Prediction of Maize (Zea Mays L.) Population with Normalized-Difference Vegetative Index (NDVI) and Coefficient of Variation (CV)
Prediction of Maize (Zea Mays L.) Population with Normalized-Difference Vegetative Index (NDVI) and Coefficient of Variation (CV)
Introduction
With the escalation in cost production and environmental concerns, researchers have in the recent past focused in investigating resourceful means of increasing yields while reducing fertilizer use. Arnall & Godsey assert that the seeding rate and hybrids that promise the highest crop populations is continuously sort by corn cost cultivators worldwide (2013). Nielsen et al. notes that the crop populations are tied to the end yields. Corn populations have greatly increased in Indiana over the past thirty years to make about 340 plants per acre (PPA) (2016). The producers have the need to establish a well-documented cropping system to account for the production of their corn. In essence, numerous factors are involved in determining the planting population such as the seeding rate, seeding costs, planting date, nitrogen (N) fertilizer, cover crops, crop rotation among others (Arnall & Godsey, 2013). The factors enable the farmer to maximize the crop population within a particular field. The goals of grain production are imperative decisions made each season by all producers globally (Chung et al., 2008). According to Bushong et al., the plant population is correlated to seeding rate and row populations (2016). If the plant population is high then crops compete with each other leading to lodging. On the other hand, if the population is low then the farmer is under exploiting the farm and reducing the chances of harvesting optimum yield.
According to Arnall & Godsey the seeding rate, row spacing, plant calibration, germination rate, and seed placement determine the plant population (2013). Seed placement, the depth, soil tillage, and type determine how many plants survive after planting. Seeds are placed shallowly in heavy soils. According to Arnall & Godsey ,when seeds are placed uniformly, the emergence is also uniform, thus, ensuring healthy plant-to-plant competition (2013). The seeds costs determine the seeding rate. The farmer should assess the percentage of pure grains and other characteristics such as germination percentage to determine the desired plant population. Most of the seed companies tests the seeds before they sell their seeds, and thus, report on the potential germination rate, ideal moisture, and temperatures (Freeman et al., 2007). Bushong et al. observed that soil moisture is essential in determining the overall plant population (2016). It is imperative to note that soil moisture is the available amount of ground water stored in the soil profile, which a plant can use when growing.
Freeman et al., argues that the existence of Nitrogen in the ground is spatially determined by the soil interactions, the weather, and the cultivator management systems (2007). The normalized difference vegetation index (NDVI) utilizes the N in the soil to determine the soil ability to handle a plant population density. The plant population is attributable to these factors too together with other variables such as soil texture, landscape position, the history of crops, soil chemical, physical properties, and availability of nutrients (Martin et al., 2012). However, they do not agree what causes the variability. Evidence collected by Edmonds, showed that management for nitrogen input is the only variable that caused plant population variability (Edmonds et al., 2013). The nitrogen temporal variability is responsible for the spatial variability even in the fields that are irrigated. High amounts of Nitrogen are required to enable the realization of high output of corn (Martin et al., 2012). With this evidence, it is possible to conclude that the quantity of maize harvested, its growth and development are correlated to the actual amount of Nitrogen in the soil. However, it is hard to address the difficulties accrued to Nitrogen variability because of the inability to differentiate between temporal and spatial availability and the level of variability (Martin et al., 2012).
According to Arnall et al., various sensors have been used to address the problems of spatial and temporal variability (2006). The remote sensors are used to estimate the potential plant populations correlated to the cultivator environment through assessing water, nutrients, and other factors when controlled (Martin et al., 2007). The first remote sensor developed was known as normalized difference vegetation index (NDVI) which calculates the number of plant per acre. The coefficient of variation (CV) determines the variability of the NDVI measurements statistically (Arnall et al., 2006). Martin et al., states that CV is a ratio expressed in a percentage form calculated as a standard deviation to mean (2007). According to Martin et al., CV has been used before by various scientists in determining the variability of reports of grains output of which nutrients addition experiments. This study is not different; it will incorporate the use of NVDI and CV in determining the maize population within a particular area. NVDI will calculate the variability measures while CV will evaluate the spatiality of the grain growth. In essence, the study will determine whether there is any relationship between NVDI, CV, and the grain population (2007).
