FARMVIEW VIDEO TUTORIALS
Farmview: The Premium service in Fieldclimate
Since 2005, FieldClimate platform is an indispensable partner for the agriculture decision-making process and has been improving over the years.
Identical to the registration at Fieldclimate: FieldClimate registration.
Identical to the login at Fieldclimate: FieldClimate login
Identical to the dashboard at Fieldclimate: FieldClimate dashboard.
Additional widgets will be available for Farmview in March 2020. These will substantially ease your work with Cropzones by presenting an overview of the selected parameters per cropzone. Stay tuned.
Farmview works with so called Cropzones. Cropzone is a basic unit and represents your field or a portion of your field that is managed identically within given borders during given time . The common situation would be that an user collects multiple Cropzones per each Field and multiple Fields per Farm.
The Farmview main screen consists of three menus (Figure 1). The Navigation Bar on the left allows you to move between the dashboard, the settings and the Farmview services (so far only the Irrimet service is available, the other services will be launched during 2020). Irrimet has this icon:
The Submenu of the Navigation Bar shows the submenu for each selected icon in the main menu. For instance, it shows Daily water balance and Irrigation calendar for Irrimet. The Cropzone Menu offers an option to move between different Cropzones, adding and removing them by the ‘+’ and ‘-’ buttons at the top of the menu. It also allows you to sort Cropzones based on the Farm name, the Field name and year. Finally, User Menu allows you to access the user settings such as the language, the units and the passwords.
To start using the Farmview services, you need to define your fields. This is easily done by clicking on the Cropzone list > Add cropzone and filling in the Farm name, the Field name and the Cropzone name (Figure 2).
Farm name refers to your entire farm, while Field name describes a single field with boundaries. While the Farm and Field names stay often identical during multiple seasons or years, the Cropzone name changes as you cultivate new crops.
For a Cropzone, use a name that you can easily identify later. You can even include the year and/or the crop name directly in the Cropzone name, i.e. Nexttoroad2019 or P1_Sorghum_18. This may ease your orientation between different Cropzones in the Cropzone list.
Once you added the Cropzone, you need to define its cultivation period, the grown crop and the boundaries (Figure 3).
We provide you model outputs and maps exactly for this Cultivation period. Therefore, define the period so that it includes all your operations of interest. For annual and seasonal crops, define your season or a year. For perennial crops, make sure you define each single season in separate Cropzone as modifications from year to year are expected in the crop development. We are already now working on an option ‘Copy from’ to ease copying an existing Cropzone to a new one (available in March 2020).
Define your Crop (i.e. Apple). The crop name is used for user’s overview and has no automatic implications in the service.
Identify your Borders. Please include only single polygons. Use a polygon drawing option or import a geojson in a standard EPSG 4326 format. Do not include any holes in the polygon. If multipolygon is imported, only the first shape will be imported.
After adding and defining multiple Cropzones you can list them in the overview in the Cropzone Menu (Figure 4). Here, you are also offered to remove an existing Cropzones and filter and sort them by the Farm and Field name as well as by the year.
You can now start using your Farmview services. The first service is Irrimet.
Irrimet is the first service within Farmview. It monitors water stress within your field and supports you by scheduling irrigation events. Also, it helps you to monitor your irrigation events with the help of an interactive calendar.
Irrimet module requires sensors for:
- Air temperature
- Air humidity
- Solar radiation
- Wind speed
After you have successfully defined your cropzones, you are redirected to the irrimet setting page (Figure 5). First, you are asked to define your crop from the FAO crop type table. The default FAO crop (evapotranspiration) coefficients and the default root zone depth will be provided in the lower interactive graphic. Adjust them if needed by moving the green/red points up and down.
Next, you are asked to select the ID Number of stations that should provide data for rain and evapotranspiration for this Cropzone. Stations within a 10 km radius from the field are displayed in the selection.
Note: Make sure you select stations that contain data for the season that you are interested in.
Next, indicate rain efficacy: which portion of rain infiltrates the soil. Use default numbers if you don’t have any better information.
