Weather Data: Virtual, In-Field, or Regional Network—Does It Matter?
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Which data source is better? In the world of specialty crops, there is disagreement on how well weather-driven insect, disease, and frost prediction models actually perform. Dr. Dave Brown, former director of Washington State University’s AgWeatherNet spent years comparing different weather data sources and how those data affect the accuracy of common environmental models used by orchard growers. In this 20-minute webinar, he shares the surprising things he learned. Decrease chances of crop damage with one simple practice Find out how you can increase the accuracy of your predictive models and decrease frost, insect, and disease incidents by doing just one thing differently—improving the quality of your weather data. Discover: - Microclimates: what are the conditions like inside a crop canopy versus outside? - Virtual data vs. weather station data: Which is better? - How do site-specific weather data vs. regional network data compare? - How much does a small decrease in data quality affect the accuracy of your models? - What’s the value of in-orchard measurements? - What are some best practices for higher data quality? Presenter For 20 years as a faculty member at Montana State University and Washington State University (WSU) Dr. Dave Brown pursued research on soil sensors, spatial data science and digital agriculture. At both universities, he served in many leadership roles for major research projects, academic programs and most recently as Director of the WSU AgWeatherNet program. In this capacity, Dr. Brown hired and supervised a team of meteorologists who pursued research and extension activities focused on evaluating and improving the quality of weather data used for agricultural decisions.
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In this webinar, world-renowned soil physicist, Dr. Gaylon Campbell shares his newest insights and explores options for water management beyond soil moisture. Learn the why and how of scheduling irrigation using plant or atmospheric measurements. Understand canopy temperature and its role in detecting water stress in crops. Plus, discover when plant water information is necessary and which measurement(s) to use. Find out:
- Why the Penman-Monteith equation, with the FAO 56 procedures, gives a solid, physics-based method for determining potential evapotranspiration of a crop
- How the ATMOS 41 microenvironment monitor combined with the ZL6 logger and ZENTRA Cloud give easy access to crop ET data
- How v can be controlled by manipulating plant water potential using appropriate irrigation strategies
- Why combining monitoring soil water potential with deficit irrigation based on ET estimates provide an efficient and precise method for controlled water stress management
- And more…Forbes co2 and temperature presentation for earth day at cua april 22 2015 ...
Extended Abstract
Introduction
While the vast majority of climate scientists have concluded that the changes in the climate over the past few decades can be attributed to human activity [Doran and Zimmerman, 2009], there has been a degree of reluctance to attribute specific weather events to elevated CO2 concentrations. For example, Coumou and Rahmstorf [2012] have noted that there has been an exceptionally high incidence of extreme weather events over the past decade and that some of the events can be linked to climate change but nevertheless concede that particular events “cannot be directly attributed to global warming.” Moreover, the World Meteorological Organization has noted that the incidence of extreme weather events matches IPCC projections, but qualifies this conclusion by stating that “it is impossible to say that an individual weather or climate event was “caused” by climate change….” [World Meteorological Organization, 2011, p 15]. This claim of “attribution impossibility” is not a minor shortcoming; it leaves the causes of extreme events open to question, allowing climate skeptics to attribute the increased incidence of extreme events to so-called “natural variability.” In the United States, this has undermined the political consensus necessary to adopt robust, cost-effective policies to reduce CO2 emissions.
This paper explores the relationship between CO2 and weather by addressing whether there is a causal relationship between the atmospheric concentration level of carbon dioxide and hourly temperature. The analysis begins by noting that traditional correlation analysis is not capable of addressing whether there is a causal relationship between CO2 and temperature because statistical methods alone cannot render results that establish or reject causality between two variables that are contemporaneously correlated. Nevertheless, it is possible to address the issue of causality by using more advanced statistical techniques.
An Approach to Establishing Causality
This paper addresses the issue of causality between CO2 and temperature by following the research of the Nobel Laureate Clive Granger [1969], who defined causality in terms of whether lagged values of a variable lead to more accurate predictions of some other variable. In his words, “The definition of causality …is based entirely on the predictability of the some series, say Xt. If some other series Yt, contains information in past terms that helps in the prediction of Xt … then Yt is said to cause Xt.” [Granger, 1969, p 430]. This study embraces this view of causality by examining whether lagged values of CO2 lead to more accurate forecasts of temperature. The specific approach adopted here is to exploit the diurnal nature of the variation in the hourly CO2 concentration levels by using the CO2 concentration level in hour t – 24 as an explanatory variable. This variable has a 0.96 correlation with the CO2 level in hour t but i
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We are all searching for a contemporary but on-demand topic of research, such that based on that topic, we can prepare papers that journals can accept, or Ph.D. and Postgraduates can be obtained. Normally, we search the existing journals, books, conference etc. depositories to find a gap in the research but this subjective search yields the better within the literature we had searched, but, when some more manuscripts are included, the gaps may change. If you have the same experience, then you are in the correct place. Here we propose not any topic but five most sought-after questions, whose answers are supposed to change the pace of “Sustainability Research”, and you can easily get your suitable topic of research from these answers.
