## 4 Results and Discussion
### Detection of Fertilizer Applications at the Field Scale  
Farmer fields in Sukumba are very heterogeneous and display a large NDVI variability, both between and within fields regardless of the typically small field sizes (1.4 ha on average). The effect of fertilizer can be observed visually in the field, and on both UAV and satellite images from about the end of August (Figure 4) until the beginning of October. _In-situ_ observations highlighted important height and plant color differences between the most fertilized plots and the farmer's practices (plots A). Figure 5 shows significant differences in the evolution of plant height and GC fraction between different plots of a millet and sorghum field (both crop types usually not fertilized by farmers). Both satellite and UAV detect gradual increase in NIR reflectance with fertilizer rates (Figure 4). However, the very large variability within and between the fields clearly shows that other environmental factors, such as residual soil fertility, presence of trees, decomposing tree root systems, anthills, termite mounds, _etc._ affecting plant emergence and subsequent plant growth, are important as well. The regression model used to quantify the fertilization effect on the NDVI provides three major learnings (Figure 6). Firstly, the best period to observe a NDVI response to fertilization is crop specific: end of August for peanut, millet and sorghum fields and beginning of October for cotton. Maize also appears to respond more strongly in October, but that may be due to differential patterns of senescence. Most likely, this pattern may reflect variations in soil water holding capacity rather than a fertilizer response. Secondly, the 9-10 fields of each crop have very different responses to fertilization leading to a wide range of R\({}^{2}\) values. This suggests that the feasibility to detect differences in soil fertility at the landscape scale will be impeded by a very large between-field variance. This is a function of different management practices (such as souring dates, occurrence of pests and diseases) and biophysical conditions. Among these, crop type differences in the evolution of the sensitivity of NDVI to fertilization are also important. The last learning is that the response to fertilizer application on maize fields was weaker than the other crops, and is most likely the consequence of the fertilization under farmer's practices, which was not well controlled at the time of trial establishment, reducing crop response to additional fertilizer inputs. Some cotton and maize fields had already been fertilized by farmers at the time of plot establishment. The fertilization quantities prescribed by the protocol  
Figure 3: Mean and standard deviation (colored shading) of NDVI recorded in plots B–F, reflecting different fertilizer application treatments, for different overpass dates. have thus been adapted but some over fertilization should have occurred in plot B for these two crops. This could also contribute to the high value of R\({}^{2}\) prior the fertilization treatment, in mid-July. When NDVI response to fertilization is strongest, the regression model explained at least 50% of the variance between plots within a given field for about half of the fields, except for maize. Many factors can explain the remaining part of the within-field variation. For example, African smallholders actively maintain economically and culturally valuable tree species inside their field crops (especially rain-fed). Treatment plots were not very close to trees, however they remain exposed to competition for water and nutrients. They also largely benefit from tree and leaf litter nutrient deposit, although this varies with catena position [12]. Tree cutting leads to multi-annual, residual fertility hotspots as tree root systems decompose resulting in spectacular islands of dense crop canopy.