Literature review
Plant placing determines the plant population. Narrow spacing with less equidistant spacing increases plant population. Nielsen et al. reported that decreased soybeans spacing from 0.76cm to 0.38m doubled the realized crop population (2016). Agronomic decisions are made about goals of grain yields. The yields are determined through numerous processes. A grain yield is measured for potential and the estimated amount is the quantity harvested on the site, while the possible value is forecasted for a given period in a particular place with assumptions that the growth factors are maintained (Martin et al., 2012). In addition, the maximum amount can only be realized in a controlled environment where the crops get the growth factors in the required quantities. According to Martin et al., various soil factors influence the amount of Nitrogen in the ground. Owing to several studies, soil moisture has shown significant impact on the amount of yield (2012). Historically, Nitrogen is recommended to increases the output harvested to the potential quantity. The recent advancement in technologies including the sensors and modifications in weather and crops has enabled adjustments of Nitrogen in-season, which allow the farmers to achieve potential yield (Martin et al., 2012).The three past decades show that the methodologies that incorporate sensors are responsible for the many agronomic decisions made within any farm. Raun & Johnson notes that the fundamental significance of NVDI was realized in witness of wheat at the growth stages four and five, which allowed successful prediction of both the plant biomass and uptake of Nitrogen (1999).
Raun & Johnson notes that there was also a report of dormancy of NVDI results, which was later recorded as a perfect predictor of the crop yield, which can be used to calculate the Nitrogen use. Over 30 years scientist continually used winter wheat grain yield to investigate the accuracy of NVDI. However, in the recent past, the work was shifted to grain yield. The reports calculated showed that in-season estimate of yield (INSEY) has a non-linear relationship with the grain yield; thus, giving a biomass collection of each day. In the past, researchers continuously tried to incorporate soil moisture in the sensor technology measurements while predicting the yield of wheat (199).
Girma et al. used NVDI soil moisture measurements to determine the grain yield during winter (2008). The factors considered included chlorophyll, height, tiller density, canopy temperatures, plant density, leaf color, and soil nutrients such as Nitrogen, PH, and organic Carbon. The factors are good predictors of wheat grain yield although it is not a reliable prediction. Mourtzinis et al. used soil water to predict wheat grain yield using NVDI in the mid-season (2013). The combination of NVDI approach and data at planting time were combined which accurately predicted the grain yield. According to Raun et al., temperature also affects the growth of crops and their development (2005).
Heat units accumulation have been used continuously and accurately to predict the crop development through utilizing the basic physiological stages (Teal et al., 2006). The approach is better compared to uses of some days and time. The errors associated with cardinal temperatures have small consistent errors related to crop development stages and the processes. According to Teal et al., other studies consider utilization of the concept of biological days to show plant growth, and thus, declaring optimal temperature unfit for assessing crop growth solely (2006). The scientists believe the inclusion of photoperiod, and water or nutrients stress while predicting the crop population. Another factor, water EvapoTranspiration (ET), assists while predicting the grain yield (Martin et al., 2012). The soil moisture content is analyzed to show whether there is available water to enable crops to mature. The evaluation of the ratio of the available soil water for a plant and the potential evaporative quantity determined the Stress index (SI) which affects the final crop yield (Martin et al., 2012). There was significant correlation between the crop yield and the SI that led to a conclusion that adequate water is necessary for crop growth and the final yield (Chim et al, 2014).