finalPresentation
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Follow the steps of a meteorologist
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Grow your crop steering expertise
Crop steering can optimize crop production and production costs, but to crop steer successfully, you need to do it right. You have to understand how to obtain the right soil water contents and soil electrical conductivities to either stress the crop or avoid stressing the crop in a controlled way. To do this, you’ll need to perform crop steering calculations.
Steer your way to higher quality, productivity, and profit
In part 3 of our greenhouse webinar series, Dr. Gaylon Campbell, internationally recognized soil physics and environmental measurement expert, teaches how to perform crop steering calculations that give you the information you need to stress or de-stress your crop at the right time and in the right way to achieve your goals. In this 30-minute webinar you’ll learn:
The water balance equation
- How to calculate the irrigation amount
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- Environmental factors that influence the water balance
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Soil is no longer a black box. Advances in sensor technology and software now make it easy to understand what’s happening in your soil, but don’t get stuck thinking only measuring soil water content will tell you what you need to know. Water content is only one side of a critical two-sided coin. To understand when to water, plant-water stress, or how to characterize drought, you also need to measure water potential.
Better data. Better answers.
Soil water potential is a crucial measurement for optimizing yield and stewarding the environment because it’s a direct indicator of availability of water for biological processes. If you’re not measuring it, you’re likely getting the wrong answer to your soil moisture questions. Water potential can also help you predict if soil water will move, and where it’s going to go. Join METER soil physicist, Dr. Doug Cobos, as he teaches the basics of this critical measurement. Learn:
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Irrigation of Controlled Environment Crops - Nutrients & Stress
Welcome to Part 2 of our webinar series: Irrigation of Controlled Environment Crops for Increased Quality and Yield. Today we’ll hear from Dr. Gaylon Campbell, who will discuss how to measure electrical conductivity and osmotic stress to optimize crop steering for maximum yield.
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The first step to understanding if you are collecting quality soil moisture data is to first know where they’re wrong. But what makes good data go bad?
Proactive prevention of data’s main confounders.
Would you recognize bad data if you had it? Knowing what to look for and the key steps to take to prevent later problems is the difference between accuracy and estimation. In this 30-minute webinar, METER’s soil moisture sensor product manager and ecology and plant physiology specialist, Chris Chambers, will break down the largest contributors to degradation in data.
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There are ovens, vacuum ovens, halogen moisture balances, Karl Fischer titrators, NIR devices – and that’s only the beginning. How do you choose the right one?
Once you’ve chosen, how do you ensure consistently precise and reliable results? Measuring moisture content can be a minefield, but getting it right pays dividends.
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Steady-state methods for measuring thermal conductivity in insulation are painstakingly slow. The temperature gradient inherent to the method also induces moisture movement within moist samples, making it unsuitable for such measurements. This seminar describes a new algorithm, used with a line heat source, to measure the thermal conductivity of insulating materials in one minute, even in the presence of moisture.
In this 30-minute webinar, Dr. Gaylon Campbell, world-renowned environmental measurement expert, describes:
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Moisture content and water activity. Each has its assigned roles and responsibilities. They’re often used separately, and for very different purposes. But can they work together?
In this live webinar, our METER experts will break down how and why mapping moisture content and water activity together – and how they change over time – can open a new world of valuable information.
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How do I characterize expansive soils? Will water drain through the soil quickly or be retained? How can I predict deep drainage or runoff?
What if you could get an inside picture of the soil moisture relationships that cause these issues?
A soil-water characteristic curve shows the relationship between water content and soil suction. And it’s one of the most powerful diagnostic and predictive tools.
Learn about what soil-water characteristic curves are and why they’re so powerful.
Irrigation of Controlled Environment Crops for Increased Quality and Yield—Pa...
