*3.2. Ground-Based Crop Evaluation and Harvested Crop Yield*

Ground-based measurements of crop traits are shown in Figure 10. Estimates of the general crop growth stages taken on 28 May 2019, scaled according to the BBCH, reported a more advanced crop stage for the century-old biochar plots (median BBCH = 43) compared to the reference plots (median BBCH of 40.5) as shown in Figure 10a. The observed difference in crop growth stages (BBCH) was statistically significant (*p*-value = 0.01265) between the reference and century-old biochar plots. This could be explained by the dark color of the century-old biochar plots, resulting in higher soil and plant temperatures stimulating plant development.

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**Figure 10.** Impact of century-old biochar on ground-based crop traits including crop growth stages, expressed as the biologische bundesanstalt, bundessortenamt and chemical industry (BBCH) scale, on 28 May 2019 (**a**), and yield on 20 July 2019 (**b**). The horizontal black line displays the median value, surrounded by box edges representing the 25th and 75th percentiles. The black circles show the experimental plots of each pair and the dark green lines relate the corresponding reference and century-old biochar plots of each pair. **Figure 10.** Impact of century-old biochar on ground-based crop traits including crop growth stages, expressed as the biologische bundesanstalt, bundessortenamt and chemical industry (BBCH) scale, on 28 May 2019 (**a**), and yield on 20 July 2019 (**b**). The horizontal black line displays the median value, surrounded by box edges representing the 25th and 75th percentiles. The black circles show the experimental plots of each pair and the dark green lines relate the corresponding reference and century-old biochar plots of each pair.

The mean harvested crop yield (taken on 20 July 2019) value was 8.69 ± 0.69 t.ha−1 for the centuryold biochar plots compared to 8.84 ± 0.66 t.ha−1 in the reference plots; the observed contrast was not significant with a *p*-value of 0.42489 (Figure 10b). This finding contrasts with the positive promotion of the aboveground productivity for long-term biochar enriched soils [6,23]. This can be explained by the fact that our study area is situated in a quite rich soil type and hence the influence of century-old biochar on the harvested crop yield is limited. However, other studies considering poorer soil types have shown that the difference in yield due to the long-term biochar enrichment is more substantial [6]. The mean harvested crop yield (taken on 20 July 2019) value was 8.69 <sup>±</sup> 0.69 t.ha−<sup>1</sup> for the century-old biochar plots compared to 8.84 <sup>±</sup> 0.66 t.ha−<sup>1</sup> in the reference plots; the observed contrast was not significant with a *p*-value of 0.42489 (Figure 10b). This finding contrasts with the positive promotion of the aboveground productivity for long-term biochar enriched soils [6,23]. This can be explained by the fact that our study area is situated in a quite rich soil type and hence the influence of century-old biochar on the harvested crop yield is limited. However, other studies considering poorer soil types have shown that the difference in yield due to the long-term biochar enrichment is more substantial [6].

