Use of Innovative Tools to Increase Nitrogen-Use Efficiency and Protect Environmental Quality for Temperate and Tropical Regions

Offsite transport of nutrients is a significant source of nonpoint pollution for surface waters. Using a geographical information systems (GIS), a global positioning system (GPS), and other technologies to monitor soil and crop responses, farmers can develop management zones to better manage resources, reducing the effect of nutrients on receiving waters. These and other technologies offer the potential to help develop more effective best management practices (BMPs) for water-quality management.It has been reported that overall nitrogen-use efficiencies (NUE) average 50% or less (Newbould, 1989). The worldwide NUE for cereal production is reported to be about 33% (Raun and Johnson, 1999). For these cereal cropping systems, the estimated global loss is equivalent to $15.9 billion per year for the 67% uncounted nitrogen (N). Other researchers have reported use efficiencies of about 50% for potassium (K) and about 10% for phosphorous (P) (Baligar and Bennett, 1986a & b). A need exists not only to improve these nutrient-use efficiencies that result in billions of dollars in losses worldwide, but also to reduce the transport of nutrients out of the fields. Transport of nutrients by wind and water erosion or leaching mechanisms can contribute to offsite environmental impacts (Smith et al., 1990; Follett et al., 1991). It is important to continue increasing nutrient-use efficiencies to improve economic benefits for farmers and protect environmental quality from the local to the global level. Different scientists have reported that wind erosion and NO₃^–-N leaching have been identified as events that can affect soil and water quality (Pratt, 1979; Follett et al., 1991). Water and wind transport mechanisms contribute to an estimated erosion and transport of N of about 3.6 and 0.9 million metric tons, respectively (Legg and Meisinger, 1982). Continued development of viable BMPs that can minimize these losses and contribute to increases in NUE are needed. These BMPs need to consider that when maximum yield has been reached, any additional N will significantly increase the levels of NO₃^–-N available to leach (Pratt, 1979).It has been reported that although N deficiencies will lower yield, excessive N applications can affect the quality of grains, tubers, and other cropping systems. For malting barley at higher-than-needed N applications, quality of the grain can be affected due to higher levels of proteins (Zubriski et al., 1970). Higher N rates can also affect tuber quality for potatoes (Laughlin, 1971; Painter et al., 1977; Westermann and Kleinkopf, 1985) and sugar beets (Hills and Ulrich, 1971; Cole et al., 1976). When quality is an important factor in economic returns, fine tuning of BMPs could have significantly greater impacts in NUE and therefore in economic returns to farmers by maximizing yields while improving product quality.Delgado (1999) reported that for cropping systems of the San Luis Valley of south central Colorado, residual soil NO₃^–-N was correlated with soil type and texture. These potato-malting barley cropping systems, which were grown in fields with soil spatial variability, had higher residual soil NO₃^–-N in the sandy loam than the loamy sand areas (Delgado, 1999). If farmers do not account for spatial variability of residual soil NO₃^–-N, using a single sample soil analysis for the whole field, they will be underapplying N fertilizer in the coarser areas and overapplying in the finer-textured zones. For these areas of higher residual soil NO₃^–-N and higher N fertilizer applications, the malting barley will be susceptible to lodging due to higher N uptake. Problems in tuber quality can also occur for the potato-malting barley rotation. This article presents examples of new technologies and innovative tools that can potentially be used to increase NUE and improve N management. We present specific cases from south central Colorado that can be applied to other geographical regions, such as the Caribbean Basin.