Last, please define the phenological stages for your crop. This is required to correctly model evapotranspiration during the season. The dates can be fastly inputted with the help of an interactive graphic by dragging the orange lines (Figure 6).
Optionally, you can also define your soil profile information such as the initial water status or the soil type (Root zone settings are located under the interactive graphic, Figure 6 and 7).
Note: Rootzone settings should be provided especially in areas with water abundance. In such areas, water that can be held by the soil should be limited in the model.
Finally, you are ready to study water stress and water surplus in your field (Figure 8). For the daily water balance computation, we used the daily rain and evapotranspiration data from your station, the crop coefficients and the management dates. The evapotranspiration was computed according to the FAO-56 Penman-Monteith equation. If any irrigation events were reported (see the next chapter), these will be also considered.
The lower graphic shows all the input data. The upper graphic displays the Daily water balance, where green represents water surplus and red water deficit. The maximum water balance is limited by the Field capacity and Refill points that you provided in the Rootzone settings (optional, Figure 6 and Figure 7). If a rain event occurs after any water stress period (red color), the daily water balance is automatically computed from 0.
Our interactive calendar (Figure 9) enables you to input your single or regular irrigation events. These are then immediately considered in the daily water balance computation (previous chapter).
You can input your events by clicking on the date in the calendar and filling in the Event name, the Irrigation type, the Irrigation Start and the Duration (Figure 10).
For recurring events, you should (in addition to previous) activate the Recurring event button, input the Frequency and the End of schedule prior to saving events.
The Yield Prediction tool provides a water-limited yield estimate based on rainfall data from METOS station measurements, seasonal weather forecast, and user settings for soil texture and moisture conditions. It shows a comparison of the current season’s Yield Prediction vs. Yield Prediction based on the 35-year historic averages of rainfall for your specific location. Yield Prediction helps you make an informed decision when it is time to fertilize or irrigate in a water-limited environment.
Yield Prediction includes the important crops wheat, barley, and canola. The tool was specifically parameterized for conditions in northern areas of North America but can generally be applied worldwide. Stay tuned, as we plan an update for Spring 2023 with additional crops and improve Yield Prediction in any location.
Yield Prediction requires a weather station with:
- Precipitation sensor
- Air temperature sensor
- Active Weather Forecast license
You can use your already existing physical stations with the required sensors or also a virtual weather station.
After you have defined a cropzone, navigate to the Yield Prediction setting page (Figure 12 and 13). There are only three simple inputs required: crop, sowing date, and expected harvest date (rough estimate is sufficient). This is already enough to get the first results, as the more advanced settings are pre-filled with defaults. You can directly go to the Yield Prediction results page by clicking on the Yield Prediction icon on the left (see Figure 14, jump to 5.3 below), or have a look at the advanced settings (continue reading here).
When clicking “Show Advanced Settings”, additional setup parameters can be modified. “Best possible average yields” will put an upper limit to the Yield Prediction. The “Initial soil moisture (at sowing)” accounts for the stored soil water at the time of sowing. Percentages indicate the amount of actual available soil water (i.e. between wilting point [0%] and field capacity [100%]). Use your best judgment here: How much water did the previous crop use? How much rainfall occurred prior to sowing?
Soil texture can be selected based on the USDA soil texture classification. This will, in conjunction with initial soil moisture, define the initially “Actual available soil water at sowing” displayed at the lower right end of the section. Remember to click “Save configuration” to update the value.
Soil texture can also be set to “custom”. This allows setting field capacity and wilting point manually. You should only do this if you have a trustworthy source such as a laboratory analysis of your soil.
“Air Temperature Source” and “Rain Source” will list all of your stations within 10 km radius from the cropzone. These can be both physical and virtual weather stations. However, one of the selected stations must have an active Weather Forecast license, since Yield Prediction uses a seasonal weather forecast. It does not matter to which station (air temperature or rain) the Weather Forecast is tied. You can also select the same station for air temperature and rain source (this will be the most common case).
Note: Make sure you select stations that contain gapless data from the sowing date onwards.