According to Crain et al., the biggest challenge of farming is to identify the spatial variability that will give them the required net returns (2013). Most of all the other studies concentrate on managing the spatial variability amongst most of the farming products such as corn. The spatial variability is the significant tool for maximum yields; however, the variability keeps on changing depending on levels of fields, which is treated like the field constituent. A field component is derived by a size of an area that has parallel nutrients and growth status for plants. Beyond this area part, the crops planted require different treatments (Crain et al., 2013). Conversely, a treatment that is smaller in a part of the field element is inefficient since the whole element calls for equal treatment. Although this idea of field element sounds practical, it is tough to determine the size of each item, especially for large farms. Many studies have continuously discussed ways of identifying a field element. One of these approaches is the 97-m grid that maximizes the precision of the field element in agriculture (Arnall et al., 2006).
Martin et al. asserts that Remote sensors are commonly used in agricultural experiments today (2007). Many varieties of remote sensors have been established with each having remote applications from previous revisions and upgrades to increase their efficiency. Raun et al. noted that indices found on the sensor also vary, and thus, for this study NVDI will be used to predict the corn population by use of Nitrogen fertilizer (2005). A major study that utilized NVDI examined wheat production. The scientists used the spectrometer to establish the relationship of the Nitrogen, Phosphorous (P) uptake, and the wheat biomass (Martin et al., Raun 2012). The study utilized both the numerator and denominator to give a substantial prediction of the wheat biomass, Phosphorous and Nitrogen uptake at all stages of corn production. Martin & Raun discovered that each version of sensor gave better results of NVDI measurements (2012). NVDI measurements taken during planting predicted the Nitrogen uptake and were related to the end wheat grain yield. The formula used is NDVI=(ρNIR-ρRed)/(ρNIR+ρRed), where ρNIR is a fraction of the emission of infrared radiation from the sensed plants while ρRed is the portion of red radiation emitted from the detected plants (Martin et al., Raun 2012).
According to Chim et al. seed spacing shows the spatial distribution of the crops, the canopy structures, light, efficiency distribution, interestingly, the biomass, and the grain yield (2014). Nielsen et al. notes that different spatial selections realized in varying row spacing affects the plant density, since, it influences crops resource competition (2016). Consequently, the population and distribution of these plants in the field result in the final yield. The plant population per square meter determines the nutrients undertaken from the soil (Chim et al., 2014). Uneven crop distribution gives less maize yield unlike when uniformly distributed. The corn yield is detrimental if the plants are extremely distributed. Proper plant spacing increases the yield because it enables the crops to absorb nutrients adequately. Plants should be given a spacing of about 0.05 to 0.07 m (Chim et al., 2014). The equidistant spacing gives the yield a more advantage than the undistributed spacing. The spacing gives the plants an increased resource utilization and absorption. The quantity harvested increases with increase in the plant density although the population might at times be uneconomical. Plant population is sensitive because extremes give plants stress thus reducing the expected yield.
On the other hand, less plant population gives a low quantity due to lack of exploitation of the resources. The only problem with improper plant community is there is plant competition for the nutrients. However, the extent of influence depends on with the environment and hybrid conditions (Martin et al., 2012). Each mixture of maize has the minimum nutrients and conditions they can thrive. Plant population within a row determines the light access and thus photosynthesis and the end yield. The farmers have the ability to manipulate the plant spacing to increase access to light for each plant. In the normal conditions, the growth of crop yields is related to how much light they intercept. Maximizing the light interception is significant in corn production to acquire optimal return. The light necessity for corn is required during early reproductive periods and vegetation (Martin et al., 2012). High plant densities and narrow spacing give the plants the chance to access adequate light. Nitrogen uptake is closely related to plant spacing just like all the other nutrients. Narrowing the maize crops allows them to utilize the Nitrogen exhaustively which otherwise could be lost. The proper intake of Nitrogen allows optimal corn yield. Numerous studies have supported that the crop spacing, Nitrogen uptake, and seed distribution are significant to crop population (Girma et al., 2008).
