Open access peer-reviewed chapter

Improving Nitrogen and Phosphorus Efficiency for Optimal Plant Growth and Yield

Written By

Lakesh K. Sharma, Ahmed A. Zaeen, Sukhwinder K. Bali and James D. Dwyer

Submitted: 07 June 2017 Reviewed: 06 November 2017 Published: 20 December 2017

DOI: 10.5772/intechopen.72214

From the Edited Volume

New Visions in Plant Science

Edited by Özge Çelik

Chapter metrics overview

1,741 Chapter Downloads

View Full Metrics


Nitrogen (N) and phosphorus (P) are the most important nutrients for crop production. The N contributes to the structural component, generic, and metabolic compounds in a plant cell. N is mainly an essential part of chlorophyll, the compound in the plants that is responsible for photosynthesis process. The plant can get its available nitrogen from the soil by mineralizing organic materials, fixed-N by bacteria, and nitrogen can be released from plant as residue decay. Soil minerals do not release an enough amount of nitrogen to support plant; therefore, fertilizing is necessary for high production. Phosphorous contributes in the complex of the nucleic acid structure of plants. The nucleic acid is essential in protein synthesis regulation; therefore, P is important in cell division and development of new plant tissue. P is one of the 17 essential nutrients for plant growth and related to complex energy transformations in the plant. In the past, growth in production and productivity of crops relied heavily on high-dose application of N and P fertilizers. However, continue adding those chemical fertilizers over time has bad results in diminishing returns regarding no improvement in crop productivity. Applying high doses of chemical fertilizers is a major factor in the climate change in terms of nitrous oxide gas as one of the greenhouse gas and eutrophication that happens because of P pollution in water streams. This chapter speaks about N and P use efficiency and how they are necessary for plant and environment.


  • nitrogen use efficiency
  • phosphorus
  • yield
  • phosphorus
  • and agriculture

1. Introduction

Crop nitrogen use efficiency (NUE) in world cereal production has been estimated to be inefficient with only an average of 33% of fertilized N being recovered during production [1]. Denitrification caused by excessive amount of rainfall and nitrate leaching are the leading causes of N loss in the soil. Loss of N to ground and surface water has resulted from ongoing fertilizer management processes in the Corn Belt region of the USA [2, 3, 4]. Insufficient coordination between N applications and the requirement of the crops, applying excessive amounts of N before planting as an example, has been cited as one of the primary reasons for the low NUE of ongoing fertilizer management processes [5, 6]. According to the USDA, for the last two decades, close to 150 kg ha−1 has been the usual N application amount in the Corn Belt region of the USA [7], and around 75% of N applications, including the previous fall, was applied before planting [6]. The usual consumption rate of mineral N in the soil for corn for the first 3 weeks after emerging from the ground is less than 0.5 kg ha−1 a day. N consumption then increases exponentially to around 3.7 kg ha−1 a day after the first 3 weeks until the corn plant reaches the tasselling stage [8]. A recorded consumption of 6 kg ha−1 a day has been the highest rate recorded (J.S. Schepers, personal communication). Early season leaching of pre-plant N applications to areas below the crop-rooting zone before the plant reaches its peak N uptake phase is reliant on present soil and weather conditions [9]. The introduction of high amount of available N in the soil profile is risky as it is in danger of being lost to leaching, and the plant can take up denitrification over a period of several weeks before it during its active uptake phase. As the rate of N fertilizer applied in a single pre-plant N application increases, the efficiency of the N application will decrease [10]. However, NUE has been observed to increase when applied in-season as opposed to being applied pre-planting [11]. It has been suggested [12] that N should be applied when required by the crops to increase NUE. Farmer support of the practice of applying N in-season in the corn growing region is low, despite the improved NUE application strategies being supported by ample research [13]. Farmers are likely rejecting the practice of in-season applications in favor of the simpler strategy of applying pre-plant N applications due to the cost and practicality of the labor and equipment associated with in-season applications [6]. Despite the presence of spatial and temporary variables in different landscapes, N is applied in a uniform pattern onto the landscape, ignoring the variables and studies that have proven the economic and environmental benefits of spatially variable N applications, contributing to low NUE in the corn regions [14]. Due to the spatial variabilities in the interior of fields, different sections have varying levels of soil N content, different rates of crop N uptake, and different N responses [15]. Therefore, it is a risk to apply large uniform N pre-plant applications in ignorance of this variability within the field as N in the over-applied areas, or at-risk soils could be lost to environmental factors. Over application of N recommended by out of date N recommendations has been cited as another source of low NUE. Analysis of nitrate in the soil before planting and yield expectations is used as the basis for determining the recommended rate of N application for corn in North Dakota. However, corn will only benefit from these recommendations if they follow a rotation and if manure had been recently used [16]. About 30–60% N loss [17], sometimes N losses could go beyond 70% [18], have been proven by a handful of studies. In North Dakota, the different regional climates, the experience of farmers, and cultural practices are not taken into consideration when developing N recommendations for corn. N availability for corn and the rate of mineralization for residues and organic matter in the soil are dependent on regional climate variables such as the temperature and precipitation. N loss via leaching, the rate of N mineralization, or from denitrification caused by periods of excessive precipitation is affected by different soils in a field with variable traits such as soil texture, pH levels, and organic matter content (OM).

In regard to phosphor the green revolution that followed World War 2, the use of chemical fertilizers increased to increase yields but at the expense of the environment [19]. The common usage of P fertilizers has led to P pollution in the waterways of the USA due to lack of preventative measures to prevent the erosion of P in bodies of water. As a result, wildlife and the environment are at risk. Studies carried out by the Environmental Protection Agency (EPA) has reported and confirmed the presence of P pollution in the Northeastern USA [20]. The application rates of major fertilizers containing N, P, and K have increased in all crops grown in the USA [21]. After 1989, consumption of P temporarily decreased after reports of P erosion in lakes and rivers were released [22] until consumption rates started to increase again in 2010, despite government regulations. Historically, the potato industry in the Northeastern USA was the primary source of P pollution. P fertilizer was applied when not needed, and the potato crops would only recover low amounts of P. It was recently discovered that P concentrations are increasing in lakes and rivers in the Northeast [20], raising concerns about the amount of P currently applied in the agricultural industry in the Northeast. In comparison to potato cultivation in other major potato growing regions, it was found that state-wide P consumption in Maine has declined. This could be attributed to a drop in land dedicated to potato growth over the last 20 years. Despite this, average yield has increased, with the last 2 years of potato production reaching record highs and growing still every year despite declines in P application. However, it was found that this decrease in P application had a nonsignificant reduction. Despite decreasing from 198 to 182 kgha−1 (Figure 1), it is still very high. When low levels of P are found, the University of Maine Soil Testing Laboratory recommends 50 kgha−1. In the agricultural industry, potato growers apply the maximum amount of fertilizers, making them a prime suspect of being the principal source of P pollution. P pollution in the St. Johns River in Florida has been directly linked to P loss in potato cultivation [23]. The United States Geological Survey found that 71% of the cropland in the USA had at least one of the four contaminations responsible for water quality degradation. Dissolved nitrates, fecal coliform bacteria, suspended sediments, and total P are the four contaminants. A total of 20,000 ha of agricultural land is dedicated to potato cultivation which has a production rate of 44 kg ha−1 [21]. The EPA in Maine has raised concerns over the nonpoint source of P that is increasing P pollution in water bodies; 14,407 ha of land has been impaired by P pollution [24]; 3350 t, with an average of 182 kgha−1, of P, was applied to potatoes in 2014. Potatoes have a low P uptake at an average of ~28 kgha−1 [25]. Only 10% of P applications are available to potatoes, resulting in a lowered efficiency and loss of P to erosion [26].

Figure 1.

The trend of average P (kg ha−1) used under potato in the key potato-growing states. The polynomial regression analysis was utilized to a potential relationship between years and P use. USDA, National Agricultural Statistics Service, and New England Ag Statistics.

In Maine, out of ~3600 t of P that was applied, only 612 t was taken up by potatoes with only 1.12 kg ha−1 of it mineralized (fertility and fertilizer book). In Maine, there is P efficiency of ~17%, with applied P only has an efficiency of 16%. P can enter the water via run-in, runoff, or leaching. Water quality degradation is primarily caused by P pollution [66]. Soil runoff and leaching cause an estimated 10–40% of P pollution from agricultural land [46]. Severe eutrophication of water can occur if P concentrations exceed 0.02 ppm [27, 28]. Need for P management to mitigate eutrophication was brought to attention after high levels of P were recorded in the river and lakes of Maine [29]. Despite growers, receiving specific recommendations from soil testing, P pollution is still rising; suggesting growers are still applying excessive P. Because of different parent material, various soil types have different abilities when it comes to releasing available P in soil. Available Ca, Al, and Fe affect the soil ability to hold moisture and the availability of P in soil [30]. There is no clear answer to P requirements, especially in the case of potatoes, despite several studies had been carried out since the 1940s [30, 31, 32, 33, 34, 35, 36, 37].