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Rev up your productivity
If you’re crop steering to optimize quality and productivity, understanding nutrient concentration is critical to stressing your plants correctly. If nutrient concentrations get too low, you won’t get the production you’re paying for with the rest of your infrastructure. If the concentrations are too high—you’ll risk killing your plants.
Measure. Don’t guess.
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Part 1: Substrates and Water
Stop guessing. Start measuring.
When you irrigate in a greenhouse or growth chamber, you need to get the most out of your substrate so you can maximize the yield and quality of your product. But if you’re lifting a pot to gauge how much water is in the substrate, it’s going to be difficult—if not impossible—to achieve your goals. To complicate matters, soil substrates and potting mixes are some of the most challenging media in which to get the water exactly right.
Without accurate measurements or the right measurements, you’ll be blind to what your plants are really experiencing. And that’s a problem, because irrigating incorrectly will reduce yield, derail the quality of your product, deprive the roots of oxygen, and increase risk of disease.
Supercharge yield, quality—and profit
At METER, we know how to irrigate substrates. We’ve been measuring soil moisture for over 40 years. Join Dr. Gaylon Campbell, founder, soil physicist, and one of the world’s foremost authorities on soil, plant, and atmospheric measurements, for a series of irrigation webinars designed to help you correctly control your crop environment to achieve maximum results. In this 30-minute webinar, learn:
Why substrates hold water differently than normal soil
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The fundamentals of measuring soil moisture: specifically water content and electrical conductivity
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学校原件一模一样【微信：741003700 】《(Newcastle毕业证书)纽卡斯尔大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
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Water polluted by dyestuffs compounds is a global threat to health and the environment; accordingly, we prepared a green novel sorbent chemical and Physical system from an algae, chitosan and chitosan nanoparticle and impregnated with algae with chitosan nanocomposite for the sorption of Malachite green dye from water. The algae with chitosan nanocomposite by a simple method and used as a recyclable and effective adsorbent for the removal of malachite green dye from aqueous solutions. Algae, chitosan, chitosan nanoparticle and algae with chitosan nanocomposite were characterized using different physicochemical methods. The functional groups and chemical compounds found in algae, chitosan, chitosan algae, chitosan nanoparticle, and chitosan nanoparticle with algae were identified using FTIR, SEM, and TGADTA/DTG techniques. The optimal adsorption conditions, different dosages, pH and Temperature the amount of algae with chitosan nanocomposite were determined. At optimized conditions and the batch equilibrium studies more than 99% of the dye was removed. The adsorption process data matched well kinetics showed that the reaction order for dye varied with pseudo-first order and pseudo-second order. Furthermore, the maximum adsorption capacity of the algae with chitosan nanocomposite toward malachite green dye reached as high as 15.5mg/g, respectively. Finally, multiple times reusing of algae with chitosan nanocomposite and removing dye from a real wastewater has made it a promising and attractive option for further practical applications.
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Water contamination is one of the major causes of water borne diseases worldwide. In Kenya, approximately 43% of people lack access to potable water due to human contamination. River Kuywa water is currently experiencing contamination due to human activities. Its water is widely used for domestic, agricultural, industrial and recreational purposes. This study aimed at characterizing bacteria and fungi in river Kuywa water. Water samples were randomly collected from four sites of the river: site A (Matisi), site B (Ngwelo), site C (Nzoia water pump) and site D (Chalicha), during the dry season (January-March 2018) and wet season (April-July 2018) and were transported to Maseno University Microbiology and plant pathology laboratory for analysis. The characterization and identification of bacteria and fungi were carried out using standard microbiological techniques. Nine bacterial genera and three fungi were identified from Kuywa river water. Clostridium spp., Staphylococcus spp., Enterobacter spp., Streptococcus spp., E. coli, Klebsiella spp., Shigella spp., Proteus spp. and Salmonella spp. Fungi were Fusarium oxysporum, Aspergillus flavus complex and Penicillium species. Wet season recorded highest bacterial and fungal counts (6.61-7.66 and 3.83-6.75cfu/ml) respectively. The results indicated that the river Kuywa water is polluted and therefore unsafe for human consumption before treatment. It is therefore recommended that the communities to ensure that they boil water especially for drinking.
