Feasibility of a low-cost weather sensor network for agricultural purposes : a preliminary assessment

英语. This study has focused on challenges encountered when setting up a weather-network for agricultural purposes (e.g. linking temperature to the suitability of crops and pest incidence) with only low-cost sensors and materials. The study included a set of experiments in a meteorological station and a one-month period of observations in a large coffee plantation with a complex terrain in Costa Rica. The created network is intended to be linked to agronomic trials, and are part of a package that will make farmers the scientists in a range of extension projects. Ongoing projects provide farmers with a range of seeds that are tested, while feedback is provided by a crowd-sourcing approach. Farmers send text messages back to the project managers. The coordinates of the farmers' field can be extracted from a stack of rasters with climate information, resulting in a clear understanding of the conditions during the agronomic trials. Experiments with different sensor (iButton DS1923) resolution showed that losses in precision when using a low temperature resolution are small compared to other losses (e.g. interpolation in time and space). Using a high-resolution for humidity observations provides very small improvements over low-resolution, as it provides data with the same accuracy. Experiments also focused on adjustment to PVC tubes, which functioned as sensor-shielding. All adjustments provided large differences with a certified shielding for the maximum temperature on sunny days. The best coating to limit the impact of radiation was insulating foil, and it is recommended that future experiments focus more on aeration, as this has not yet provided the expected benefits. As no (combination of) adjustments provided data in line with the reference station, different types of data calibration were tested. While direct correction of data by a polynomial regression model provided reasonable results, the main difference between PVC shields and the certified sensor was caused by the faster heating/cooling over time (thermal-inertia properties). By creating a linear model between change in time in the PVC and certified shield, a calibration model was developed that has been used to correct data. This has been done by setting an anchor point on each day, to which the corrected change was added/subtracted. With some minor additional calibration, this model provided data that was very similar to data in the certified shield. After the initial experiments were analysed, one hundred sensors were placed in a large coffee plantation with a 500 meter elevation gradient; fourteen sensors were lost and six provided incorrect data. The correlation between temperature and a range of variables was assessed. This included static (elevation, slope, aspect, canopy height, leaf-area-index and daily radiation) and dynamic (hourly radiation) covariates. On average, 52% of variance in temperature could be explained by static covariates. Including hourly radiation as covariate instead of daily radiation improved this model by 1%. Elevation is by far the most important independent variable (± 67%), although this influence is lower during periods with high temperature. A higher daily maximum temperature reduces the strength of elevation/temperature correlation. These are periods during which temperature is harder to predict based on interpolation in the complex terrain. A lower correlation between elevation and temperature can partly be compensated by a stronger correlation with other covariates; hourly radiation contributes on average 20% to the temperature-predicting models during hours with sun (although the models can only predict 54% of variance during these hours). Geostatistical interpolation has been tested for 80, 40, 20 and 8 sensors, with different kriging approaches and sets of covariates. Cross-validation provided the best results for universal kriging with elevation. Dynamic kriging provided smaller errors only with the full 80-sensor network. Co- and Spatio-Temporal kriging provided larger errors in predicting a left-out sensor, while data of the sensors included in the kriging showed least modification. The preferred approach depends on the network objective and reliability of data. While the network in this study cost ±US$ 8,700, a sufficiently accurate network of 25 sensors, can be created with a smaller budget: 20 low-res (temperature) iButton sensors (DS1922L-F5), 5 high-res (temperature and humidity) iButton sensors (DS1923-F5), 50m thin white PVC, 50 PVC elbows, 1m2 insulating foil, a small amount of fibre-glass mesh, and labor for construction (drilling holes and assembling). The cost for this weather network - which can store 341 days of 1-hour resolution data - will be approximately US$ 1,450. A 50-sensor network would still cost <US$ 3,000.

英语. Climate change is creating the need for farmers to adapt to changing conditions. Changing the crops and varieties that they grow is one of the easier approaches that can provide large benefits. Different crops and their varieties can cope differently with conditions of prolonged drought, submergence, salinity, and other expected conditions. As regions around the world have already faced conditions that other regions are still expected to face, a wide range of traditional and modern varieties are readily available in seedbanks. In ongoing projects, farmers are provided with a small set of varieties that are expected to be suitable to their regions. Farmers test these varieties, and provide feedback through a crowd-sourcing approach. This means farmers also receive a mobile phone and charging station, and provide feedback by text messages. In order to evaluate the results of farmer, local climate information is required, which often lacks in these areas. This study has shown that this information can be made available at low-costs. The sensors that have been used to measure temperature and humidity in this study were iButtons, which are small sensors with a long battery life. The shield in which these sensors have been placed were created from material that was purchased at a local market in Costa Rica, where this study was conducted. The main shielding consisted of PVC tubes and mosquito mesh (on which the sensor was placed). During a first set of experiments, additional adjustments have been tested, to reduce the required corrections of the data. Based on recommendations by the World Meteorological Organization (WMO), adjustments were made to improve the air flow around the sensor, and reduce the impact of radiation. With only low cost material, however, none of the adjustments could match the official data to a level that no correction was required. For this reason, experiments on correction were also done. This resulted in an approach that worked well in different areas. Once the shields were sufficiently tested, one hundred sensors were placed in a coffee farm during one month. The observations were used to find possible linkages to the different conditions in the farm. This included factors such as altitude, height of trees, and daily sunshine. Data from 80 sensors could be used for analysis. The other 20 sensors were either lost or damaged. The link with temperature and elevation in the studied area was strong. This was expected based on similar studies and the large differences (>500m) in the area. The other factors that were included showed smaller links to temperature, although at certain moments this could become more important. This link of temperature with different factors that can be created from existing maps and algorithms can be used to reduce the number of sensors that would be required in a certain network. However, hours during which it was warm and sunny showed very little linkages to the commonly available factors (variables). Including hourly sunshine could partly compensate this, by having a relatively strong link to temperature, but this is still smaller than during nighttime/cloudy periods. The percentage of variation in temperature that can be explained at different hours (dotted line) and the relative contribution of the studied factors is shown in the figure below. Based on this knowledge, different approaches of data interpolation (creating a network from point-based observations) have been tested, while reducing the number of sensors to 40, 20 and 8. The most useful results were found for an approach that only included elevation, as this is the most stable variable. More advanced (geo-statistical) approached require a larger number of sensors (> 50), which places them outside the objective of these networks: to be available at low costs. The first set of experiments, aiming to create a shield that would perform similar to a WMO certified shield, did not provide very good results. The influence of radiation could not be avoided by improving air flow (drilling holes) and adding different types of foil and tape. While correction would thus still be required, the selected correction approach did perform very well at different locations. It would still be important to better study the actual shield, by experimenting by even more combinations of materials. The data that was derived from the network in a large coffee plantation in Costa Rica, showed that variation in temperature is especially linked to differences in elevation. Factors such as density and height of the canopy played a smaller role. During hours with sun, elevation could explain less of the variation, but the hourly differences in sun/shadow could partly compensate this. The different factors can be used to create a relatively accurate networks with around 25 sensors, which would cost less than US$ 1,500 and can measure every hour for a period of around one year. This would be ideal for most annual crops.