The topic of this dissertation is the quality of spatial data related to arable farming work executions. There is a lot of uncertainty related to farming operations. Whether the field was treated evenly or with variable rate application, the performed field work is often inaccurate and vague. This is mainly because driving patterns in the field are not accurate and the optimal machinery input amount needs are uncertain. It is important to study this uncertainty in order to understand its impact on precision farming. This dissertation approaches the problem of uncertainty by applying geographical information quality evaluation and measurement methods to the evaluation of farm work execution. This makes it possible to exploit existing evaluation principles for spatial data in farm work execution cases. ISO 19157:2013 standard defines the key elements in spatial data quality. By studying agricultural technology research case studies, the effect of the quality elements and the estimated overall accuracy level of the farming work execution in grain production were constructed. The different case studies integrated remote sensing technologies such as satellite images and drones with cameras and hyperspectral technologies together with different spatial farm data and farmer's tacit knowledge. The case studies included also positioning errors based on dynamic GNSS positioning accuracy measurements and simulations and measured field work driving accuracies of farmers. The temporal quality was studied by developing methods how to apply real time data from external sources in ISOBUS environment. The machinery driving lines were overlapping by 10 % on average. Accurate steering assistance can cut that in half but there are still remarkable overlaps especially in headland areas. The biggest difficulty is the optimization of the variable rate application levels meaning the thematic accuracy. The thematic accuracy was determined as the variation of different tasks conducted for the same purpose being 22 % on average. The temporal accuracy was completely a case dependent containing a response to the immediate rain forecasts or applicability of one month old satellite image. A single precision farming operation was estimated to benefit about 31 €/ha which was estimated to be only 23 % of the total precision farming benefit potential according to the variables in this work. The overall accuracy of spatial data inputs was estimated to be 61 % in relation to optimal treatment in the studied cases. This number indicates the quality of spatial data inputs to farm machinery. This uncertainty is large in contrast to typical attempted precision farming adjustments and defined machinery performance requirements. These results suggest that there is a need for better uncertainty management, before different precision farming applications can truly be developed and evaluated.
|Translated title of the contribution||Paikkatiedon epävarmuuden vaikutus täsmäviljelytyötehtävien onnistumiseen|
|Publication status||Published - 2019|
|MoE publication type||G5 Doctoral dissertation (article)|
- spatial data quality
- precision farming