Figure 12: Simple Yield Prediction settings
Figure 13: Advanced Yield Prediction settings
Finally, you are ready to study the Yield Prediction result for your field (Figure 14). On the top of the page (below the gray cropzone overview) you will find an overview of the settings for yield prediction, including the date when the Yield Prediction results were calculated (usually every day). On the bottom of the page, a summary of the important prediction results is shown.
The main chart area shows the Yield Prediction from sowing until the estimated date of crop physiological maturity. Note that harvest ripeness occurs several days to weeks after physiological maturity, depending on the crop and weather conditions, but yield does not further increase in this time period.
The gray area in the chart shows the time series of predicted yield based on long-term rainfall average for the field. The orange area shows the Yield Prediction based on actual station measured rainfall up until today, and the pink area complements this – based on a seasonal rainfall forecast – up until crop maturity. At a glance, the final values of the gray and pink areas indicate whether the current season’s yield is likely to be above, below, or similar to the long-term average. In the example (Figure 14), the season was very dry and Yield Prediction far below the long-term average.
Crops are also color-coded for your convenience: Orange for wheat, yellow for canola, and brown for barley.
Figure 14: Yield Prediction result page
Yield Prediction comes with the Previous Yields page which provides a convenient way to keep track of your crop’s yield performance over time (Figure 15). Below the chart area, measured yields for the selected field from previous years can be entered manually. These will show up immediately in the chart area, which also includes the current Yield Prediction. This gives a handy overview of the trend of yield performance, possibly indicating necessary actions or confirming the current strategy.
Figure 15: Previous Yields
Yield Prediction is exclusively based on water availability and the duration of the growing period: Each unit of water (mm, inch) available to the crop during the growth period translates into a certain amount of yield (tons, bushels per area), depending on the crop/cultivar. A certain minimum amount of water (ca. 100 mm or 4 inch) is necessary for crop development before any yield is produced. The Yield Prediction tool factors all this in and is, therefore, most suitable to be applied in regions where rainfall is the decisive factor for yield buildup such as semi-arid climate zones. Other potentially yield-affecting factors (e.g. weeds, pests, diseases, nutrients) need to be managed well, as these are not considered in the Yield Prediction tool.
Yield Prediction is shown as ranges to indicate that there is a considerable degree of uncertainty in the estimate, e.g. from the seasonal weather forecast. However, even if the weather forecast turns out to be fairly correct and everything is managed well on the field: While Yield Prediction is likely to be precise, there is still no guarantee that actual yield will be within the predicted ranges. The reason for this is that yield is the result of a multitude of factors, and no crop model can cover all of them. Still, when applied in a suitable manner, Yield Prediction can provide valuable information for decision making, for instance when nitrogen fertilization amounts need to be decided.
Yield Prediction is currently (Dec. 2022) parameterized for conditions in northern areas of North America and corresponding crops (i.e. spring crops). While Yield Prediction is likely to work well in similar conditions at other locations, winter crops may be predicted poorly. However, an update is scheduled for early spring 2023 which will enable fine-tuning of the crop cultivar’s yield potential (high, low) and growth period (spring, winter; early, late). Also, new crops will be added: soybean, maize, durum wheat.
Take a look at the video for Satellite Module release. Check all the possibilities to work with satellite data, moreover, to combine it with FarmView and FieldClimate features for a full data integration.
Among the several benefits, LAI-Dynamics Module offers:
- Biomass Viewer graph: based on Leaf Area Index (LAI) quantification, at any given time, space (cropzone) and crop type.
- Identify regional differences of growth development in the same CropZone: field variabilities and heterogeneity can be easily identified with satellite images together with biomass data.
- Closely monitor growth stages and crop development
- Correlate growth stages to weather conditions such as GDD graphs from FieldClimate, disease models, weather forecast, Irrimet data.
- High quality satellite imagery from Sentinel-2 satellite, with 10 m resolution combined with LAI scale of biomass development, updated every 5 days.
