50 MPa were similar in shape in both topsoil and subsoil. Cross-dependence between sand content and soil water contents showed that soil texture controlled the spatial variation of water contents at the site, at all depths and pressures evaluated.
The reflectance spectra (400–2500 nm) and color attributes of samples from the major horizons of six soil classes with broad distribution in Brazil were analyzed for intrinsic relationships (principal components analysis) as well as for correlations with chemical components. The objective was to detect the spectral changes with depth that are related to the soil chemical properties, thus improving the pedological characterization of the soil profile and vertically extending the links between topsoil properties and remote sensing data. The albedo (first principal component) differentiated the studied soil profiles in all horizons and was inversely related to FeA (total iron), Ti02, and Al203 contents. The spectral variability within each soil profile was associated with changes in the shape (slope) of the spectra (second principal component). Such changes produced ratio values between longer and shorter wavelengths that tended to decrease with depth because of the increasing quantities of clay components, and the interplay between iron and carbon, respectively. Thus, in each profile, the albedo was generally preserved, but the shape of the spectra was not, except for soils with very low albedo. Hematite-rich soils were distinguished from goethite-rich soils by the redness index, which was estimated from the three color parameters: dominant wavelength, purity, and luminance. The results suggested that the use of spectroradiometers to measure reflectance of samples from several horizons can expedite soil surveys, and also optimize laboratory analysis for soil chemical evaluation. Also, they can be important to characterize the relative distribution of some soil constituents in large unvegetated areas.
The temporal stability of soil water content patterns may have profound implications for precision agriculture in general and water management in particular. Spatio-temporal variability in soil water was assessed over four fields in a two-year potato (Solanum tuberosum L.) and barley (Hordeum vulgare L.) rotation to determine the potato yield implications and the potential for precision water management based on a stable spatial pattern of soil water. A hammer-driven time domain reflectometry probe was used to measure soil water content repeatedly along 10 transects. Irrigated, un-irrigated, and late irrigated treatments were employed. The temporally stable soil water pattern was mapped and compared with elevation and soil particle size classifications. A temporal stability model explained 47% of the observed variability in soil water content. An additional 20% of the variability was attributed to random measurement error. Calibrated in 2002, the model predicted water content (root mean square error of 0.05 m3 m−3) along transects in 2003 from a single measurement at the field edge. Field-scale trends and extended (>100 m) wet and dry segments were observed along transects. Coarser particle size class soils were generally drier. Potato yield increased linearly with water content in un-irrigated areas. Yield was comparatively high in the drier areas for the irrigated treatment but was highly variable and frequently poor in the wetter areas. For the late-irrigated treatment, a strong yield response to added water was evident in the dry areas; however, the yield response was neutral to negative in the wetter areas. Knowledge of the underlying stable soil water distribution could provide a useful basis for precision water management.
Ultrasonic transducers have been used for the digital control of agrochemical application rates in sprayers and fertilizer spreaders of tree crops for about 16 years. The early systems were developed before the advent of commercial DGPS receivers and relied on real-time tree canopy sensing and agrochemical rate adjustment according to the size of canopy detected. The widespread availability of fast and accurate DGPS service since 2000 and the rapid evolution of laptop computing power, has offered new opportunities for enhanced processing and spatial mapping of ultrasonically sensed orchard or grove data. The objective of this study was to develop and evaluate a Windows software application for a 10-transducer Durand-Wayland ultrasonic orchard measurement array and Trimble AgGPS132 DGPS that would allow real-time sensing, monitoring, calculation, storage and mapping of citrus tree canopy volume and height. Program code was written in Delphi 5.0 for Windows. Four main program windows displayed (1) real-time instrument status of ground speed, transducer outputs in a bar chart, canopy height, cross-sectional canopy area, and DGPS signal status; (2) database; (3) SQL filter pages to review and sort the acquired spatial data; and (4) canopy volumes and coordinates for individual trees in the citrus grove. The data collected with this automated system were compared with manually measured size data of 30 trees to estimate accuracy, and a grove of 376 citrus trees was surveyed twice with the system to estimate repeatability. Results showed no significant differences between ultrasonically and manually measured tree sizes ranging in height from 2.1 to 4.3 m (R2 = 0.940***; RMSE = 0.157 m) and in volume from 6.3 to 54.0 m3 tree−1 (R2 = 0.944***; RMSE = 3.14 m3 tree−1). The system located tree positions for GIS mapping purposes within 1.37 m, 95% of the time.
High-resolution digital soil mapping for applications like precision agriculture requires the collection of good-quality high spatial resolution information. Conventional soil analysis is expensive, time consuming and laborious. The development of proximal soil sensors to lessen the need for or to complement conventional soil analysis is important. Although proximal sensing may produce results that are not as accurate as conventional laboratory analysis, they allow for the collection of larger amounts of data using simpler, cheaper and less laborious techniques. This forms the rationale for proximal soil sensing. This article deals with indirect measurements of soil organic carbon (OC) and iron (Fe) contents using soil colour as the proxy. Measurements of soil colour were made using a digital camera. The RGB tristimuli were transformed to variables from other colour space models and a redness index (RI) and these were used to derive pedotransfer functions for soil OC and Fe. Predictions using univariate as well as full factorial regressions (FFR) of these tristimuli were compared to predictions using visible–near infrared (vis–NIR: 400–1100 nm) spectra with partial least squares regression (PLSR) and a reduced number of wavelengths selected using the variable importance for projection (VIP) with PLSR (VIP-PLSR). For predictions of soil OC content, the VIP-PLSR technique produced predictions with R2adj. and RMSE values of 0.91 and 0.46%. These were only very slightly better than predictions by an FFR of the CIELa*b* tristimuli (R2adj. of 0.91 and an RMSE of 0.48%) and PLSR (R2adj. of 0.91 and an RMSE of 0.50%). Predictions using the logarithmic regression of the CIEv* variable were least accurate with R2adj. and RMSE values of 0.88 and 0.56%. For predictions of Fe, an FFR of the CIELc*h* tristimuli produced an R2adj. of 0.71 and an RMSE of 0.068%, which was better than those obtained by PLSR (R2adj. values of 0.64 and RMSE 0.074%) and VIP-PLSR (R2adj. of 0.64 and 0.075%). Predictions of Fe using the logarithmic regression of the RI produced the least accurate results with R2adj. and RMSE values of 0.56 and 0.081%. In this study, we showed that a digital camera can be used for fast, accurate and non-destructive measurements of soil colour and predictions of soil OC and Fe contents in Brittany, France.
Common soils used for citrus production in Florida
- T A Obreza
- M E Collins
Obreza, T. A., and M. E. Collins. 2002. Common soils used for citrus
production in Florida. Fla. Coop. Ext. Serv. SL193. Univ. of Fla. Inst. of
Food and Agric. Sci., Gainesville. Available at edis.ifas. ufl.edu/SS403
(verified 12 Feb. 2009).
Eight years of root injury from water table fluctuations.
- Ford
Ford, H. 1972. Eight years of root injury from water table fluctuations. Proc.
Fla. State Hortic. Soc. 85:65Y68.
Water and Florida citrus: Use, regulation, irrigation, systems, and management
- B J Boman
- L Parsons
- T A Obreza
- E Stover
Boman, B. J., L. Parsons, T. A. Obreza, and E. Stover. 2002. Citrus water use
and irrigation scheduling. In: Water and Florida citrus: Use, regulation,
irrigation, systems, and management. B. J. Boman (ed.). Univ. of Fla.
Inst. of Food and Agric. Sci., Gainesville, pp. 175Y191.