## 5 Conclusions  
Monitoring crop growth in spatially heterogeneous landscapes with trees in every field is very well possible with a time series of very high-resolution imagery. Large differences in temporal signatures between fields of a given crop type were observed. On average, crops clearly responded to fertilization, in terms of both crop height and ground coverage impacting the NDVI that is measured by a satellite sensor. NDVI differences between fertilizer treatments within individual fields were not consistent due to large within-field and within-plot variation, especially for treatments with a low fertilizer application (B and C).  
The influence of fertilizer applications on crop NDVI response was proportionally small (lower than 35% of total variation) when compared to the influence of field management and catena position. Large differences in soil fertility and soil related constraints, interacting with plant density and field management, such as replanting at sub-field scale, mean that many factors influence crop growth and thus the NDVI response within a field. As a consequence, a measured NDVI response can only partially be benchmarked against a fertilization reference within the field. Indeed, the variance decomposition model applied at field level showed that 50% of the NDVI variance within a field is due to the fertilizer application for only half of the fields at times when the observed NDVI response was strongest. Moreover, the period with largest contrasts between treatments was crop specific: at the end of August for peanut and cotton and at early October for sorghum and maize. Standard precision agriculture methods based on comparisons of the response of a vegetation index within one field [30] are therefore not very useful. Accounting for other sources of variability in these heterogeneous landscapes is needed before such an approach can be applied.  
However, at plot scale, strong relationships between NDVI and plant growth parameters including plant height and ground coverage were observed. This implies that NDVI imagery directly reflects differences in plant growth due to the inherent patterns of soil fertility and management. In the landscapes of southern Mali, differences in soils within the typical catena are large [12] and were strongly reflected in the responses of the five studied crops. We conclude that comparisons of spatial and temporal responses of vegetation indices for a particular crop type can only be done when plant growth conditions are similar, _i.e._, in within strata related to soil catena position in the landscape.  
This publication was made possible (in part) by the STARS project, an integrated effort to improve our understanding of the use of remote sensing technology in monitoring smallholder farming [18]. Additional support was provided by the CCAFS Flagship 2 project on \"Capacitating African Smallholders with Climate Advisories and Insurance Development\". The authors would like to acknowledge the field team members: [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), [PERSON] (AMEDD), and [PERSON] (ICRISAT).  
[PERSON], [PERSON] and [PERSON] designed and carried out the study under the supervision of [PERSON]. [PERSON] was in charge of the satellite image preprocessing, [PERSON] perfomed the statistical variance decomposition and [PERSON] coordinated the field data campaign, UAV images processing and the interpretation of the results. [PERSON] and [PERSON] directly contributed to the data interpretation. All the authors participated in the writing.  
The authors declare no conflict of interest.