Date: Mon Aug 1, 2016
Time: 1:40 PM - 3:40 PM
Environmental and economic demands make it necessary for farmers to adopt management systems that improve Nitrogen Use Efficiency. The premium paid to producers has made farmers striving for maximum grain protein levels because protein is a very important quality component of grains and an important attribute in the market place. The protein content of wheat grains approximately ranges from 8 to 20%. The optimization of nitrogen (N) fertilization is the object of intense research efforts around the world. Soil tests are capable of estimating the intensity of N release at any point in time, but rarely the capacity factor over a longer period. Fluorescence sensing methods have been used to monitor crop physiology for years, and they may offer solutions for N status diagnosis over reflectance-based methods. Developed precision management of nitrogen through precision agriculture allows growers to use fertilizer input on more productive areas as well as saving nitrogen costs across the field. On the other hand, grain protein in cereal crops as well as yield is receiving increased attention because of premiums being paid to producers. Research has shown that grain protein levels are mainly affected by N availability. This paper presents a model which was develop to determine the optimum rate of nitrogen for yield and protein content through using different indices from nitrogen sensor in winter wheat. This model will allow for mid season variable rate application of N fertilizer. For this purpose, an experiment was established to determine the effect of nitrogen on yield and protein content using a randomized block design by applying five different rates of nitrogen (0, 80, 120, 160, 200 kgN/ha) and the two different varieties of wheat. A quadratic polynomial model was best describe the relationship between nitrogen, yield and protein content for optimum nitrogen rate.
As a grass (Poaceae), sugarcane needs supplemental mineral nitrogen (N) to achieve high yields on commercial production areas. In Brazil, N recommendations for sugarcane ratoons are based on expected yield and the results of N response trials, as soil N analyses are not a suitable basis for decisions on optimum N fertilizer rates under tropical conditions. Since the vegetative parts in sugarcane are harvested, yield components such as the number of stalks and stalk height are directly correlated with crop biomass, which, at early growth stages, can be determined by a vehicle mounted optical crop canopy sensor. With the aim to investigate the relationship between the vegetation index (VI) obtained from early season crop canopy sensing and final yield, a study on three commercial sugarcane fields located in the state of São Paulo, Brazil was conducted between 2010 and 2013. The fields included in the investigation ranged in size between 10 and 16 hectares and represented typical soils for sugarcane production, with soil textures ranging from sandy to clayey, where sugarcane of different ages (1st, 2nd, and 3rd ratoon crops) was grown. The harvest of the cane occurred in September/October (end of the dry season). After the previous harvest, all fields were soil sampled (0 - 0.25 m depth) on a 0.5 ha regular grid for chemical and physical soil attributes. After sprouting, during the early season, fields were scanned with an optical crop canopy sensor (N-Sensor® ALS, Yara International ASA). On one field, these measurements were repeated three times (at approximately 0.2, 0.4, and 0.6 m stalk height) and on the two other fields just once, at 0.4 m of stalk height. After maturation, fields were mechanically harvest (no burning) with a harvester that was equipped with a yield monitor system, logging data points every two seconds. The yield data was filtered to eliminate errors and noise. Using a GIS software, buffer zones with a diameter of 20 m were created around the georeferenced soil sampling points. Average values for the measured sensor VIs and yields were calculated for the data points located within a certain buffer zone and related to each other and the respective soil properties. Finally, all factors were correlated in a matrix. From all the sampled parameters, optical sensor VI was the only one with stable good correlation with yield on all three study fields. At a stalk height of approx. 0.4 m on average in the field, correlation coefficients (r) for this relationship ranged between 0.5 and 0.6. The optical canopy sensor seems to be a valuable tool to predict in-field variability of yields. As the expected yield is the predominant factor for decisions on optimum N fertilizer supply in sugarcane production systems, this gives the opportunity for a crop sensor based variable rate nitrogen fertilizer application, aiming for improved nitrogen use efficiency (NUE) in this crop.