LAI-Dynamics Based on Satellite Data is a new Module under FarmView software. FarmView is a Premium Service inside FieldClimate, offering you more details and data zonation, to better support your crop management decisions.
Since 2005, FieldClimate platform is an indispensable partner for the agriculture decision-making process and has been improving over the years.
To obtain access to the Satellite Module, customers will need an extra license inside FarmView.
For more information, please contact Caroline.firstname.lastname@example.org or email@example.com.
To access Satellite features, you will need to login to Farmview first. Under your Satellite credentials, the user can find the satellite icon on the left side of the page. Satellite has this icon:
CropZones will automatically be displayed inside the Satellite Module, so there is no need to add them or change any settings. The main Satellite dashboard will show Leaf Area Index tab, with the Biomass Viewer graph, followed by satellite images. Page will look like this:
Figure 17: Leaf Area Index tab
LAI (Leaf Area Index) is the canopy area per unit of horizontal ground area. The biomass LAI can vary on a scale from 0 (bare soil) to 3-4 (highest LAI for tomatoes or wheat), to 5-6 (highest LAI for maize, soybeans). Expected LAI will vary between crops, likewise, be influenced by environmental conditions such as sunlight, climate, water availability, nutrients, etc.
The Biomass Viewer graph will present biomass development over time. Each Satellite page refers to a specific CropZone and will show all the data out of it. To understand daily biomass accumulation over time is crucial to track growth stages and, consequently, yield potential.
To monitor growth stages allows growers to plan ahead crop management from planting day to flowering, and determining the best days for harvesting. Moreover, it can be combined to temperature data via the GDD Accumulator tool, in FieldClimate. Once users have all the data for crop-temperature threshold, GDD, and biomass development, field decisions become more complete.
• Monitor growth stages of multiple cropzones:
biomass accumulation over time allows users to identify crop-growth stages such as Vegetative phases (planting to flowering), maturity, and harvesting timing. Get the best out of your crop at the right time.
Figure 18: biomass accumulation over time
Crop of soybeans monitored with Satellite Module, throughout the cultivation period. Possibilities to control growing degree days (GDD) via Biomass Viewer graph allow growers to strategically prepare crop management from planting to harvesting. Satellite images – from right to left – show crop development from: planting (right-pink images with low LAI), to maturity with highest LAI accumulation (middle-dark-green images), and finally harvesting on the left side.
• Identify regional variances in growth status: within one cropzone, growers can identify areas below or above average of biomass development via data zonation; resulting in instant actions and local management.
- Optimize biomass development based on field variabilities to uniformly reach maximum LAI and, consequently, enhance yield potential.
- Action on problematic areas first.
- Allocate more devices to tackle weakest biomass spots, such as weather stations, iSCOUT sensors, Disease model, soil improvement with Mobilab, and many other options.
• Association with weather-related conditions: satellite imagery also shows land topography patterns such as slopes and terraced fields. Topography patterns will influence weather patterns inside the CropZone, affecting soil moisture and land heterogeneity on its turn.
- Install weather stations on relevant spots.
- Correlate satellite data with weather forecast and Disease model.
- Group historical satellite and weather data to project yield potential and optimize field management.
- Understand eroded areas and water runoff effects on yield by observing crop characteristics.
Satellite images captured from a maize crop, with terraced fields. Possibility to identify crop characteristics:
Figure 19: Satellite images captured from a maize crop, with terraced fields
(1) Terraced structure can be seen on the line division among the crop.
(2) Spots with biomass development below growth average. For example, the Southeast border of the CropZone in a light-pink color indicates lower biomass accumulation, when compared to the rest of the crop (dark-green areas). Identify field heterogeneity, and act regionally to increase and uniform yield potential all over the CropZone, such as: soil nutrition improvement, prevent from disease infections with iScout sensors on the right spots, install weather stations with Soil Moisture module, water balance and irrigation control with irrimet.
• Data correlation with Growing Degree Days (GDD): Growing degree days (GDD) is a weather-based indicator for assessing crop growth and development. In addition, it helps to calculate pest risks during the growing season. FieldClimate software provides GDD graphs, based on lower and upper air-temperature thresholds (under Accumulator Tool section).