The majority of the agricultural production that used NVDI was based on wheat production solely for prediction of the yields. Corn is the second largest product cultivated globally and that is why sensor production is being adopted (Edmonds et al., 2013). The sensor enables a cultivator to predict accurately the yield that is likely to be harvested. The knowledge of the return likely to be collected from a certain field allows the farmer to be able to maximize the management practices on the field. The management decisions made are somehow based on an individual platform. The impact of the knowledge used is seen if the farmer relates and understands the yield harvested about the information. Various researchers in the past have used N to show how corn expected yield is correlated (Edmonds et al., 2013).
It is essential to note that Nitrogen is the usual limiting factor in most of the studies in the different production systems and accounting for the production costs. Therefore, it is easier to relate to the data collected on the yield and yield relationships potential estimates that were calculated on the wheat production (Teal et al., 2006). The growth strategies are crucial while determining the yield. Each crop has the appropriate development stages that are used while investigating the yield potential. According to Bushong et al., the remote sensors are suitable for reflecting and monitoring the amount of Nitrogen in a crop and the amount required to be added (2016). However, other scientists used the Green-Seeker hand sensor to determine the individual plant NVDI difference (Teal et al., 2006). According to Girma et al., the capacity to test these variations was seen as the V8 psychological stage that disappeared at V10 development stage. The sensor technology also enables farmers to be able to quantify yields and their variability in the field. The sensors give spatial resolution images that show the minimum distance between two different objects. Nutrients cause plants to have variability depending on whether a plant gets excessive or inadequate amount in the place it is located in the firm. These soil nutrients vary beyond 1 m2, and thus, plants beyond this area with have different characteristics (2008). Moreover, the plants characteristics within this area will depend on with the amount of nutrient added. Subsequently, many reports from previous work show the variability in soil nutrient has negative impacts ion the end.
According to Martin et al., CV, statistical parameter, is used to assess the variability of the NVDI data collected (2007). The CV is a ratio equivalent to the ratio of the standard deviation and mean. CV has been used in various calculations involving wheat production. The CV assesses the effects of added nutrients during any season of production. At the same time, the researchers discovered that the NVDI was related to the yield biomass and the corn population. The NVDI measurements recorded in various projects show that the corn grain in the fields varies from one plant to the next. The sensors have developed to the point they can evaluate characteristics of each plant. Each advance in the ability to predict the final yield is a crucial advantage while making agronomic decisions (Martin et al., 2012). The study concentrates predicting the population of maize by use NVDI.
Experiments will be conducted to evaluate the population of corn grain depending on the seed distribution within a particular locality. Block designs that are completely randomized are used while carrying out the experiments during seed treatments and replications. The seeds are treated in factorial processes of 1, 2, and 3 with inter planting of 0.16, 0.32, and 0.48m respectively (Arnall et al., 2006). The treatments will be planted with Pioneer P1498HR corn hybrid of spacing 0.76m. The seeds will be placed in holes then the holes covered by foot. Each site has rows approximately 70 to 100 rows with a starting point of less than 20m to prevent the border and end row effects to the crops. The 130kgNha at the rate of 180 will be applied as urea before planting. Four sites are set aside for the practical. One site will be irrigated while the others will be left to be rain fed (Martin et al., 2012).
Thorough preparation of landing including tilling and harrowed will be carried out. Herbicides use for controlling weeds and irrigation materials are set aside to mitigate the loss of water, if there is less rain during the growing period. The preparation and planting dates, maturities, and another relevant information are duly reported. Each plant will be tagged at the base and depending on the rows, measured and recorded. The location of every plant must be noted to allow the calculations (Arnall et al., 2006). The Green-Seeker sensors from NTech Industries Inc. will collect data for the NVDI records of every plant. The experimental plots will be assessed through NVDI at different stages (Martin et al., 2012). The sensor is automatic and records the average of the measurements. The Green-Seeker will be hand held while carrying out the measurements. Finally, the CV of the NDVI will be calculated for each row at every respective stage of growth.
References
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