2. Soil and plant analysis

A few studies [38, 39] with the goal of applying the amount of N needed with spatial variables in mind, recommended marking spatial variable management zones (MZ) as part of a soil-based method for variable N applications and for bettering NUE. MZs are defined here as areas within a field with homogeneous characteristics in regard to soil conditions and landscapes. Traits within an MZ such as similar crop yields, electrical conductivity (EC), and producer-defined areas make zones homogenous [40]. Impact of fertilizers on the environment, input-use efficiency, and yield potential are some of the similarities that the attributes have. To define borders for MZ’s, a range of methods were put forth by researchers as viable approaches. Geo-referenced data layers (i.e., soil color, electrical conductivity, yield, and topography) are statistically clustered or combined using geospatial statistical analyses within geographic information systems (GIS) to delineate zone boundaries [41]. Soil mapping units [42], remote sensing [41, 43], topography [44], yield maps, and soil EC [45] have been successfully used to delineate the MZ. Static and inconsistent (because of effects of temporal variations on yields) sources is what the MZ relies on for much of the delineation [14]. Because of their static and inconsistent nature, they are likely inappropriate in accounting for all the variability of N requirement within a field.


3. Use of tissue analysis for N management

N concentrations in critical states can be used as an indicator of crop N status. Critical N is the minimum amount of N required to provide the maximum amount of growth at a particular time [46]. The concentration of N is high when the corn plant first starts to grow and develop but eventually decreases as the corn plant matures. Critical N dilution is the graphical depiction of this process [47]. The ratio of actual N in the plant to the critical N set by experiments in the past is called the N nutrition index (NNIN) [58]. The value of NNI more or less than 1 relates to a nonlimiting growth or deficient situation of the crop, respectively. Wheat (Triticum aestivum L.) [48], grain sorghum (Sorghum bicolor L.) [49], rapeseed (Brassica napus L.) [50], Rice (Oryza sativa L.) [51], and grasses [52] have been used in the NNI approach. Suggested to be the result of competition between corn plants [53] at the early stages of growth, the advent of critical N does not contribute to a solid estimate of crop N status [54]. In what is referred to occasionally as “dilution,” an increase in crop biomass will lead to a decrease in N concentrations [53].


4. Spatial variation

Variations in traits such as soils, soil management techniques, production history, movement of water and nutrients, and spatial variation are to classify types of fields for commercial corn production. Because of the spatial variations, changes in N requirements of plants, vulnerability to stress, and productive plant variations across a landscape can occur. Slope changes in the interior of landscape and soil depth and drainage can have huge impacts on grain yield variability and corn grain yield, respectively [55]. Because of the flow of water and deposition of soils containing clay and organic matter into depressions and foot slopes in the landscape in areas of commercial corn production, these landscape features have a high level of N fertility in comparison to the rest of the landscape. The downward shift of these nutrient-rich materials has a noticeable effect on the soil in the upper landscape positions as they have been found to be low in OM [56]. This downward movement also affects P and potassium (K) concentrations as they can be found in higher levels of availability in footholds and depressions. High levels of crop production history naturally lead to higher rates of crop removing, potentially resulting in P and K that are lower than anticipated [44]. This suggests that unlike OM, the redistribution and deposition of soil P and K may not be as strongly related to variations in slope, suggesting a resistance to movement [62]. A loss in growth and yield could be a reaction to crop stress. In some different landscapes, seasonal weather conditions exert an influence on crops. Variations in yield caused by differences in landscape position during dry or wet growing seasons are amplified. High levels of OM or a high water holding capacity can increase the resilience of a landscape to the extreme conditions caused by droughts in comparison to upland areas [56]. Yields can drop if large amount of precipitation causes ponding to occur in depressions in the landscape [56].


5. Fertilizer placement and timing

N application can guarantee the high level of N availability that the crops with high NUE need are required. Injected UAN (urea-ammonium nitrate solutions) has better yield results than the yields that are a result of broadcasting UAN, especially on landscapes with surface residue [58]. Utilizing broadcast UAN applications can result in N loss a variety of ways, including the volatilization of ammonia in the urea portion of the UAN and N immobilization with the surface residue of the landscape [59]. Because of this, the application of fertilizer beneath the surface of the soil may be more efficient. The V7 growth stage in modern corn hybrids accounts for around 15% of total N uptake, as well as 5% of the total dry matter build up [60]; 40% of the total dry matter build up and 60% of the total N uptake have happened by the time the corn plant reaches its silking phase. This means that the period of 30 days between the V7 stage and the VT stage accounts for 40% of the corn plants total N uptake. With no risk of a reduced yield, N synchronization can be enhanced by holding off on in-season applications of N until the V7 stage [61, 62]. At 28 locations with a variety of soils in which timing of N fertilizer application was the experimental variable experimented. At the planting stage, V7 stage, V14 stage, and the silking stage, a single application of ammonium nitrate was put down at a rate of 180 kg N ha−1. At most, of the sites, there was a positive response in corn yield to the N fertilizer. Out of all 28-study sites, only one site experienced slight yield loss when the application of N was held off until the V14 stage. With delayed N applications, there is a possibility that the climate could affect the relative risk of yield loss. Maximum yield was achieved in many locations during dry years by withholding N surface applications until the V14 stage in water-stressed corn. However, the amendment of many of the study sites with animal manure, the use of soybeans as an earlier crop, and the implementation of a variety of tillage systems across the sites have complicated this study. Two locations will be included where corn sites were tilled with the application of manure. The severity and timing of N deficiency due to N mineralization rates and soil N-supplies were affected by the previous crops that were used, manure management, and tillage management. In contradiction to the conclusions in [57, 62], unchangeable yield loss was experienced after N was applied during or after the V6 stage at one of the sites, implicating that at the location, N availability has to be sufficient before side dressing to guarantee that the maximum yield is achieved. There was a decrease in the yield response of the grain to N as N deficiency decreased the longer delay in side-dress N applications, implying that the N deficiency levels were positively interacting with the corn yield at the time of N application.


6. Leaf area index

The ration of the leaf surface area to the ground surface area is called the Leaf Area Index (LAI) [66] and is a direct depiction of the photosynthetic capacity of vegetation [63]. LAI has a direct link to the productivity of vegetation in some species and communities; however, for some, the link between productivity and LAI is dependent on variables such as the canopy extinction coefficient, light, NUE, and the amount of light cut off by the canopy top [64]. C4 plants growing in thick stands having higher NUE and higher leaf area production than C3 plants that are in the same environment is an example of this [64]. Remote sensing has been used to develop approaches for determining LAI. Inversions of canopy radiative transfer models [65] and the empirical relationships between spectral vegetation indices and LAI [66]. A short-coming of algorithms based on vegetation indices is the difficulty in extrapolating their results to larger regions or different canopy types [67]. Vegetation index predictions are often confounded with atmospheric and background effects, canopy architecture, solar-target-sensor geometry, and to lack of spectrum difference when measuring moderate to high levels of LAI [65].


7. Environmental interaction

Environmental stress is the primary influence on crop productivity. Corn yields can drop up to and over 70% under negative environmental conditions [68]. Corn hybrids created by breed programs today have shown the ability to withstand environmental stresses, as well as higher plant densities [69]. It is important to note that only 50% of the increases in yield during the modern age of breeding can be attributed to genetic improvements in corn [68], as the other half is a result of better management practices. Corn yield results drop sharply when available soil moisture at depths of 40 cm drops below 25% [70]. Yield can be doubled, however, with the introduction of water via irrigation. During the silking stage, barren ears can occur during drought conditions [71]. Crop yield can drop up to 20% if drought conditions occur after the silking stage [72]. Another study found that moisture stress before silking can cause yield to drop up to 25% and can drop 50% if moisture stress is present during the silking stage [73]. There can be a 21% reduction in yield if soil moisture stress is still present after silking [73]. Moisture stress can cause a plethora of negative symptoms in corn plants such as reduced grain yield, reduced cob length, reduced leaf area, and reduced stem elongation [73]. High temperatures are another source of crop stress. At temperatures of 45° Celsius (113° F), the rate of photosynthesis in corn can be restricted up to 95% during these extreme conditions [74]. Tassel initiation can be postponed by corn stress caused by excessive heat [74]. An increase in high air temperatures to around 32–27°C from a more moderate range of 22–17°C can, respectively, reduce the rate of photosynthesis and the rate of total biomass production by 11 and 32% [75].


8. Spectral response

The spectral properties of leaves can change because environmental stresses [76] observed similar changes in spectral responses across multiple species with changes in plant competition, disease interaction, insufficient ectomycorrhizal infection, senescence, herbicide damage, increased ozone, dehydration, and presence of saline soils. The basis of these responses was that stress reduces chlorophyll content. In regard to the red and green spectrums, chlorophyll α has a low rate of absorbency. Even small changes in chlorophyll concentration can cause increased reflection at these wavelengths [77]. Zhao et al. [78] found more than a 60% reduction in chlorophyll A in leaves after 42 days of emergence, resulting in increased reflectance near 550 and 710 nm. Stress caused by deficiencies in micronutrients is similar to stress caused by N deficiencies. After an evaluation of deficiencies of Fe, S, Mg, and MN, Masoni et al. [79] discovered that decreasing the concentrations of micronutrients caused a decline in chlorophyll concentrations in corn leaves. Chlorophyll a concentrations were 22% less, when Fe, Mg, and Mn were deficient in comparison to unstressed plants. Chlorophyll α concentrations dropped up to 50% when there are deficiencies in sulfur. Because of the decreased concentrations of chlorophyll, there is a decrease in light absorbency, increasing reflectance to around 555 nm and 700 nm [79].


