A gis-based approach for manure spreading monitoring within 2 the digital agriculture framework

Agronomy


Introduction
The agricultural sector has a strong impact on emissions of pollutants, including 35 ammonia from livestock farming and fertilisation of agricultural land.
At the European level farmers will be permitted to spread manure in specific 37 temporal windows [1] although monitoring every plot of land is a critical point [2].38 The approach of integrating Earth Observation acquisitions, ground data and 39 available datasets bring benefits to deal with issue [2].Within this perspective, the 40 objective of the activity is to develop a tool to support monitoring of manure spreading.
Biol.Life Sci.Forum 2022, 2, x. https://doi.org/10.3390/xxxxxwww.mdpi.com/journal/blsf The study area identified to develop and demonstrate tool capacity is the Po Plain (Fig. 1), one of the regions most involved in agricultural activity in Italy.The tool was developed using the open source software QGIS.
A number of surveys were conducted in the north-east and in the east of the study area (province of Modena and Bologna) (Fig. 1) to collect Ground Truth Points (GTPs).The approach is based on a combination of the following set of relevant spatially explicit variables, combined using a weighted formula: • Variable 1: Manure spreading frequency; • Variable 2: Manure spreading areas manually detected; • Variable 3: Distance from farms and/or bio-digesters.
The data processing approach is shown in Figure 2.

Field campaigns
Field campaigns were carried out to collect a dataset of GTPs, used to validate the spreading and no-spreading areas (Fig. 3).Measurements of relative humidity, electrical conductivity and temperature were taken in the fields surveyed.They were carried out both where spillage had occurred and where it had not.

Satellite data processing
First, the satellite acquisitions were pre-processed by applying raster masks that isolate arable fields by integrating the following ancillary datasets: Soil Map [5] to circumscribe the lowlands; Land Use [5] and Land Consumption [6] to exclude urban and non-agricultural; Crop Map [7] to exclude grassland and alfalfa.
Then, spectral analysis was carried out in order to investigate manure spreading response.Average reflectance values for each spectral band acquired by the satellite sensor were extracted and analysed, in particular for spread areas.Variations in the ShortWaveInfra-Red (SWIR) region of the electromagnetic spectrum (corresponding to bands B11 and B12 of S2 MSI data) have been considered to be more suited in manure spreading detection, according to scientific literature [2,8].
Finally, a spectral index calculated from the combination of SWIR, near-infrared (NIR) and red spectral bands of S2 MSI was employed to map manure spread regions in each satellite acquisition: The separation between manure and other land cover has been reached with a threshold of 3, which represents the optimal performance according to the overlapping rate test for the pure pixel detection.Furthermore, the pixel aggregation procedure has removed the noise.These steps constitute a semi-automatic processing that provides the raw dataset utilised for the development of two different independent products:

Variable 1: Manure spreading frequency
The analysis of the preliminary output of the satellite data processing has shown that some areas were more often involved by manure spreading events; thus, the spreading frequencies have been investigated.Firstly, the data redundancy has been mitigated, realising a 350 m buffer for each candidate "manure spread" area.Later, each buffer was assigned the value 1, while the background was set to value 0.Then, manure spread rates have been obtained by summing up the manure spread maps generated from each satellite acquisition.Ultimately, the obtained values were converted into 5 classes, each of which was assigned a susceptibility value (as reported in Table 2).
Manure spreading frequencies lower than 5 have been excluded, since they could represent a low impact fertilisation practice.
Table 2. Susceptibility values depending on the automatic frequency computation.

Variable 2: Manure spreading manual identification areas
Using photo-interpretation, an expert operator confirmed manure spreading areas identified by semi-automatic processing, in order to refine the product by removing false positives.The resulting product consists in a spatially explicit variable in which manure spreads regions are identified by a 350 m radius buffer.Buffer operation has enabled the aggregation of contiguous manure spreads areas.Buffer areas and background pixels have been assigned the values 1 and 0, respectively.

Variable 3: Distance from farms and/or bio-digesters
Spray fields are typically located in close proximity to animal houses and manure lagoons due to the high cost of hauling the liquid [9][10][11][12][13].Positions of livestock farms and biodigester (main manure storage points in Po Plain) were identified using information on the characteristics of farms and biodigesters in the Emilia-Romagna region found in regional datasets [14].Only cattle farms with more than 80 animals/farm and pig farms with more than 600 animals/farm were selected.
A spatial analysis was then performed to assign each pixel in the study area a distance value from farms and biodigesters.
The last step was to reclassify the distance values into discrete classes by assigning each ring a susceptibility value between 0.2 and 1, as reported in Table 3.The assigned values were agreed upon with the stakeholders.

Integration of variables and tool calibration
In order to prioritise and integrate the three variables, a set of weights, agreed upon with the primary users, were assigned to combine variables according to formula: Susceptibility = (0.4 * Variable 1) + (0.2 * Variable 2) + (0.4 * Variable 3). ( Within the variables integration process, statistical indicators have been evaluated.
Among the coefficient combinations following a normal distribution, the one that better shows the balance between the three variables has been selected.Thus, very high susceptibility rates are detected uniquely in the areas where the three variables reach a maximum value.

Results
The product of the formula ( 2) is the susceptibility tool (Fig. 4), which ranges 0 to 1.

Discussion and Conclusion
Regarding the parameters measured in field campaigns, no evidence was found between the spreading and the non-spreading areas.However surveys have been essential in evaluating the correspondence between the satellite and the ground data.
New field campaigns could be carried out together with soil chemical analyses to better investigate any differences and to enhance the accuracy of the monitoring of the area.In this study the accuracy of the manure spectral index is calculated on test areas and will be implemented simultaneously with the aforementioned field campaigns.
The tool could be tailor-made to users' needs for different geographic areas modifying the weights used for each variable in the formula (2).In addition, the manure spreading frequency could be provided annually, estimated from satellite time series analysis updated.

Figure 1 .
Figure 1.Study area in Po Plain (in red).Surveys carried out in the green-marked areas.

Figure 2 .
Figure 2. Workflow of the methodology for the GIS-based tool development (Susceptibility map).

Figure 3 .
Figure 3. Field campaign measures and S2 MSI true colour acquisition over the surveyed site.Field campaigns were performed in the temporal window from October to March: October manure spreading for winter crops, November -February fertilisation ban, March soil/land prepared for new crops.

Figure 4 .
Figure 4. Susceptibility tool, zoom on the north-east portion of the Modena province.The values were converted into 5 classes (from very low to very high) to enhance the readability (Tab.4).

Table 1 .
. The processing operations, for both S2 and PRISMA acquisitions, have been executed using the software ENVI version 5.6.Technical specification of satellite data[3][4].

Table 3 .
Spreading susceptibility values assigned for distance from livestock farms and biodigesters.

Table 4 .
Classes of susceptibility according to values obtained.