The agricultural research sector is working to develop new technologies and management knowledge to sustainably increase food productivity, to ensure global food security and decrease poverty. Wheat is one of the most important crops into this scenario, being among the three most important cereal commodities produced worldwide. Precision Agriculture (PA) and specially Remote Sensing (RS) technologies have become in the recent years more affordable which has improved the availability and flexibility of acquiring images from both manned and unmanned vehicles. For this reason, CIMMYT’s research agenda aims at developing new crop management practices using PA/RS technologies. As part of these efforts, a wheat experiment was established on a farmer’s field in the Yaqui Valley, northwestern Mexico, sown in January and harvested in May 2014. This work focuses on the evaluation of narrow-band physiological spectral indices to estimate wheat grain protein content (GPC). Also to determine the optimum normalized difference spectral index (NDSI) and ratio spectral index (RSI), aiming to better explore the use of the hyperspectral signal on the assessment of GPC. A weekly/biweekly flight campaign took place from GS31 stage (stem elongation) until harvest, totaling 10 airborne images acquired at high resolution with a micro-hyperspectral imaging sensor ranging from 400-850 nm region, flying at 1200 m above ground resulting in a ground resolution of 1 m. Manual grain sampling took place just before harvest through a targeted grid of 14 sampling points on block A and a half regular / half stratified grid of 50 sampling points each on block B. Under the conditions of this study, characterized by low spatial variability within the commercial field, the results obtained yielded coefficients of determination among vegetation indices (VIs) and GPC ranging from non-significant to 0.14 across all images. Complete two by two combinations of wavelengths approach applied into NDSI formula performed better on assessing GPC than VIs from the literature. However, the spectral region beyond the visible and near-infrared might be needed to assess GPC at field level. On the other hand, this approach allowed visualizing the spectral range/wavelengths that predominantly better explained GPC across the crop cycle than ordinary VIs.
Chlorophyll is one of the most significant biochemical parameters for evaluating crop status. It can be used as an index of photosynthetic potential as well as crop productivity. Crop chlorophyll content has been widely used in identifying crop growth condition, physiological status and health. Crop growth condition monitoring and prediction of crop optimal harvest date are both important to the crop final yield. Crop growth monitoring help farmer take measures in time when the crop is suffering from insect pest, plant diseases or meteorological stress. And yield loss occurs if harvest is implemented either in too earlier or delayed time, both of which are undesirable. In this paper, based on the inversion of crop chlorophyll content, we tested the application of crop chlorophyll content in crop growth monitoring and prediction of crop optimal harvest date, proposing new methods. In crop growth monitoring, we took both the individual and group crop condition into consideration, explored different monitoring indexes of them, including crop chlorophyll content and leaf area index. Then the comprehensive assessment of crop growth condition could be implemented by combining these two indices. In crop optimal harvest date prediction, leaf and stem water content decreasing as crop reached maturity, the color of leaves gradually turning to yellow, a decrease occurs in crop chlorophyll content. Optimal harvest date was predicted by analyzing the change of crop chlorophyll content with temporal variation of harvest yield. Different crop has different characteristics and we used different methods to predict soybean and corn optimal harvest date. The prediction result has been validated to be reliable. The abstract is often the only part of the paper to be read, so include your major findings in a useful and concise manner. Include a problem statement, objectives, brief methods, quantitative results, and the significance of your findings.
The development of Unmanned Aerial System (UAV) makes it possible to take high resolution images of trees easily. These images could help better manage the orchard. However, more research is necessary to extract useful information from these images. For example, irrigation schedule and yield prediction both rely on accurate measurement of canopy size. In this paper, a workflow is proposed to count trees and measure the canopy size of each individual tree. The performances of three different methods to classify tree canopies are compared. Then morphological methods are used to filter grass patches and separate the trees from each other. Finally, the number of trees and the size of tree canopies are obtained.