9. Use of spectral properties of plants

The total photosynthetic pigment in a leaf is linked directly to the total amount of solar energy that is absorbed the leaf surface [80]. The photosynthetic potential is directly related to chlorophyll content [81]. Total chlorophyll content changes in response to plant developmental stages or stress. Therefore, measuring chlorophyll content can be a tool for evaluating the physiological health of plants. Gitelson and Merzlyak [82] assessed vegetative indices of a variety of species, leading to a conclusion that the absorption and reflectance of light in the 530–6300 nm and near 700 nm wavelengths were related to chlorophyll content. The light reflectance of plant tissue at the specific wavelengths of 700 and 550 nm was highly correlated with chlorophyll content (r2 > 0.97). Wavelengths in the near infrared spectrum (750–900 nm) were relatively insensitive to chlorophyll content. The ratio of the 750 nm light reflectance to the 550 nm wavelength was used to create an index to be used for predictive measurements [82]. A similar study was conducted on corn [83]. Individual leaves were sampled every 2 weeks. To determine the total chlorophyll content (r2 > 0.94), the red wavelength was used. Crop reflectance is defined as the ratio of the amount of incident light as the denominator to the amount of light reflected back as the numerator [9]. In-season N management was done with active optical sensors by [13] and in the winter wheat fields. During the approach, the NDVI was divided by the growing degree days accrued between planting and sensing. This value was defined as the in-season estimate of yield (INSEY) and was related to the growth rate of the plant. In comparison to solitary sensor readings, INSEY is a better indicator of plant health [13]. To be valid when just using the instrument reading, readings must be done at the same growth stage every year. For developing improved relationships for readings done within a year and over a period of years, time differences between seasons are normalized by INSEY during readings. Blue and red spectra have weaker penetrative properties than green and red-edge spectra when it comes to the capability of light to penetrate into leaves. Eighty percent and higher incident leaf absorption occur in the range of 400–700 nm during the process of photosynthesis [84]. There is a set or range of values, which are not high and narrow in range, in the absorption coefficient in the green and red-edge spectra called saturation, allowing the light in these spectra to be more responsive to changes in the chlorophyll content, especially more than any other wavelength [85]. The ability of leaves of some plant species to absorb light from the visible spectrum increased as plant leaves change their tint from a lighter green to a darker green [80]. The minimum rate of absorption by chlorophyll is 550 nm, while the maximum is 680 nm. Radiation absorption is also influenced by the angle of incident light on the leaf. The comparison of the amount of red light to the amount of near-infrared light absorbed underneath the plant canopy is the most commonly used method of spectral plant analysis [86]. As LAI increases, the amount of light absorbed in the red spectrum and light reflected in the near-infrared [86] increases. LAI could indirectly determine by using a light ratio (675/800) not over but beneath the canopy of the forest. Despite being able to estimate LAI remotely, the authors came to the conclusion that measurement accuracy could be affected by environmental conditions like the angle of incident sunlight and cloud cover. In the evaluation of grass canopies, like approaches have been used [87]. The absorption rate of incident light in spectra (630–690 nm) increases when green biomass increases. Irradiance near the infrared spectrum is defined as lack of absorption or reflection of chlorophyll [87]. Several ratios of the red and near-infrared spectrum are related to the mass of plant greenness [87]. There is a group of ratios that are responsive to physiological parameters and environmental parameters called vegetative indices. Common spectral vegetative indices include chlorophyll indices (Clgreen = (RNIR/R green)−1) for estimating chlorophyll content [88] and the soil-adjusted vegetation index (SAVI = (RNIRRred) (I + L)/(RNIR + Rred + L)) for LAI estimation [89]. The normalized vegetative index (NDVI) is a widely used vegetative index [13]. Chlorophyll a and b are the most active in the process of photosynthesis, absorbing light (in the red and the blue spectra) and reflecting green spectra [90]. There is more reflectance in the near infrared (700-1400 nm) spectrum of light [90]. Biomass measurements and nutrient deficiencies can be found using these traits in plant leaves [91]. Specialists and researchers prefer to use the Normalized Difference Vegetation Index (NDVI) when they are predicting plant biomasses [91]. NDVI is the ratio of in the red wavelength to NIR light [92]. NDVI = (NIR − red)/(NIR + red), where “NIR” is the reflectance in the near infrared region of the spectrum and “red” is the reflectance in the red region of the spectrum. Because of its usage of the two light spectra and the easiness of its calculations, researchers embrace the NDVI [93].


10. Estimation of vegetative indexes

10.1. Nutrient status

After developing active sensors, the impact of factors such as environmental constraints and ambient light on sampling has been reduced. A plethora of techniques such as destructive plant analysis and soil testing have been used in the past to determine the nutritional status of plants, but recent developments have introduced nondestructive sensors as an alternative [94]. Much of the work done with nondestructive sensors is used to determine the N status of crops [95]. Leaf photosynthesis is negatively impacted and reduced when there is a deficiency of N. Low N availability in corn affects overall production by reducing all components of the corn yield such as kernel dry weight [96]. Crucial for determining the N status of corn, there is a group of wavelengths associated with the N status of corn [97]. Shanahan et al. [98] proposed using NDVI and Green NDVI (GNDVI). In the GDVI, the two spectrums used were NIR, and the other was in the range of 500–600 nm. The light in this spectrum is green; therefore, it was named as green NDVI. The basis for their finding was an experiment of four corn hybrids under irrigation using 5 N rates. Active-optical sensors emitted light in four bands: blue (460 nm), green (555 nm), red (680 nm), and NIR (800 nm). Differences in NDVI were related to N rate and sampling date. N was correlated to increased chlorophyll content (R2 > 0.96). Also, Ref. [99] found that NDVI could be used successfully in evaluating growth and development of small grains.

10.2. Yield estimation

Kitchen and Goulding [104] found it hard to use sensors to establish estimations of yield, even with the established links between green leaf biomass and vegetative indices. In wheat, sensor readings at Feekes growth stage 5 tended to be more correlated with grain yield than any other stage of development [100]. Raun et al. [13] found that sensor-based estimated grain yields were able to explain 83% of grain yield variability. The relationship between sensor reading and yield may be variable over space and time [101]. Inconsistencies have been found in hybrid variations, sampling, seasonal changes, dates, N fertilization, and spatial differences, when determining an estimation of yield [101].

10.3. Nitrogen management using site-specific technologies

Destruction of an area or object is avoided when data are measured via remote sensing methods such as the use of satellite imagery, ground-based active-optical sensors, ground-based reflective sensors, leaf chlorophyll sensors, and aerial imagery or photography [100]. In the agricultural industry, the estimation of land use, land cover, and crop biomass has been done using remote sensing [102]. The in-season status of spatial crop N is now determined using remote sensing [91]. The link between spectral reflectance, crop N status, and chlorophyll content has been better developed as a result of a few studies [91]. Canopy reflectance/color photography, SPAD®(Konica-Minota Americans, Ramsey, NJ), and chlorophyll meters were some of the very first methods of remote sensing used in studies [103]. A plethora of geospatial technologies have been accessible since the mid-1990s for the agricultural market and industry. Crop reflectance, color photography, and GBAO sensors have been successfully used to measure spatial variability in crop canopies.