Figure 1. Variability of cation exchange capacity (meq/100g) across a center-pivot irrigated sprinkler (54.7-ha) system. Soil samples were collected with a DGPS using 0.405-ha grid sampling technique with one randomized sample collected within each grid.The island of Puerto Rico, with about 2.2 million ac., has a great variability as far as the number of soil orders (classified by the United States soil system) that have been identified (Lugo-Lopez and Rivera, 1977). Topography and position can affect the soils’ physical and chemical characteristics (Schimel et al., 1985), fate of the added N, and its dynamics and compartmentalization (Delgado et al., 1996). We also expect to have the same significant variability across tropical soils as we see in temperate soils (Figure 1). This variability in chemical and physical soil characteristics can affect the yields and crop responses as well as the NUE. New technologies are being developed, calibrated, and tested to improve the management of agricultural resources.Positioning SystemsNew advances in technology are constantly being transferred to the management of agricultural fields. We now have the opportunity to use positioning systems to develop precise records of all agricultural operations, contributing to better management and use of the resources. Generally, positioning systems electronically record the location of equipment, people, objects, or benchmarks. A GPS uses satellite technology to record geographical locations. These technologies are becoming widely used in agricultural management.For security reasons, the US Department of Defense (DOD) introduced an error to the GPS signal that degraded its accuracy. In May 2000, the US government decided to stop this degradation of the signal. Before May 2000, an approach called differential global position systems (DGPS) was used to compensate for or reduce the error. Each GPS receiver determines its unique global position by measuring its distance from satellites in space. Since the satellites are constantly transmitting positions and timing signals, GPS uses a process called ranging, utilizing the time delay to calculate the distance from each satellite. The real-time kinematic GPS can make calculations in seconds with accuracies at the subcentimeter level (Zuydam, 1999). Application of GPS and GIS to Monitor Soil and Crop ResponsesGIS technologies store, organize, and manipulate geographical data. To navigate to specific locations, DGPS can be linked with GIS and a laptop computer or other handheld device, allowing navigation to specific locations where geographic position can be recorded for the sample. DGPS and GIS can be used to create field boundaries for mapping, crop diseases, soil sampling, variable-rate fertilizer applications, and many other agricultural applications. The DGPS antenna can be anchored to a vehicle (truck, tractor, harvester) and connected to a laptop with GIS software, recording sampling locations or crop yields throughout the field. These data can be analyzed and model the spatial variability of the field. These models can be used to develop variable-rate recommendations and create maps for GIS. Variable-rate fertilizer equipment can then be used with these GIS maps to apply specific amounts of fertilizer throughout the field.

Another technology that can be used with a DGPS is the Veris model 3100 sensor cart (see Note at end of article) that identifies soil variability by measuring soil electrical conductivity (EC) (Lund et al., 1999). Lund reports that an EC GIS map can be used with other information to help understand the spatial chemical and physical variability of a specific field. These maps can contribute to improvement in such management practices as liming operations and variable-rate application of nutrients.

Figure 2. Correlation between tuber yield and chlorophyll values for irrigated potato grown (85 days after planting) in the San Luis Valley of south central Colorado.There is also potential to use these DGPSs with quick field methods to determine N status during the growing season. Different authors have used the SPAD 502 chlorophyll meter to correlate N levels with yields and with N status during the growing season (Turner and Jund, 1991). Our data show that potato yield from tubers grown in the San Luis Valley is correlated with SPAD 502 chlorophyll readings, 85 days after planting (Figure 2). There are also other quick field methods that can be used to measure sap NO₃^–-N concentrations; these methods have also been correlated with standard dry-tissue NO₃^–-N laboratory analysis. The test-strip method to measure NO₃^–-N sap concentrations has been correlated for vegetables (Scaife and Stevens, 1983) and small grains (Papastylianou, 1989). Additionally, the Cardy meter method to measure NO₃^–-N sap concentrations has been correlated in potato (Westcott et al., 1993), cauliflower (Kubota et al., 1996), broccoli (Kubota et al., 1997), other vegetables (Hartz et al., 1994), and winter cover crops (Delgado and Follett, 1998).

Figure 3. Using a DGPS, a GIS, a laptop, and a navigational program to collect aboveground and belowground potato samples from irrigated systems of the San Luis Valley.There is great potential in combining these quick field test methods with the DGPS technology. The DGPS can be attached to a backpack and used when collecting crop samples to associating plant and soil samples (Figure 3). King et al. (1999) found that potato petiole NO₃^–-N concentrations were correlated with soil water-holding capacity, percent sand, percent clay, and soil organic-matter content. A laptop or palm-held computer can be used to navigate; the user walks with the DGPS to the same position where the initial soil sample was collected. The DGPS can be used with a chlorophyll meter and connected through an RS-232 data port to a data logger that automatically records the chlorophyll readings and DGPS position. The DGPS can then be uploaded into the portable computer for further analyses. These DGPS and quick field-test technologies can also be used with remote-sensing images. The remote-sensing images could be used to determine N indices and to identify deficient areas. Bausch and Duke (1996) developed the nitrogen reflectance index (NRI) to detect N deficiencies in corn (Zea mays L) (Bausch et al., 1996). They used the ratio of the canopy reflectance in the green (G) and near-infrared (NIR) portions of the electromagnetic spectrum, NRI = (NIR/G)_target / (NIR/G)_reference. Nitrogen content, chlorophyll status, remote sensing, and nitrogen indices can be used to evaluate N status across the field during the growing season. These techniques can help determine if there are any areas in the field that need additional N application. There is potential to use this new technology to improve the efficiency of split applications of N and to protect environmental quality.The NRI accounts for the separability of stressed versus vigorous crops as it is related to the relationship between plant available N and chlorophyll concentration. A healthy plant with vigorous growth has higher chlorophyll content that an N-deficient plant. The reflectance of a healthier plant in the green electromagnetic spectrum (near 530-570 nm) is lower than that of an N-deficient plant because the healthier plant has higher chlorophyll concentrations that will absorb more energy in the green band. On the other hand, the reflectance on the green band by an N-deficient plant that has a lower chlorophyll content is higher. Additionally, the reflectance at the NIR 780-850 nm is higher for the healthier plant because of higher biomass production than in an N-deficient plant.