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The popularity of functional foods among scientists and common people has been increasing day by day. Awareness and modernization make the consumer think better regarding food and nutrition. Now a day’s individual knows very well about the relation between food consumption and disease prevalence. Humans have a diversity of microbes in the gut that together form the gut microflora. Probiotics are the health-promoting live microbial cells improve host health through gut and brain connection and fighting against harmful bacteria. Bifidobacterium and Lactobacillus are the two bacterial genera which are considered to be probiotic. These good bacteria are facing challenges of viability. There are so many factors such as sensitivity to heat, pH, acidity, osmotic effect, mechanical shear, chemical components, freezing and storage time as well which affects the viability of probiotics in the dairy food matrix as well as in the gut. Multiple efforts have been done in the past and ongoing in present for these beneficial microbial population stability until their destination in the gut. One of a useful technique known as microencapsulation makes the probiotic effective in the diversified conditions and maintain these microbe’s community to the optimum level for achieving targeted benefits. Dairy products are found to be an ideal vehicle for probiotic incorporation. It has been seen that the encapsulated microbial cells show higher viability than the free cells in different processing and storage conditions as well as against bile salts in the gut. They make the food functional when incorporated, without affecting the product sensory characteristics.
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Results of geophysics and pneumatic injection pilot tests during 2003 – 2007 yielded significant positive results for injection delivery design and contaminant mass treatment, resulting in permanent shut-down of an existing groundwater Pump & Treat system.
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The results of the MetaArray™ analysis’ support vector machine (SVM) identified groundwater monitoring wells with a 80% confidence that were characterized as either Limited for Reductive Decholorination or had a High Reductive Reduction Dechlorination potential. The results of MetaArray™ will be used to further optimize the site’s post remediation monitoring program for monitored natural attenuation.
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Weather Data: Virtual, In-Field, or Regional Network—Does It Matter?
- 2. WEATHER DATA VIRTUAL, IN-FIELD, OR REGIONAL NETWORK—DOES IT MATTER? Dave Brown, PhD METER Group, Inc. USA
- 3. DOES IT MATTER? WHAT USE? WHAT CROP? 3 Integrated Pest Management Crop Models Irrigation Frost Pesticide Drift Labor Management
- 5. GLOBAL MODELS NUMERICAL WEATHER PREDICTION 5 Mathematical modeling of atmospheric physics • Forecast models @ time zero Multiple governmental models • Fundamentally NOT site-specific Derivative commercial grid products • Ensemble methods (combining models) • Statistical/Interpolation methods “Virtual” Weather Stations
- 6. WEATHER STATION NETWORKS MESONETS+ 6 Monitor regional weather • Open, level, unobstructed area with grass cover Long-term record • Standard configuration, high-quality measurements Not intended to monitor microclimate!
- 7. SITE-SPECIFIC STATION MICROCLIMATE + MANAGEMENT 7
- 8. ORCHARD EFFECTS Zagrodnik, Brown, Kalcsits, & Khot (2020-2023) 8 4 high-density apple orchards Average paired in-out difference
- 9. SITE-SPECIFIC STATION OUTSIDE CANOPY? 9 Key outside measurements • Wind – drift regulations • Evapotranspiration Wear and tear Reference for management effects Train site-specific forecast!
- 10. GDD COMPARISON 10 0 days 5 days 8 days 15 days
- 12. VIRTUAL VS. REAL STATION AgWeatherNet COMPARISON 12 156 Weather Stations, 2012-2020 • Duplicate air temperature sensors Daily high and low air temperature comparison • N = 445,102 daily comparisons Zagrodnik and Brown, 2020 https://extension.wsu.edu/wam/can-virtual-weather-stations-replace-real-weather-stations
- 13. VIRTUAL VS. REAL STATION DAILY HIGH/LOW COMPARISON 13 Virtual station not good if accurate real-time air temperature is required • e.g., frost, pollen tube, etc.
- 14. VIRTUAL VS. REAL STATION SYSTEMATIC SITE BIAS 14 Individual sites can be systematically off by a substantial amount
- 15. VIRTUAL VS. REAL STATION SITE BIAS STABILITY 15 Virtual biases can change from year to year
- 16. VIRTUAL VS. REAL STATION GDD BASE 50°F 16 1248 Station-Years 74% of sites had a 5+ day miss at least once in 8 years
- 17. VIRTUAL WEATHER DATA 17 How do growers “correct” weather data?
- 18. VIRTUAL + WEATHER STATIONS 18
- 19. QUESTIONS? Dave Brown Director of Pomology METER Group, Inc. 2365 NE Hopkins Ct, Pullman, WA 99163 T 509.332.5984 E sales.environment@metergroup.com W www.metergroup.com