10.4. Use of sensors and NDVI

When preparing N applications, many farmers use factors such as previous crop, soil management, and soil drainage properties when determining the optimal N rate. However, they commonly do not use in-season tools during these determinations [85]. Farmers apply excessive amounts of N fertilizer in an attempt to guarantee that they will get maximum yield in their fields [105]. Excessive N application leads to problems such as the loss of unused N in the form of nitrate to surface and groundwater, causing environmental problems [105]. Use of proximal plant canopy sensors offers an opportunity for corn producers to adjust N requirement according to the crop requirement. The optimal N rate for any variety of corn and fields is challenging to determine. In order to diminish environmental impact of excess nitrate originating from the production of corn, Schepers et al. [106] suggested that sensing tools to determine to exact amount of N needed instead of applying excessive amounts of N. By estimating crop N status against a standard, the SPAD chlorophyll meter measurement method can help farmers apply N as needed. As a result, farmers still get their maximum yields while using less N fertilizer [107]. However, the SPAD approach requires a laborious process of compiling data from a large number of leaves and then finding a way to standardize N deficient plants from ones that are not deficient with a more significant number of varieties. Active optical sensors are utilized by the SPAD chlorophyll meter to measure two different wavelengths of light (NIR and RED) through the plant leaf. Then, as determined by the manufacturer, a value is computed. The SPAD chlorophyll meter assesses the status of N/nutrition of the plant by analyzing leaf tissue in a nondestructive manner. A positive correlation between chlorophyll content and SPAD chlorophyll meter readings has been proven in multiple studies [108]. However, measurements are done on a one-leaf-at-a-time basis, requiring large of amounts of time to take multiple readings in a field with the SPAD chlorophyll meter. Bullock and Anderson [109] discovered a lack of correlation between V7 stage yields and chlorophyll. An improved correlation between yield and N concentrations in leaves, however, was found at the more advanced stage of R1 and R4. Chlorophyll meter readings at the R1/R4 stages were more closely linked to grain yields than they were to N concentrations in leaves. Correlation coefficients between leaf N and meter readings in the early stages of corn were initially positive (r2 = 0.23), but as the crops grew, there was a drop in value (r2 = 0.20). N recommendations for irrigated corn systems that use irrigation water as a method of N delivery have been successfully made using relative chlorophyll meter readings made by comparing sensor readings from normal farmer fields to readings from plots with high N. Continuous examination of the N status of corn with the chlorophyll meter enabled the additional low N applications when the readings of the chlorophyll meter indicated that N levels had fallen below a set value that determined to be critical [110]. Relative recommendations using the chlorophyll meter require a location where nonlimiting rates of N were applied. Corn grain yield predictions were more accurate when made with relative chlorophyll meter readings rather than predictions using absolute meter readings [105]. Corrective N applications can only be made in a single application in a dryland corn production system, and there are no simple relationships between the application of N that the crop needs and the chlorophyll meter readings [105]. In comparison, low fixed amounts of N can frequently be applied when required in irrigation systems, while guiding N application rates are only active when done with a chlorophyll meter if the meter is the basis for a single N application recommendation [105]. Sripada et al. [113] have analyzed active optical sensors and their possible use at a field scale to determine irrigated corn N status. Variations of growth were manipulated by altering time applications and the rate or amount of N applied. A chlorophyll index (CI) at 590 nm and a NDVI at 590 nm were the two evaluated vegetative indices. Both indices were related to N rate, hybrid, and growth stage. The chlorophyll content during the vegetative growth stages had a stronger relationship to sensor readings than the vegetative reproductive stages. A group of studies has evaluated two available commercial active optical sensors and their efficiency. The two sensors studied were the GS Model 505 (Trimble Inc., Sunnyvale, CA) and the CC ACS-210™ (Holland Scientific, Inc., Lincoln, NE), and they were both used to predict corn yield. The two sensors were differentiated by the wavelengths that they used to calculate NDVI. Both sensors utilized visible and near-infrared wavelengths but the GS Model 505 utilized reflectance measurements from 660 nm and 770 nm, while the CC ACS-210 emitted and detected light at 590 nm and 880 nm. Both sensors are sensitive to crop growth differences (r2 > 0.89). The GS Model 550 exhibited saturation at later stages of growth in comparison to the CC ACS-210, as the different wavelength used by the CC ACS-210 to predict yield reduced its sensitivity and allow usage at the later stages of growth [92]. The GS was also found to be sensitive to the rate of the sensor movement and row spacing [111]. Once again, the CC ACS-210 outperformed the GS by displaying stability during the early and late stages of growth, as well as over multiple row spacing and speed of sensor movement [112]. Therefore, while choosing an appropriate sensor variable N management, the red-edge (680–730 nm) and green wavelength (590 nm) provide a better estimation of canopy development [111]. The hand-held GS 505 is a GBAO sensor, which, unlike the chlorophyll meter, measures reflected light. Satellite imagery, chlorophyll meters, and aerial photography have disadvantages in comparison to the GS when it comes to corn N nutrient management on a field scale regarding speed and labor intensiveness. Ultra-high resolution and fully canopies are needed for aerial photography, while it is not necessary for the GS [113]. Deficiencies of N in plants result in decreased photosynthetic activity, resulting in a higher reflectance of the visible segment of the spectra (400–700), while the stress caused by the N deficiency results in reduced leaf surface area, causing a decrease in NIR (>700 nm) reflectance [114].

10.5. Materials and methods for phosphorus

Three approaches for the study were considered. For the first approach, the last 10-year nutrient analysis data from UMaine Soil Testing Laboratory (UMSTL) were used. Loam, gravelly loam, sandy loam, and silty loam with a parent material of glacial outwash are the soils present in Aroostook County, Maine. Soil testing procedures recommended for the Northeastern USA with publication no.493 by 1:1 method were followed. Modified Morgan soil extracts with inductively coupled plasma (ICP) were used to measure P, Mg, K, Al, and Ca. Using 2874 mL glacial acetic acid mixed with 40 L carboy containing ~ 20 L of distilled water, a modified Morgan extractant (0.62 N NH4OH + 1.25 N CH3COOH) was prepared. Most laboratories did not do bulk density measurements to make it easier for farmers to understand as they convert PPM to pounds/acre. The formula for this is PPM × 2. For all soil testing, the universal assumption/conversion is 2 million pounds or 1000 tons dried and sieved soil per “acre plow layer.” Fixed volumes were obtained by scooping rather than weighing by the laboratories to calculate PPM by volume (mg/dm3) and multiplied by 2 to get a pounds/acre volume. A 1-year N and P study done in 2016 was used for approach 2. A farmer’s field in Easton, Aroostook County, was used as the research site for this method. Isotic, frigid Aquic Haplorthods and gravelly loam, fine loamy, isotic, frigid Typic Haplorthods were the soil types used for this study. The Russet Burbank potato cultivar was utilized for this study and was planted 10 cm deep and with row spacing of 91 cm. At planting on the study plots, 6 N treatments, 0, 56, 112, 168, and 280 kgha−1, was done for each of the N fertilizers that are being used in the study, ammonium nitrate (AN) and calcium ammonium nitrate (CAN). The Univ. of Maine Soil Testing Lab., potassium (KCI), gives following recommendations, and P applications were implemented. In the study plot, P was found at a sufficient range (45–49 kgha−1) out of a required range of 24–56 kg ha−1 needed which eliminated the need of additional P application. However, the farmer still applied 224 kg ha−1 of P on his field leaving the study plot. A UMaine study done in 1996 found no response on the soils, with high P tests (>40 kg ha−1) was cited as the reason for no P applications. The location site was 46 × 46 m and was divided into 3.7 × 9 m subplots. Four replications within a complete randomized block design were used, see Table 1.

Location/Soil Sample DepthOMpHPKCaMgNSBCuFeMnZn
Easton/0–15 cm3.45.4183861065125261330.
Easton/0–15 cm3.15.5204591062114181670.

Table 1.

Before planting at the Easton site, a comprehensive soil test was conducted.

Soil samples were collected from 0 to 15 cm deep and 15–46 cm deep from the study using a standard soil probe.

Two 10 foot potato rows were harvest from each subplot, and each collected bag was graded. The P study from the 1999 master thesis and an article from [115] were reviewed with permission for the third approach (Table 2), as well as data from other studies. Maine P study recommendations were developed and critically examined in and near areas in the Northeastern USA and Canada. The Hochmuth study was done in Maine on 12 research locations in farmers in 1995 and 1996. Medium to high P levels was found at all the sites. Diammonium phosphate was applied at 5 P rates, 0, 56, 112, 168, 224 kg P2O5 ha−1, using a randomized complete block design with five replications. The “Atlantic” potato cultivar was used for the experiment. All fertilizer was applied at planting. Only one site responded positively to an increase in P rates. To determine the correlation between several parameters of soil that changed with time, the coefficient of correlation (R2) was used. SAS for Windows 9.2 using PROC REG was used to conduct regression analyses. To compare the N treatments with farmer field yield data and potato yield for approach 2, SAS GLM was used. The relationship between time and P levels was from the UMaine Soil Testing Laboratory who averaged the 10-year data set. The simple percent calculation method was used to calculate the percentage of P samples that were at or above sufficient P levels. The simple percent calculation method is as follows: X = number of samples with P levels above 35 kg/ha and Y = total number of samples.

Min Range5112219132900123502
Max range89667062627596502120335881142617
Min Range51234181561600215102
Max range8103467125921,61630816651522112202519
Min Range4133819116800117503
Max range7789552228277033311133468217410615
Min Range41128161341900216403
Max range779575132841063012837418724014920
Min Range411471084600225403
Max range884659225968214332125249722112917
Min Range51248231761000212101
Max range710706873756854320284837881102015
Min Range51342191641700126403
Max range7980558271423528871533291792442215
Min Range41325171481900214401
Max range8106148828510,35832916911517721212713
Min Range51131171712000214403
Max range886555526445993582144327171225612
Min Range41247221761100122302
Max range8106161339210,9665151365337619024314

Table 2.

The chemical analysis of soil samples received by the UMaine Soil Testing Laboratory each year.

The number here is the average of ~1000 potato soil samples received by the laboratory each year.

11. Results and discussion

Of the total Maine soil samples in approach 1, 85% were found to have sufficient P (Table 2 and Figure 2) in the range between 24 and 56 kgha−1. However, farmers still applied P in the range of 180–200 kg ha−1. Since 2006, growers have been ignorant of recommendations and have been applying significant amounts of P, when the application is not needed, causing P pollution. About 5% of soil samples had more than 56 kgha−1 of P, and 10% were P deficient. There may have been a steady build-up of P in the soil over the years ([116, 117]) due to steady P application. In 2016, 85% of the soil samples were found to have a higher range of P in comparison to ~70% in 1996. Growers apply excessive P to protect themselves from P deficiencies caused by soil fixation and erosion in an attempt to ensure that a sufficient supply of P is available to their crops. The low cost of P makes it easier to over apply P. Soil reactive aluminum (Al) that potentially fixes P has a great presence in soil with pH’s of around 5–6 pH [118] and is cited by growers as an additional reason to apply excessive P. Maine soil has a general pH range between 4.9 and 6 pH. Al reacts with P to form Al phosphate, a crystalline structure that can transform again to form amorphous Al phosphate [118]. P is also lost to erosion.