- Utilize crop-coefficient-temperature thresholds to calculate adequate GDD per crop type.
- Overlap GDD data with Satellite Biomass Viewer for an overview of crop development, which directly reflects on yield potential.
- Unify vegetative phases with temperature conditions to determine the ideal dates for planting, while predicting dates of flowering and maturity.
Figure 20: GDD calculation on FieldClimate under common temperature thresholds for soybeans (10.6 – 36.7 °C for lower and upper set points respectively).
Also available in FarmView, the NDVI (Normalized Difference Vegetation Index) is a remote sensing index retrieved from satellite imagery. It stands for a qualitative indicator of vegetation health based on how the plant reflects light at certain frequencies (some waves are absorbed and others are reflected).
The page structure will remain the same as LAI: a graph shows NDVI development over time, mean curve, fitted curve, max and min values, in addition to cloud-status conditions.
NDVI values range from -1 to 1. Zero and below correspond to non-plant surfaces, thus focusing purely at values 0-1, which correspond to vegetated areas. The scale can be read as:
- Red tones = least vigorous crop canopy, low NDVI.
- Yellow tones = medium vigorous crop canopy, medium NDVI.
- Green tones = most vigorous crop canopy. The higher the NDVI value, the greater plant density and health.
Figure 21: The NDVI (Normalized Difference Vegetation Index) remote sensing index retrieved from satellite imagery
Spatial imagery is retrieved from Sentinel-2 Satellite, updated every 4-5 days. Some regions may suffer from cloud coverage which can interfere in the number of captured images thus, consequently, reducing the amount of available data. Please have a look at the ‘cloud-status tool’ to track cloudy days over your cropzone.
The Satellite page contains a background map, with a satellite layer to indicate the exact location of your cropzone boundaries, filled out with the provided satellite indexes (LAI and NDVI). The map can be found when clicking on the individual imagery data.
This feature allows users to visualize, at a glance, the location of the cropzone polygon together with the stage of crop development given by the remote sensing spatial imagery. When clicking on the scale (LAI or NDVI), the pixels will be automatically populated with its respective percentage.
Figure 22: Background Map in Satellite page
After uploading the SoilGuard data on your PC, via User menu -> data upload -> CSV file upload, you will be able to visualize your SoilGuard sampling data. Files can only be imported in CSV format.
Figure 23: SoilGuard sampling data
The data consists of the geolocations of all samples on the map, with a raw-data table down the page. In order to see the accurate location, don’t forget to switch on your GPS location on the SoilGuard device before starting with sampling collection, in the field.
The selected sample (violet) can be chosen at any time. The respective measurements will be shown on the raw-data table, highlighted in blue. The cropzone boundaries will be shown in red.
Figure 24: The cropzone boundaries are shown in red
Below the map, there is a raw-data table with all measurements collected within this cropzone showing: date, VWC% (Volumetric Water Content), EC, Soil Temperature, coordinates.
Figure 25: All measurements collected within this cropzone
Within the Nutrition Cropzone page you can visualize data for DualEx and Mobilab devices. For DualEx, data uploading can be done through User menu -> data upload -> CSV file upload.
Figure 26: Visualized data for DualEx and Mobilab devices
In the same nutrition page, users can also see Mobilab data.
- 1 – Download the Mobilab app for iOS or Android.
- 2 – Register your sample locations on the app, respectively creating individual IDs for each one of them.
- 3 – Download the
- on your computer.
- 4 – Within the Desktop Software, execute the measurements for soil or Plant SAP.
- 5 – Results will appear on the Desktop Software. For more information, visit our
- 6 – To visualize data in the FieldClimate platform, users need to sync data from Desktop Software to the FieldClimate server.
7.2.2 How to visualize Mobilab data in FieldClimate?
After the data sync is done, Mobilab data can be accessed via station list or via cropzone list in case of FarmView users. For the second option, data display will look this:
Figure 27: Mobilab data visulaised in FieldClimate