Recipient Organization
MICHIGAN STATE UNIV
(N/A)
EAST LANSING,MI 48824
Performing Department
PLANT SOIL MICROBIAL
Non Technical Summary
Alfalfa was the third most valuable field crop in the USA in 2017, but acreage has been decreasing over the last ten years, primarily losing ground to the greater feed energy production per acre of corn silage. This land use change has societal implications because alfalfa provides important soil-related ecosystem services when included in crop rotations, such as biological N fixation, soil carbon sequestration, reduced soil erosion, and improved soil structure, none of which are provided by corn. High-tech, big-data, and precision agricultural technologies are available to assist growers of other field crops, but methodologies suitable for alfalfa production have generally lagged behind. Alfalfa's multiple harvests per year require a constant compromise between yield and quality because the feeding value of a perennial forage declines as yield increases. Timing of each harvest is therefore critical. Cost-effective remote sensing technologies that can predict yield and quality potential of field of alfalfa in real time could be invaluable to informing harvest decisions and thus improving management and profitability of alfalfa. We propose to begin the process of developing pre-harvest remote sensing technologies for alfalfa by building a forage spectral library that can ultimately be used for drone- and satellite-based remote sensing systems, high performance computing, and modeling to permit the rapid and efficient application of precision agriculture solutions on a landscape scale in diverse managed alfalfa and forage systems. On the other end of the value chain from growers, we have stakeholders who feed alfalfa, to whom post-harvest forage quality is the most important factor. Near-infrared spectroscopy (NIRS) evolved as a laboratory-scale technology for assessing post-harvest forage quality, but instrumentation was too large and costly to be feasible for on-farm use. Today, NIRS technology is marketed directly to farmers in units as small as a cell phone. Farmer are bombarded with advertising for these new devices, but there is little objective data comparing accuracy or precision of these products versus laboratory-scale NIRS instruments. The primary uses for post-harvest analysis of alfalfa nutritive value are hay pricing and ration balancing. Being able to predict forage constituents on-farm can potentially save time and money over shipping samples to a testing lab, but a small accuracy error can potentially have large negative impacts on livestock performance. Therefore, there is a pressing need for objective comparative information to assist in use of these new devices. We will collect this data, develop educational programs to train students and farmers in technology use, and develop new calibrations designed for hand-held units. The project will conclude with a conference that brings together forage -based precision agriculture technology.This project will bring the precision agriculture revolution to alfalfa management by developing and adapting cutting edge spectroscopic technologies for use on-farm. The development of inexpensive, accurate, real-time producer-accessible forage mapping tools and forage analysis is a game-changing improvement over the current situation. By enhancing profitability, the project should increase alfalfa acreage along with its positive ecosystem services, thus addressing an important component of the broader question of how to sustainably feed the ever-growing world population in the face of increasing global demand for meat and dairy.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
(N/A)
Goals / Objectives
Our project goal is to address the explosion of precision technology related to two emerging issues: 1) effective use of existing hand-held and small farm-scale benchtop units to estimate post-harvest forage nutritive value on farm, and 2) development of new landscape-scale remote sensing technologies for pre-harvest estimation of alfalfa yield and quality.Specifically, our objectives are:identify spectral signatures for pre-harvest alfalfa and its primary companion grasses,use spectral unmixing algorithms to determine pre-harvest yield, nutritive composition, and abundance of alfalfa and grass in a mixture,develop extension materials to assist in adoption of precision technologies in alfalfa production.
Project Methods
Objective 1. Identify spectral signatures for pre-harvest alfalfa, companion grasses, and mixtures:Task 1.1. Collecting spectral and supporting data from alfalfa and grass monocultures: We will utilize a high spectral-resolution field spectroradiometer with a spectral range of 350-2500 nm and with bandwidth resolution of 3 nm at 700 nm and 8 nm at 1400/2100 nm. Spectra will be collected from existing alfalfa and grass variety test plots and alfalfa management trials at three locations representing the range of growing conditions in Michigan (East Lansing, Lake City, and Chatham). Sampled plots will be selected from existing test plots in Michigan to provide a cross-section of commercially important traits and characteristics, including stand ages, fall dormancy ratings, pest and disease resistance ratings, glyphosate resistance, and reduced lignin traits. Supporting data, including weather conditions, forage height, chlorophyll content, leaf area index, photosynthetically active radiation, soil moisture, vegetation coverage, forage yield, moisture content, nutritive composition, and phenological stage will be obtained from each plot. Nutritive constituents will be predicted using near infrared reflectance spectroscopy (NIRS, Foss 6500, Eden Prairie, MN) and a universal mixed forage equation (NIRS Forage and Feed Testing Consortium, Hillsboro, WI) and validated using laboratory chemistry for 20 randomly selected samples per year. This prediction-validation method reduces cost of nutritive value analysis and can produce acceptable prediction equations in remote sensing applications.Task 1.2. Performing statistical analysis to predict alfalfa yield and nutritive composition, and