Figure 2.

The AL and soil P levels in Aroostook County, Maine. The Univ. of Maine Soil Testing Laboratory has been receiving soil samples since 2006. (a) represents the change in phosphorous levels with time (p = 0.03), (b) represents the relationship between Al and P (p = 0.01), (c) accounts for the change in AL levels with time (p = 0.2), (d) represents the relationship between Ca and pH (p = 0.02), (e) represents the relationship between Ca and P(p = 0.2), and (f) represents the relationship between pH and P (p = 0.6). The polynomial model was used in 2 (f) because it was best suited. The trend was positive and properly depicted the significant association of soil P buildup with successive years.

The possibility that P might potentially be fixed in high amounts in Maine’s soil was confirmed by a gradual increase of Al levels with a coefficient of correlation (R2 = 0.41) over time in the soils of Aroostook County. Despite a strong correlation, the relationship between P, Ca, P, and pH was not significant. However, it was found that P and Al had a very strong relationship with serious correlations (R2 = 0.72). The maximum yield obtained in approach 2 where no P was applied was 59 t ha −1 in comparison to the average Maine potato yield of 44 t ha−1 [97] with an average P rate of 182 kgha−1. Compared with the zero P application at the experimental plot, the farmer applied P at the rate of 224 kgha−1 but got a maximum yield of ~53 t ha−1. This confirmed that many farms in Maine potentially have enough P for maximum optimal potato yield. Due to crop and livestock production and high fertilizer applications, soil fertility in Maine may have improved [113]. Another source of improvement in soil fertility is manure application and organic agricultural practices. Over 50% of the annual soil tests in the Northeast States had results that showed high levels of plant-available P [110], indicating that the large P soil reserves could lead to excessive P application as many of the states in the Northeast have not calculated soil P tests results satisfactorily due to P sites that were nonresponsive. Consequently, they were not able to find the optimum P rate for optimal yield. The necessity of developing recommendations for different regions and crop to account for the effects of multiple soil types, climate, crop growth habits, and crop requires has increased the amount of work needed. A study on the effect of residual P in Northeast Florida on the Sebago potato by [107] discovered that soils with P levels greater than 20 mg P kg−1 (Mehlich I method) produced about the same yield as soils without P fertilizer. The experiments carried out by [107] were performed on acidic soils with a pH range of 4.5–6, similar to soil pH levels in Maine. Other differences (such as soil types and climate) make it unreasonable to use the results of their study as a basis for P recommendation revisions in Maine. P fertility experiments in the early 1800s in Northern Maine revealed results similar to Rhue’s. P applications could potentially be reduced or eliminated without yield reduction on soils that have high amounts of plant-available P (modified Truog method). Potatoes require ~39–45 kg ha−1 of P for optimum yields [119, 120]. As potatoes do not use P in soil aggressively, fields with high P concentrations may not need an application of P for several years [121]. However, the variability in P in soil may cause yield to decrease across larger fields. As such, growers may not want to risk nonapplication of P in the soil as it may affect their profits. A study in Florida in 2002 determined that even though P was applied at rates of 0, 12, 24, 49, and 74, yields were not impacted significantly. This may have been due to P fixation in the soil that releases P during plant growth by mineralization or other means [120]. Only one site out of five showed a decrease in yield with a higher P concentration, but it was not significant. The P concentration in the leaves was highly correlated with yield, and only one site found to have an inverse relationship. When graphing all combined outcomes, they are weakly correlated, but individually, they show a strong correlation.

Several studies have indicated that variations in soil type could have an impact on P response regarding crop yield [122, 123] as demonstrated in the introduction. This deems it necessary to study the varying soil types in Maine and Aroostook County, Maine. There are 21 mapping units in Maine, and of these, 15 mapping units are located in Aroostook County, which is a major potato growing area. The soil behaves differently P response of crop yield, P supplying ability, and P retention, and they may vary further in P distribution throughout the landscape. Table 2 explained that there are soils containing gravel and stones with loam to silt loam. The higher drainage portions of the gravel infused soil may move P into groundwater and nearby streams, whereas silty loam may retain more P. The primary soil order in Maine is Spodosols, susceptible to P deficiency with the third minimum distribution of P among the 12th order after Andisols and Vertisols [124]. A University of Kentucky study on P showed that testing soil P changed under different soils with the same rate of P application [123]. This study explained that different soils have varying rates of P absorption, which results in different levels of P soil tests despite the same rate of application, making it crucial to consider soil type when testing for P levels and recommending P rate for agronomic crops. Figure 3 explains the rate of change of P concentration in soil depending on the initial test, showing variations in P soil tests with increasing P rates of 16 soil mapping units of large agronomic crops.

Figure 3.

Representing the change in P levels with seven P rates under 16 different soil mapping units in Kentucky, United States. Source: Data adopted from Hochmuth et al. [115].

Soil pH is a key factor that regulates soil P in soil solutions. The pH range for maximum P availability is between 6 and 7 [112]. At pH levels lower than 6, the available P is fixed by Al and Fe ions and fixed by Ca at a pH higher than 7. Changes in pH were in the study due to its influence. The approach I was used to determine that change in pH over time. Other studies were also discussed to find the answer of the impact of P recommendation and pH on P pollution. Shaver et al. [112] concluded an experiment in Maine to develop P recommendations which proved that there was sufficient P available in Maine soils when only one site (R2 = 0.66) out of 12 was found with positive P response, and the sites with no P response had high to too high P availability. There were not sufficient data to develop P recommendations, so researchers recommend a minimum of ~56 kgha−1 when the P value is between 22 and 56 kgha−1. Moreover, while the application is not too high, it may have an impact on P erosion to Maine’s water sources. Soil pH rates in Maine have improved over the last 10 years (Figure 4), mostly after P recommendations were developed. Maine’s potato soils have increased after the variety switch from round whites to scab resistant Russet Burbank potatoes and due to grains and other rotation crops that require a higher pH level. The current emphasis on growing grains has led to the increase in soil pH from 5 (20 years ago) to ~6 presently and is expected to continue to improve.

Figure 4.

The trend of change in the soil pH and calcium level over time in Aroostook County. (a) The change in pH, and (b) a shift in calcium level with time.

Crop response to an application of P depends on the P availability and crop uptake ability. The soil P can be slowly replenished, but it still depends on the uptake speed and overall crop behavior [125]. Once the crop has absorbed the P from the soil solution, the unavailable or stable form of P can slowly replenish it. The uptake ability also depends on root distribution [125]. P application in potatoes as a banded application that comes in direct contact with the roots ensures P availability later in the season. However, the rainfall before uptake could cause the P to move deeper into the soil or become fixed in unavailable or marginally available forms. In contrast, less movement of P could result in less availability in a banded application as compared to a broadcast application. The potato planting in Maine happens in late May and early June, making the P application more susceptible to erosion due to rainfall. With rainfall in consideration, it is wise to apply P in high doses that are close to first of second hilling (tuber initiation), as the different soil moisture could severely affect the P uptake by the crop plants [122]. Several studies have documented the improvement of crop yields with P application [126]. However, the economic return and response were found only in places with low soil [127]. Inefficiency in soil P application leads to P build up in the soil, particularly when potatoes are used in crop rotation [127]. There is a gap between the rate of P application and the rate of P removal. Potatoes have a relatively high P requirement but a low P uptake behavior [128]. Water can be used as an extracting of P, but due to lack of major leftover undissolved P and analysis difficulties of water as an extracting, several other varieties of extractants have been suggested to extract forms of P in soils used by plants. The Truog Method (1930) is to dilute H2SO4 buffered to pH 3.0. The Bray Method is a combination of HCL and NH4F used to extract acid soluble P forms (mostly Al and Fe bound P [129]) in North Central states. In 1953, another combination (Mehlich 1), HCL and H2SO4 acids were introduced to extract P and other nutrients in Southeastern soils. In 1984, Mehlich further expanded on his earlier extractants to Mehlich 3, a combination of acetic acid [HOAc] and nitric acids [HNO3], salts (ammonium fluoride [NH4F] and ammonium nitrate [NH4NO3]). The standard soil test for P is modified Morgan in Northeastern states due to its acidic soils and low (less than 20) cation exchange capacity (CEC). Modified Morgan used 0.62 M NH4OAc + 1.25 M CH3COOH at pH 4.8. Soil tests show an increase in soil pH in Maine overall with an average P application of ~32 kgha−1. The average application of P is between 20 and 50 kgha−1, but farmers still apply P to their soils making the excess erode into local water systems. P recommendation studies and their results have never been published making it difficult for growers and researchers alike to amend their practices for better P guidelines.