Figure 4. NLEAP simulation of residual soil NO₃^–-N for the 0- to 3-ft. depth of 12 irrigated fields of the San Luis Valley.Computer simulations models can also be used as tools to evaluate the NUE under different combinations of cropping systems and management scenarios. One such model is the Nitrate Leaching and Economic Analysis Package (NLEAP) (Shaffer et al., 1991). NLEAP has been used to predict NO₃^–-N dynamics across different cropping systems (Delgado et al., 2000 and 2001; Delgado, 2001) (Figure 4) and has been found to perform similarly to other models (Khakural and Robert, 1993; Beckie et al., 1994). Different authors have reported that there is the potential to use NLEAP for evaluating the effect of precision farming on NO₃^–-N dynamics and NUE (Wylie et al., 1994; Shaffer et al., 1995; Delgado, 1999).Grain- and Potato-Yield Monitoring New technology uses yield monitors and DGPS to measure real-time yield responses at harvest across small-grain and tuber fields. Different sensors can be used to measure the force or displacement of grain as it moves across the grain elevator or conveyor. An impact plate measures the force of the grain or load cells and can be used to measure the weight of the grain as it passes through the combines. Weight sensors are also used for measuring the tuber weight as it passes through the conveyor path of a potato harvester. The grain system also uses sensors to measure the grain moisture content.These systems measure ground speed of the harvesters to calibrate the position where the harvester is moving. Some yield monitors allow for a time delay to account for the position where the crop was harvested. Yield monitors are also calibrated to improve the accuracy of the systems. Farmers can have an instantaneous measurement of the yields as they are displayed in the console. Computers installed in the console record all this information, which can be saved to PC cards and transferred to personal computers for later analysis. Yield maps can be generated daily from this information and used with geostatistical analysis to develop new management approaches for site-specific small-grain–potato rotations.

Remote Sensing With Multispectral Video Mapping Systems Implementing multispectral video mapping systems (MVMS) for remote sensing involves the integration of standard off-the-shelf hardware and software products. While there are many options in selecting the ultimate functionality of MVMS, a basic system incorporates digital multispectral area-scan optics with GPS, a data storage medium, and an interactive GIS software environment for navigation and analysis. These systems generate image data of varying spatial and spectral resolution that can be used to determine spatial variability of crop health. These technologies record GPS data that are encoded into an audio signal by VMS 200 hardware (Red Hen Systems Inc.) once per second and recorded on a single audio track of the storage media (analog or digital videotape). Using this system of archiving GPS data aligns position information adjacent multispectral image data frames on the storage media. Because NTSC video protocol delivers 30 image frames sec^-1, a single position fix corresponds to multiple image data frames acquired during the one-second GPS position pulse. With this observed amount of overlap in subsequent image data frames, scene stereo coverage becomes a byproduct of image data acquisition.

