## 4 Results and Discussion
### Detection of Crops Fertilization Treatment at Landscape Level  
The differences between the treatments in NDVI responses are significant when comparing plot-pairs of a given field and also when averaging over all fields. However, there is a large variation in response to fertilization treatment between individual fields. The hierarchical linear model described in Section 3.4 explains 75% to 95% of the total observed variance (Figure 11) when introducing the stratum and field effects. A large part of the explained variation in NDVI can be attributed to the differences between strata and between fields within a stratum due to interactions of soil type and field management. The fraction of variance explained by stratum and field factors was largest in the period between sowing (occurring within the period highlighted by a green color in Figure 11) and peak greenness (late August). NDVI differences due to different sowing dates were strongest at early growth stages but partly disappear during the growing season as the canopy closes. Figure 8: Proportion of the fields with a significant difference in mean NDVI between the fertility treatment pairs at the time of the largest observed fertilization responses: 26 August 2014 (peanut, millet and sorghum) and 4 October 2014 (maize and cotton). Figure 10: Relationships between plot mean NDVI extracted from the DG image of 26 August 2014 and mean plant height within plots as measured in the field around this date (\(\pm\)3 days) for the five crop types. Figure 9: Relationships between plot mean NDVI extracted from the DG image acquired on 26 August 2014 and the green ground coverage (GC) as measured in the field around this date (\(\pm\)3 days) for the five crop types. The differences between fields within strata largely dominate the variance explanation for most of the crops. Between-field variability is well known in these heterogeneous environments and, the so-called outfields further away from the homestead usually have much lower yields and lower soil fertility than fields near the homestead [28] due to more organic nutrient inputs. A low fertility not only affects macronutrients such as N, P or K, but also micronutrients. Therefore, fertilizer response will be better on soils with high organic matter content as that is an indication of crop growth in recent years, reflecting larger micro-nutrient availability and absence of soil constraints including low pH or a large drought sensitivity [27]. Another significant factor of inter-field variability is labor constraints, relatively more acute during the peak demand at sowing and stemming from variable farm endowment levels, often correlated with farm sizes. Within a stratum, relatively similar plant growth patterns were observed. Moreover, strata differ in their sensitivity to climate extremes, notably meteorological drought, due to distinct slope, potential rooting depth, texture, water holding capacity, runoff and drainage characteristics and, as a result differences in the amount of soil water that is available to plants. In case of early season dry spells or \"false starts\", less favorable strata will observe more plant establishment failures leading to replanting and a delayed, more heterogeneous greening up of patches in this part of the landscape. This tends to maximize differences between strata early in the season (July), before all canopies are more fully developed including the re-planted patches [29]. The fraction of variance explained by fertilization treatment was generally small when compared to the variation due to field and stratum, ranging from 1% to 35% of the total variance observed. Its contribution to the explanatory power of the model was largest in early October for maize and sorghum fields and around the end of August for cotton and peanut fields (the high values observed for peanut in October were misleading as they correspond to fields already harvested). Sorghum responded strongly to fertilizer in its vegetative growth.