12. Conclusion

Soils in Maine are highly variable and may already possess sufficient P to support the maximum yields of crops. Therefore, the recalibration of the recommendation equation is necessary, by newly inserting low, medium, and high P yield sites. While developing P recommendations, it is important to differentiate between soil types and regions, such as North Dakota State for sunflower, corn, and wheat [130, 131, 132, 133, 134, 135]; because soil variability and soil moisture are a driving force toward plant, growth, and nutrient movement among plant roots. The study found that P recommendation needs revision to account for soil variability and a recalibration of the soil P test. The average soil P test has increased showing a buildup of P in Maine soils. Due to unnecessary applications of P, the study recommends a more robust recommendation from low, medium high, and above excellent P level sites. The study also found that types of growers need to be taken into consideration, e.g., table stock growers (not concerned with frying quality), seed producers (no concern of high yield or frying quality), and processing growers (need excellent frying quality with maximum yields) when developing P recommendations. It was also found that an examination needs to be done on the banded application according to the crops root system development as banded applications stay here are applied to the roots that grow beyond the reach of the application.


  1. 1. Raun WR, Johnson GV. Improving nitrogen use efficiency for cereal production. Agronomy Journal. 1999;91:357-363
  2. 2. Schilling KE. Chemical transport from paired agricultural and restored prairie watersheds. Journal of Environmental Quality. 2002;31:1184-1193
  3. 3. Steinheimer TR, Scoggin KD, Kramer LA. Agricultural chemical movement through a field size watershed in Iowa: Surface hydrology and nitrate losses in discharge. Environmental Science & Technology. 1998;32:1048-1052
  4. 4. CAST. Gulf of Mexico hypoxia: Land and sea interactions. Task Force Report No. 134.Council for Agricultural Science and Technology, Amaes, IA; 1999
  5. 5. Fageria NK, Baligar VC. Enhancing nitrogen use efficiency in crop plants. Advances in Agronomy. 2005;88:97-185
  6. 6. Cassman KG, Dobermann A, Walters DT. Agroecosystems, nitrogen-use efficiency, and nitrogen management. Ambio. 2002;31:132-140
  7. 7. United States Department of Agriculture-National Agricultural Statistics Service. Statistics of Fertilizers and Pesticides. Available at (Verified 26 Apr. 2004); 2003
  8. 8. Andrade F, Cirilo AG, Uhart SA, Otegui ME. Ecofisiologia del Cultivo de Maiz. Editorial La Barrosa y Dekalb Press; 1996. p. 292
  9. 9. Schröder JJ, Neeteson JJ, Oenema O, Struik PC. Does the crop or the soil indicate how to save nitrogen in maize production? Reviewing the state of the art. Field Crops Research. 2000;66:277-278
  10. 10. Reddy GB, Reddy KR. Fate of nitrogen-15 enriched ammonium nitrate applied to corn. Soil Science Society of America Journal. 1993;57:111-115
  11. 11. Olson RA, Raun WR, Chun YS, Skopp J. Nitrogen management and interseeding effects on irrigated corn and sorghum and on soil strength. Agronomy Journal. 1986;78:856-862
  12. 12. Keeney DR. Nitrogen Management for Maximum Efficiency and Minimum Pollution. Madison, WI: ASA, CSSA, and SSSA; 1982
  13. 13. Raun WR, Solie JB, Johnson GV, Stone ML, Lukina EV, Thomason WE, Schepers JS. In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal. 2001;93:131-138
  14. 14. Lambert DM, Lowenberg-DeBoer J, Malzer GL. Economic analysis of spatial temporal patterns in corn and soybean response to nitrogen and phosphorus. Agronomy Journal. 2006;98:43-54
  15. 15. Inman D, Khosla R, Westfall DG, Reich R. Nitrogen uptake across site specific management zones in irrigated corn production systems. Agronomy Journal. 2005;97:169-176
  16. 16. Mulvaney RL, Khan SA, Ellsworth TR. Need for a soil-based approach in managing nitrogen fertilizers for profitable corn production. Soil Science Society of America Journal. 2005;70:172-118
  17. 17. Bock BR. Efficient use of nitrogen in cropping systems. In: Hauck RD, editor. Nitrogen in Crop Production. Madison, WI: ASA, CSSA, and SSSA; 1984. pp. 273-294
  18. 18. Pierce FJ, Rice CW. Crop rotation and its impact on efficiency of water and nitrogen use. In: Hargrove WL et al., editors. Cropping Strategies for Efficient use of Water and Nitrogen. ASA Special Publ. 51. Madison, WI: ASA, CSSA, and SSSA; 1988. pp. 21-42
  19. 19. IOM (Institute of Medicine) and NRC (National Research Council). A Framework for Assessing Effects of the Food System. Washington, DC: The National Academies Press; 2015
  20. 20. EPA. 2015
  21. 21. USDA-NASS. https: // 2016. [Accessed 1 April 2017]
  22. 22. Bell GL. Lake Erie Chemical and Physical Characteristics Data for 1967. NOAA Data Report ERL GLERL-4, Great Lakes Environmental Research Laboratory, Ann Arbor, MI(PB80-184179); 1980, 9 pp
  23. 23. Livingston-Way P, Hochmuth G, Hanlon E, Tilton A, Bottcher D, Reck B, Konwinski J. Assessment of Agricultural BMPs in the Tri-County Agricultural Area of Northeast Florida. Univ. Fla. Coop. Ext. Serv. Misc. Publ; 1997
  24. 24. Maine Department of Environmental Protection (Maine DEP). Integrated Water Quality Monitoring and Assessment Report. Bureau of Land and Water Quality, Augusta, ME. 2012
  25. 25. Stark JC, Westermann DT, Hopkins BG. Nutrient Management Guidelines for Russet Burbank Potatoes. Univ of Idaho Bull. #840, Moscow, ID. 2004
  26. 26. Van der Zaag P. Soil Fertility Requirements for Potato Production. Technical Information Bulletin 14, CIP, Lima. 1981
  27. 27. Flanagan SM, Nielsen MG, Robinson KW, Coles JF. Water-quality assessment of the New England coastal basins in Maine, Massachusetts, New Hampshire, and Rhode Island: Environmental settings and implications for water quality and aquatic biota. U.S. Geological Survey, Water-Resources Investigations Report 98-4249, Pembroke, New Hampshire. 1999
  28. 28. Sharpley AN, Smith SJ, Jones OR, Berg WA, Coleman GA. The transport of bioavailable phosphorus in agricultural runoff. Journal of Environmental Quality. 1992;21:30-35
  29. 29. Sharpley AN. Bioavailable phosphorus in soil. In: Pierzynski GM, editor. Methods for Phosphorus Analysis for Soils, Sediments, Residuals, and Waters. Southern Cooperative Series Bull; 2000. pp. 38-43
  30. 30. McGuire P. Analysis: Belgrade Lake’s Water Quality Down. Portland Press Herald. (Posted 27 July 2015); 2015
  31. 31. Peech M. Nutrient status of soils in commercial potato-producing areas of the Atlantic and Gulf Coast: Part II. chemical data on the soils. Soil Science Society of America Journal. 1946;10(10):245
  32. 32. Rhue RD, Hensel DR, Yuan TL, Robertson WK. Response of potatoes to soil and fertilizer phosphorus in northeast Florida. The Soil and Crop Science Society of Florida Proceedings. 1981;40:58-61
  33. 33. Maier NA, Potocky-Pacay KA, Dahlenburg AP, William CMJ. Effect of phosphorus on the specific gravity of potato tubers (Solanum tuberosum L.) of the cultivars Kennebec and Coliban. Australian Journal of Experimental Agriculture. 1989;29:869-874
  34. 34. Pierzynski GM, Logan TI. Crop, soil, and management effects on phosphorus soil test levels. Journal of Production Agriculture. 1993;6:513-520
  35. 35. Sharpley AN. Dependence of runoff phosphorus on extractable soil phosphorus. Journal of Environmental Quality. 1995;24:920-926
  36. 36. Porter GA, McBurnic JC. Crop and soil research. In: Alford AR et al. The Ecology, Economics, and Management of Potato Cropping Systems: A Report of the First Four Years of the Maine Potato Ecosystem Project. Maine Agr. Forest Exp. Sta. Bull. 843. Maine Agr. Forest Exp. Sta. Orono, ME. Maine Agr. Forest Exp. Sta., Orono. ME; 1996. pp. 8-62
  37. 37. Fitzgerald C. Soil phosphorus in Aroostook County (Maine) potato cropping systems: organic matter effects and residual phosphorus contributions. (Master Thesis). Orono: University of Maine; 1998
  38. 38. Franzen DW, Hopkins DH, Sweeney MD, Ulmer MK, Halvorson AD. Evaluation of soil survey scale for zone development of site-specific nitrogen management. Agronomy Journal. 2002;94:381-389
  39. 39. Ferguson RB, Lark RM, Slater GP. Approaches to management zone definition for use of nitrification inhibitors. Soil Science Society of America Journal. 2003;67:937-947