to discriminate forage species: The pre-harvest predictions of greatest interest to alfalfa growers are yield and nutritive value because these help determine when to harvest. We will use partial least squares regression (PLSR) with a stratified random sampling of 70% training and 30% test data to test if we can accurately predict alfalfa yield and nutritive constituents from the pre-harvest plant spectra. This process is similar to generation of global equations for NIRS constituent predictions in testing labs, but the uncontrolled nature of the field environment presents a greater challenge.Task 1.3. Designing the forage spectral library: We propose using NoSQL (Not Only SQL) databases. Using NoSQL databases is gaining momentum with companies such as Microsoft, Oracle, and Amazon. Cyber-Platforms based on NoSQL databases, such as MongoDB, provide a flexible and dynamic schema that can be changed on the fly. In this platform, records with different sets of properties can be stored, which is very useful for spectral databases containing landscape- and field-scale data with different sets of attributes for each spectra.Objective 2. Use spectral unmixing algorithms to determine pre-harvest yield, nutritive composition, and abundance of alfalfa and grass in a mixture:Task 2.1. Establishing and maintaining alfalfa mixtures for spectra collection: Plots will be established in spring 2019 at Hickory Corners and East Lansing, MI, to provide two different growing environments for evaluation of spectral prediction of pre-harvest yield and nutritive composition in alfalfa and alfalfa-grass mixtures. This is small plot trial with a randomized complete block design and four replications per location. Forage treatments include three alfalfa varieties (KF4020, KF401B, KF425HD) that vary in morphological and biochemical traits and three grass species ('Echelon' orchardgrass, 'Liherold' meadow fescue, and 'Kora' tall fescue) planted alone and in all possible binary combinations of grass and alfalfa for a total of 15 treatments per replication. Binary mixtures will be planted at a seeding ratio of 75% alfalfa and 25% grass based on the seeding rates for each monoculture.Task 2.2. Collecting spectra and supporting data of alfalfa mixtures: Spectra, forage nutritive composition, and supporting data will be collected from the alfalfa-grass mixtures at the first and second cutting in the establishment (2019) and first production year (2020), using the same methodologies described in Objective 1. In addition, we will manually determine botanical composition of each species in the mixtures. Task 2.3. Developing/validating hyperspectral unmixing algorithms in order to determine yield, nutritive constituents, and the abundance of forage species in the mixture: We will follow the same procedure as Effort 1.2 to develop equations to predict pre-harvest forage nutritive value and yield. Due to possible challenges imposed by alfalfa mixtures, various linear and nonlinear unmixing algorithms will be tested. Because we will also have continuous variables (fractional cover of each species predicted) we will calculate correlations between observed and predicted percent cover for species, functional group, and community.Objective 3. Develop extension materials to assist in adoption of precision agriculture technologies for alfalfa. We will create extension tools to assist growers and producers with assessment of alfalfa composition using existing on-farm technologies and in understanding how to use farm and landscape scale remote sensing technologies. Task 3.1. Use NIR optical sensing technologies to increase knowledge and skills for current producers and future stakeholders (undergraduate ag students) in forage crop quality testing. Mobile optical sensor instruments based on NIRS will be integrated into hay production workshops and field days in Wisconsin and Michigan. Use of devices in workshops will support knowledge transfer by demonstrating how to predicting forage quality constituents (CP, fiber, and other quality parameters) in real time for a range of forages differing in variety, preservation type, maturity, moisture, and weathering. This will help producers better understand the key to alfalfa crop optimization and forage profitability: that their profit depends on energy and protein content as much as it does on dry matter yield. Task 3.2. Development of a global NIRS calibration for "as-fed" whole moist alfalfa hay, haylage, and green chop. Calibration equations for as-fed (undried) forage samples are not readily available for NIRS measurements. Undergraduate students will collect data and develop these equations for alfalfa and alfalfa/grass mixed hay, haylage and greenchop as part of their training. The alfalfa varieties used will include conventional types as well as genetically modified and conventionally-bred reduced lignin alfalfa. As-fed samples will be scanned for NIR spectra using the Unity 2600XT NIR SpectraStar unit operating in the wavelength range 680-2600 nm/1nm increment. Sample constituents will be CP, in vitro true digestibility at 48 h, neutral detergent fiber, and acid detergent fiber. Reference values will be generated by drying and grinding fresh samples after scanning and determining nutritive value using appropriate laboratory chemical methods. Calibration accuracy will be evaluated using standard error of prediction (SEP) and coefficient of determination (??2). These equations will be loaded into the portable equipment and made available to producers.Task 3.3. Conduct a regional workshop on the use of remote sensing in alfalfa and forage production. In the final year of the project, we will organize and conduct a regional workshop on the use of on-farm optical devices and regional remote sensing in alfalfa and forage production.Efforts employed to transfer knowledge gained to stakeholders will include scientific papers and presentation, field days, conferences, producer workshops, classroom and laboratory instruction, and extension publications. Efforts will be evaluated using science citations, extension program evaluation metrics, and class evaluations and testing scores.