Figure 5. Remote-sensing image at near-infrared wavelength shows potato growth on an irrigated center pivot of the San Luis Valley. The light area going from top left to center bottom shows a topographic sequence and soil texture effect with lower red intensity and lower biomass production. Two other light areas can also be seen at bottom left.To acquire image data using MVMS, users elevate the system over a crop canopy while maintaining a GPS fix on the aerial remote-sensing platform. Conventional fixed-wing aircraft may be modified to accommodate vertically mounted optical components of MVMS. Using a laptop computer and Media Mapper software (Red Hen Systems Inc.) during image data acquisition enables real-time navigation over such background data as natural features, roads, waterways, and field boundaries. Users can view GPS data, including altitude, speed, and heading, while viewing current position over a digital map. This navigation system is especially useful in targeting scene locations from the air (Figure 5). Auxiliary optical components of varying spectral and spatial resolution can be used in MVMS. For video-based multispectral imaging, a DuncanTech MS2100-series (Duncan Technologies) camera is recommended. This system delivers 25 frames sec^-1 to storage media. With the charge coupled device (CCD) detector array having a 640 x 480 multispectral pixel resolution, spatial resolution of image data from the MS2100 can exhibit 1- to 2-m ground resolution for quarter-section-size image scenes. Higher-resolution CCD arrays are available; however, NTSC video bandwidth cannot support higher than 640 x 480 image data resolution. To enable higher (up to 1392 x 1040) spatial resolution of image data, a computer with framegrabber hardware must be used on the aircraft.One powerful attribute of acquiring image data using MVMS is the ability to review and analyze these data immediately after the aerial mission is complete. Because image data are stored locally on tape (or hard disk for 1392 x 1040 resolution), the analyst does not have to wait to be provided with image data. Instead, the analyst must review mission scenes and select image data for analysis. The following steps must then be completed to enable spatial analysis of the data. These images can be georegistered (Figure 6).Potential Applications and SummaryOur initial research shows the potential to use these new technologies to improve and develop new BMPs that can contribute to better NUE and protect environmental quality. The potential application of these practices, however, will depend on the variability of each individual field. Furthermore, the economical viability of these practices will also depend on the potential benefits and crop responses, such as higher yields, improvement in product quality, and best use of such inputs as fertilizers, lime, and herbicides. These practices can also be used to apply other resources, such as compost materials to specific areas of the fields, application of gypsum for reclamation of specific areas, and other management practices that can help improve the physical and chemical soil properties, increase the nutrient and water availability, and improve yields.

Figure 6. Navigational system displays DGPS data, including altitude, speed, and heading, while showing current position over a digital map.The application and use of these new technologies require a planning period in which soil testing and data analyses are conducted. The generation of maps that can be used with variable-rate technology is the initial application. There is also the potential use of these technologies for improving management during the growing season. Remote sensing supported with crop scouting (Figure 7) and generation of additional variable-rate maps is also a potential application. The harvesting season will then reflect the yields and response to these new management practices. After the first cycle is completed, additional evaluations are necessary to improve the management with following cycles. The use of computer models that can simulate cropping systems is a tool to evaluate the NUE under different management scenarios and the potential contribution to the protection of environmental quality. By increasing nutrient-use efficiencies, we can reduce nitrate leaching and protect underground water quality. These technologies can also be used to improve soil conservation and soil quality, reduce offsite transport of chemicals, and protect surface water bodies.

Figure 7. Georegistration of remote sensing will facilitate the development of precise geographic information maps for application of nutrients during the growing season.Farmers and consultants are using these practices in south central Colorado to develop new management zones. The research effort needs to continue with these new technologies, which are allowing farmers to reallocate the application of products according to their low-, medium-, and high-yield areas in the field. Using the variable-rate technology creates the potential to increase yields in the high-yield areas and minimize the cost by reducing inputs into the low-yield areas. There is also potential to use variable-rate applications of lime to improve the pH across the field and the availability of nutrients. Variable-rate herbicide applications offer other new important uses of these technologies. There is potential to use these practices in the tropics to develop management zones for better use of resources and protection of environmental quality. We will be applying some of these new technologies to develop and implement a management plan to reduce nonpoint pollution in the coastal waters of the Jobos Bay national estuary reserve in southern Puerto Rico.

Figure 8. Aerial view of tropical cropping systems north of the Jobos Bay national estuary reserve in southern Puerto Rico.The objective of this cooperative research project is to implement new technologies and alternatives, such as the use of computer models, GIS, and precision farming to improve the management of nitrogen fertilization, and increase yields and economical returns of multiple tropical cropping systems.Figure 8 shows the land-use area in this region. These studies will help model the fate and transport of nitrates within the planted area and make recommendations on the rates, frequencies, and timing of fertilizer applications and the scheduling of irrigation to reduce leaching. These studies will also support the development of a database needed to improve nutrient management practices of these tropical soils. These technologies can potentially be used to improve the management of agricultural and natural resources to reduce environmental impacts to water bodies. Note
Names are necessary to report factually on available data; however, the USDA, CSU, UPR, and AGRO Engineering neither guarantee nor warrant the standard of the product, and the use of the name by the USDA, CSU, UPR, or AGRO Engineering implies no approval of the product to the exclusion of others that might be suitable.

Use of Innovative Tools to Increase Nitrogen-Use Efficiency and Protect Environmental Quality for Temperate and Tropical Regions

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