  40. 40. Kitchen NR, Sudduth KA, Myers DB, Drumond ST, Hong SY. Delineating productivity zones on claypan soil fields using apparent soil electrical conductivity. Computers and Electronics in Agriculture. 2005;46:285-308
  41. 41. Schepers AR, Shanahan JF, Liebig MA, Schepers JS, Johnson SH, Jr Luchiari A. Appropriateness of management zones for characterizing spatial variability of soil properties and irrigated corn yields across years. Agronomy Journal. 2004;96:195-203
  42. 42. Wibawa WD, Dludlu DL, Swenson LJ, Hopkins DG, Dahnke WC. Variable fertilizer application based on yield goal, soil fertility, and soil map unit. Journal of Production Agriculture. 1993;6:255-261
  43. 43. Franzen DW, Wagner G, Sims A. Application of a ground-based sensor to determine N credits from sugarbeet. In Sugarbeet Research and Extension Reports. Vol. 34. Fargo, ND: Sugarbeet Research and Education Board of Minnesota and North Dakota; 2003. pp. 119-123
  44. 44. Kravchenko AN, Bullock DG, Reetz HF. Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal. 2000;92:75-83
  45. 45. Franzen DW. North Dakota fertilizer recommendation tables and equations. In: NDSU Extension Circular SF-882. Fargo, ND: North Dakota State University Extension Service; 2010
  46. 46. Ulrich A. Physiological bases for assessing the nutritional requirements of plants. Annual Review of Plant Physiology. 1952;3:207-228
  47. 47. Greenwood DJ, Lemaire G, Gosse G, Cruz P, Draycott A, Neeteson JJ. Decline in percentage N of C3 and C4 crops with increasing plant mass. Annals of Botany. 1990;66:425-436
  48. 48. Justes E, Mary B, Meynard JM, Machet JM, Thelier-Huché L. Determination of a critical nitrogen dilution curve for winter wheat crops. Annals of Botany (London). 1994;74:397-407
  49. 49. Van Oosterom EJ, Carberry PS, Muchow RC. Critical and minimum N contents for development and growth of grain sorghum. Field Crops Research. 2001;70:55-73
  50. 50. Colnenne C, Meynard JM, Reau R, Justes E, Merrien A. Determination of a critical nitrogen dilution curve for winter oilseed rape. Annals of Botany (London). 1998;81:311-317
  51. 51. Sheehy JE, Dionora MJA, Mitchell PL, Peng S, Cassman KG, Lemaire G, Williams RL. Critical nitrogen concentrations: Implications for high-yielding rice(Oryza sativa L.) cultivars in the tropics. Field Crops Research. 1998;59:31-41
  52. 52. Lemaire G, Salette J. Relation entre dynamique de croissance et dynamique de prélèvement d’azote pour un peuplement de graminées fouragères: I. Etude de l’effect du milieu. Agronomie (Paris). 1984;4:423-430
  53. 53. Plénet D, Lemaire G. Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Plant and Soil. 1999;216:65-82
  54. 54. Binford GD, Blackmer AM, Cerrato ME. Relationships between corn yields and soil nitrate in late spring. Agronomy Journal. 1992;84:53-59
  55. 55. Kravchenko AN, Robertson GP, Thelen KD, Harwood RR. Management, topographical, and weather effects on spatial variability of crop grain yields. Agronomy Journal. 2005;97:514-523
  56. 56. Jiang P, Thelen KD. Effects of soil and topographic properties on crop yield in a north-central corn-soybean cropping system. Agronomy Journal. 2004;96:252-258
  57. 57. Ginting D, Moncrieg JF, Gupta SC. Performance of a variable tillage system on interactions with landscape and soil. Precision Agriculture. 2003;4:19-34
  58. 58. Fox RH, Kern JM, Piekielek WP. Nitrogen fertilizer source and method and time of application effects on no-till corn yields and N uptakes. Agronomy Journal. 1986;78:741-746
  59. 59. Bandel VA, Dzienia S, Stanford G. Comparison of N fertilizers for no-till sites corn. Agronomy Journal. 1980;72:337-341
  60. 60. Shanahan JF, Kitchen NR, Raun WR, Schepers JS. Responsive in-season nitrogen management for cereals. Computers and Electronics in Agriculture. 2008;61:51-62
  61. 61. Holland KH, Schepers JS. Derivation of a variable rate nitrogen application method for in-season fertilization of corn. Agronomy Journal. 2010;102:1415-1424
  62. 62. Scharf PC, Schmidt JP, Kitchen NR, Sudduth KA, Hong SY, Lory JA, Davis JG. Remote sensing for nitrogen management. Journal of Soil and Water Conservation. 2002;57:518-524
  63. 63. Whittaker, R. H., and P. L. Marks. 1975. Methods of assessing terrestrial productivity. In: H. Lieth and R. H. Whittaker (Eds.). Primary Productivity of the Biosphere. Ecological Studies 14. Springer-Verlag. pp. 55-118
  64. 64. Anten NPR, Schieving F, Medina E, Werger MJA, Schufflen P. Optimal leaf area indices in C3 and C4 mono- and dicotyledonous species at low and high nitrogen availability. International Journal of Plant Biology. 1995;95:541-550
  65. 65. Fang H, Liang S, Kuusk A. Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment. 2003;85:257-270
  66. 66. Chen JM, Cihlar J. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sensing of Environment. 1995;55:153-162
  67. 67. Curran PJ. Multispectral remote sensing for the estimation of green leaf area index. Philosophical Transactions of the Royal Society. 1983;309:257-270
  68. 68. Boyer JS. Plant productivity and the environment. Science. 1982;218:443-448
  69. 69. Tollenaar M. Genetic improvement in grain yield of commercial maize hybrids grown in Ontario from 1959 to 1988. Crop Science. 1989;29:1365-1371
  70. 70. Fulton JM. Relationships among soil moisture stress, plant populations, row spacing and yield of corn. Canadian Journal of Plant Science. 1970;50:31-38
  71. 71. Herrero MP, Johnson RR. Drought stress and its effects on corn reproductive systems. Crop Science. 1980;21:105-110
  72. 72. Dwyer LM, Stewart DW, Tellenaar M. Analysis of corn leaf photosynthesis under drought stress. Canadian Journal of Plant Science. 1992;72:477-481
  73. 73. Denmead OT, Shaw RH. The effects of soil moisture stress at different stages of growth on the development and yield of corn. Agronomy Journal. 1960;52:272-274
  74. 74. Crafts-Brandner SJ, Salvucci ME. Sensitivity of photosynthesis in a C4 plant, corn, to heat stress. Plant Physiology. 2002;129:1773-1780
  75. 75. Al-Khatib K, Paulsen MG. Photosynthesis and productivity during high temperature stress of wheat genotypes from major world regions. Crop Science. 1990;30:1127-1132
  76. 76. Carter GA. Responses of leaf spectral reflectance to plant stress. American Journal of Botany. 1993;80:239-243
  77. 77. Carter GA, Knapp AK. Leaf optical properties in higher plants: Linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany. 2001;88:677-684
  78. 78. Zhao D, Reddy KR, Kakani VG, Read JJ, Carter GA. Corn (Zea mays L.) growth, leaf pigment concentrations, photosynthesis, and leaf hyperspectral reflectance properties as affected by nitrogen supply. Plant and Soil. 2003;257:205-217
  79. 79. Masoni A, Ercoli L, Mariotti M. Spectral properties of leaves deficient in iron, sulfur,magnesium, and manganese. Agronomy Journal. 1996;88:937-943
  80. 80. Gates DM, Keegan HJ, Schleter JC, Weidner VR. Spectral properties of plants. Applied Optics. 1964;4:11-20
  81. 81. Hatfield JL, Gitelson AA, Schepers JS. Application of spectral remote sensing for agronomic decisions. Agronomy Journal Supplement. 2008;100:117-131
  82. 82. Gitelson AA, Merzlyak MN. Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing. 1997;18:2691-2697
  83. 83. Ciganda V, Gitelson AA, Schepers JS. Non-destructive determination of corn leaf canopy chlorophyll content. Journal of Plant Physiology. 2009;166:157-167
  84. 84. Moss RA, Loomis WE. Absorption spectra of leaves. I. The visible spectrum. Plant Physiology. 1952;27:370-391
  85. 85. Gitelson AA, Viña A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophysical Research Letters. 2003;30:1248
  86. 86. Federer CA, Tanner CB. Spectral distribution of light in the forest. Ecology. 1966;47:55-560
  87. 87. Tucker CJ. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment. 1979;8:127-150
  88. 88. Gitelson AA, Viña A, Rundquist DC, Ciganda V, Arkebauer TJ. Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters. 2005;32
  89. 89. Huete AR. A soil-adjusted vegetative index (SAVI). Remote Sensing of Environment. 1988;25:295-309
  90. 90. Slaton MR, Hunt ER, Smith WK. Estimating near-infrared leaf reflectance from leaf structural characteristics. American Journal of Botany. 2001;88:278-284
  91. 91. Osborne SL, Schepers JS, Francis DD, Schlemmer MR. Use of spectral radiance to estimate in-season biomass and grain yield in nitrogen and water stressed corn. Crop Science. 2002;42:165-171
  92. 92. Deering DW, Rouse Jr JW, Haas RH, Schell JA. Measuring forage production of grazing units from LANDSAT MSS data. In: Proceedings of the 10th International Symposium on Remote Sensing of Environment; 1975
  93. 93. Deering DW. Rangeland reflectance characteristics measured by aircraft and spacecraft sensors. (Ph.D. dissertation). College Station; 1978
  94. 94. Solari F, Shanahan J, Ferguson RB, Schepers JS, Gitelson AA. Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal. 2008;100:571-579
  95. 95. Wolfe DW, Henderson DW, Hsiao TC, Alvino A. Interactive water and nitrogen effects on senescence of maize. II. Photosynthetic decline and longevity of individual leaves. Agronomy Journal. 1988;80:865-870
  96. 96. Gentry LE, Below FE. Corn productivity as influence by form and availability of nitrogen. Crop Science. 1993;33:491-497
  97. 97. Belay A, Claassens AS, Wehner FC. Effect of direct nitrogen and potassium and residual phosphorous fertilizers on soil chemical properties, microbial components and corn yield under long-term crop rotation. Biology and Fertility of Soils. 2002;35:420-427
  98. 98. Shanahan JF, Holland K, Schepers JS, Francis DD, Schlemmer MR, Caldwell. Use of crop reflectance sensors to assess corn leaf chlorophyll content; 2003. pp. 129-144. In (ed.)
  99. 99. Hansen PM, Schjoerring JK. Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment. 2003;86:542-553
  100. 100. Moges SM, Raun WR, Mullen RW, Freeman KW, Johnson GV, Solie JB. Evaluation of green, red, and near infrared bands for predicting winter wheat biomass, nitrogen uptake, and final grain yield. Journal of Plant Nutrition. 2004;27:1431-1441
  101. 101. Inman D, Khosla R, Reich R, Westfall D. Active remote sensing and grain yield in irrigated maize. Precision Agriculture. 2007;8:241-252
  102. 102. Henebry GM, de Beurs KM, Gitelson AA. Land surface phenologies of Uzbekistan and Turkmenistan between 1982 and 1999. Arid Ecosystems. 2005;11:25-32
  103. 103. Blackmer TM, Schepers JS. Aerial photography to detect nitrogen stress in corn. Journal of Plant Physiology. 1996;148:440-444
  104. 104. Kitchen NR, Goulding KW. On-farm technologies and practices to improve nitrogen use efficiency. In: Follett RF, Hatfield JL, editors. Nitrogen in the Environment: Sources, Problems, and Management. Amsterdam, the Netherlands: Elsevier Science; 2001. pp. 335-369
  105. 105. Scharf PC, Brouder SM, Hoeft RG. Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north-central USA. Agronomy Journal. 2006;98:655-665
  106. 106. Schepers JS, Varvel GE, Watts DG. Nitrogen and water management strategies to reduce nitrate leaching under irrigated maize. Journal of Contaminant Hydrology. 1995;20:227-239
  107. 107. Varvel GE, Schepers JS, Francis DD. Ability for in-season correction of nitrogen deficiency in corn using chlorophyll meters. Soil Science Society of America Journal. 1997;59:1233-1239
  108. 108. Schepers JS, Blackmer TM, Francis DD. Predicting N fertilizer needs for corn in humid regions: using chlorophyll meters. In: Bock BR, Kelley KR, editors. Predicting N Fertilizer Needs for Corn in Humid Regions. Bull. Y-226. Muscle Shoals, AL: National Fertilizer and Environmental Research Center; 1992. pp. 105-114
  109. 109. Bullock DG, Anderson DS. Evaluation of the Minolta SPAD-502 chlorophyll meter for nitrogen management in corn. Journal of Plant Nutrition. 1998;21:741-755
  110. 110. Shapiro CA. Using a chlorophyll meter to manage nitrogen applications to corn with high nitrate irrigation water. Communications in Soil Science and Plant Analysis. 1999;30:1037-1049
  111. 111. Shaver TM, Khosla R, Westfall DG. Evaluation of two Crop Canopy Sensors for Nitrogen Variability Determination in Irrigated Corn. Precision Agriculture. 2011. Online Publication
  112. 112. Shaver TM, Khosla R, Westfal DG. Evaluation of two ground-based active crop canopy sensors in corn: Growth stage, row spacing, and sensor movement speed. Soil Science Society of America Journal. 2010;74:2101-2108
  113. 113. Sripada RP, Heiniger RW, White JG, Weisz R. Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal. 2005;97:1443-1451
  114. 114. Daughtry CST, Walthall CL, Kim MS, de Colstoun EB, McMurtrey JE. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment. 2000;74:229-239
  115. 115. Bruulsema BTW. Soil fertility in the northeast. The Region. 2006;90(1):8-10
  116. 116. Hochmuth G, Weingartner P, Hutchinson C, Tilton A, Jesseman D. Potato yield and tuber quality did not respond to phosphorus fertilization of soils testing high in phosphorus content. HortTechnology. 2002;12:420-423
  117. 117. Campbell CA, Zentner RP, Selles F, Jefferson PG, McConkey BG, Lemke R, Blomert BG. Long- term effect of cropping system and nitrogen and phosphorus fertilizer on production and nitrogen economy of grain crops in a Brown Chernozem. Canadian Journal of Plant Science. 2005;85:81-93
  118. 118. McKenzie RH, Roberts TL. Soil and fertilizer phosphorus update Alberta Soil Sci. Workshop Proc Coast Terrace Inn Edmonton, AB 84 104 Feb. 20-22; 1990
  119. 119. Pierzynski GM, Sims JT, Vance GF. Soils and Environmental Quality. Chelsea, MI: Lewis Publishers; 1994. p. 313
  120. 120. Locascio SJ, Breland HL. Irish potato yield and leaf composition as affected by dolomite and phosphorus. The Soil and Crop Science Society of Florida Proceedings. 1963;23:95-99
  121. 121. Hochmuth G, Maynard D, Vavrina C, Hanlon E. Plant tissue analysis and interpretation for vegetable crops in Florida. Fla. Coop. Ext. Serv. Spec. Ser. SS-VEC 42; 1991
  122. 122. Yuan TL, Hensel DR, Rhue RD, Robertson WK. Nutrient status of a sandy Humaquept under long-term potato cultivation. The Soil and Crop Science Society of Florida Proceedings. 1985;44:93-97
  123. 123. Schultz E, Sharma LK, Graham C, Franzen DW. Nitrogen and Phosphorus Recalibration for Modern Varieties of Sunflowers for the Northern Great Plains. Madison, WI. Poster 1121: ASA-CSSA-SSSA; 2015
  124. 124. Cardelli V, Cocco S, Agnelli A, Nardi S, Pizzeghello D, Fernández-Sanjurjo M, Corti G. Chemical and biochemical properties of soils developed from different lithologies in Northwestern Spain (Galicia). Forests. 2017;8:135
  125. 125. Thom WO, Dollarhide JE. Phosphorus Soil Test Change Following the Addition of Phosphorus Fertilizer-to-16-Kentucky-Soils. Agronomy-Notes. Paper-11; 2002
  126. 126. Beegle D, Durst PT. Managing phosphorus for crop production. UC055 Penn State Ext. Agrons Facts. 2014;13:1-6
  127. 127. Jenkins PD, Ali H. Phosphorus supply and progeny tuber numbers in potato crops. Annals of Applied Biology. 2000;136:41-46
  128. 128. Ali H, Khan MA, Shakeel A, Randhawa A. Interactive effect of seed inoculation and phosphorus application on growth and yield of chickpea (Cicer arietinum L.). International Journal of Agriculture and Biology. 2004;6(1):110-112
  129. 129. Rosen CJ, Kelling KA, Stark JC, Porter GA. Optimizing phosphorus fertilizer management in potato production. American Journal of Potato Research. 2014;91(2):145-160
  130. 130. Sharma LK, Franzen DW. Use of corn height to improve the relationship between active optical sensor readings and yield estimates. Precision Agriculture. 2014;15:331-345
  131. 131. Franzen DW, Sharma LK, Bu H, Dentond A. Evidence for the ability of active-optical sensors to detect sulfur deficiency in corn. Agronomy Journal. 2016;108:2158-2162
  132. 132. Sharma LK, Bu HG, Denton A, Franzen DW. Active-optical sensors using red NDVI compared to red edge NDVI for prediction of corn grain yield in North Dakota, U.S.A. Sensors. 2015;15:27832-27853
  133. 133. Bu H, Sharma LK, Denton A, Franzen DW. Sugarbeet root yield and quality prediction at multiple harvest dates using active-optical sensors. Agronomy Journal. 2016;108:273-284
  134. 134. Bu H, Sharma LK, Denton A, Franzen DW. Comparison of satellite imagery and ground-based active optical sensors as yield predictors in sugar beet, spring wheat, corn, and sunflower. Agronomy Journal. 2017;109:299-308
  135. 135. Sharma LK, Bali SK, Zaeen AA, DwyerJ.D. Potential Reasons of Increased New England States Phosphorus Pollution: A Review. Soil Sci. Soc. A. conf; 2017

Written By

Lakesh K. Sharma, Ahmed A. Zaeen, Sukhwinder K. Bali and James D. Dwyer

Submitted: 07 June 2017 Reviewed: 06 November 2017 Published: 20 December 2017