#### *3.3. UAV-Based Yield Map Generation 3.3. UAV-Based Yield Map Generation*

Relationships between harvested crop yields on 20 July (Section 3.2) and the UAV-based multispectral vegetation indices on the last acquisition date (i.e., 24 June which was the closest to the harvest date) are presented in Table 6. The s-CCCI, NDRE, NDVI, and CI-red exhibited a good relationship with the harvested crop yield as shown in Table 6. The obtained relationships were statistically significant with *p*-value(F-test) of 0.002, 0.002, 0.008, and 0.008 for s-CCCI, NDRE, NDVI, and CI-red, respectively. The aforementioned indices showed a strong correlation with the harvested crop yield with Kendall's τ coefficient of 0.60, 0.61, 0.54, and 0.54 for the s-CCCI, NDRE, NDVI, and CI-red, respectively (Table 6). The obtained correlations were statistically significant with *p*value(Kendall) of 0.001, 0.0008, 0.004, and 0.004 for the s-CCCI, NDRE, NDVI, and CI-red, respectively (Table 6) which is in line with the previous finding of Das and Singh (2012). Relationships between harvested crop yields on 20 July (Section 3.2) and the UAV-based multispectral vegetation indices on the last acquisition date (i.e., 24 June which was the closest to the harvest date) are presented in Table 6. The s-CCCI, NDRE, NDVI, and CI-red exhibited a good relationship with the harvested crop yield as shown in Table 6. The obtained relationships were statistically significant with *p*-value(F-test) of 0.002, 0.002, 0.008, and 0.008 for s-CCCI, NDRE, NDVI, and CI-red, respectively. The aforementioned indices showed a strong correlation with the harvested crop yield with Kendall's τ coefficient of 0.60, 0.61, 0.54, and 0.54 for the s-CCCI, NDRE, NDVI, and CI-red, respectively (Table 6). The obtained correlations were statistically significant with *p*-value(Kendall) of 0.001, 0.0008, 0.004, and 0.004 for the s-CCCI, NDRE, NDVI, and CI-red, respectively (Table 6) which is in line with the previous finding of Das and Singh (2012).


**Table 6.** Results of correlation analysis of the multispectral vegetation indices, contained in Table 2, on 24 June and their relationships to the harvested crop yield on 20 July. The coefficient of determination, *p*-value of the statistical F-test, non-parametric Kendall's rank correlation tau, and its significance are expressed as R<sup>2</sup> , *p*-value(F-test), τ, *p*-value(Kendall), respectively.

The predicted UAV-based multispectral crop yield map based on the identified linear regression fit between the s-CCCI and crop harvested yield, which reported the strongest relationship (Table 6), is shown in Figure 11a. The result of the predicted UAV-based MSP crop yield is shown in Figure 11b. The western part of the experimental field clearly shows lower yield values. However, the eastern part of the field exhibited rather high yield values (Figure 11b). The impact of century-old biochar on the predicted UAV-based MSP crop yield is shown in Figure 11c. The mean predicted MSP crop yield (mean value of the 11 plots) was not statistically different (*p*-value of 0.437) between the century-old biochar plots (8.82 <sup>±</sup> 0.39 t.ha−<sup>1</sup> ) and the reference plots (8.74 <sup>±</sup> 0.42 t.ha−<sup>1</sup> ) as shown in Figure 11c. This finding balances the previous works reporting a higher aboveground productivity as a consequence of biochar presence [6,23]. However, our results showed a highly significant difference (*p* < 0.0001) of the predicted MSP crop yield between the good and moderate classes derived from the MSP sensor (Figure 11d). Similarly, the observed difference of the predicted MSP crop yield was highly significant (*p* < 0.0001) between the moderate and poor classes based on the MSP sensor (Figure 9d). The mean predicted MSP crop yields were 8.96 <sup>±</sup> 0.85, 8.50 <sup>±</sup> 0.79, and 7.84 <sup>±</sup> 0.83 t.ha−<sup>1</sup> for the good, moderate, and poor classes derived from the MSP sensor (Figure 11d). In addition, the observed difference of the predicted MSP crop yield was highly significant (*p* < 0.0001) between the good and moderate classes, and between the moderate and poor classes, derived from the RGB sensor (Figure 11e). The mean predicted MSP crop yields were 8.76 <sup>±</sup> 0.91, 8.40 <sup>±</sup> 0.86, and 7.54 <sup>±</sup> 0.89 t.ha−<sup>1</sup> for the good, moderate, and poor classes based on the RGB sensor (Figure 11e). This result indicates that other factors (such as fertilization inputs, agricultural practices, and inherent possible pedological variations) are of higher importance than the presence of century-old biochar in determining the crop health and yield variability at the within-field scale.

This paper provides a proof of concept of using high-resolution UAV images in assessing the impacts of century-old biochar on crop performance at the canopy scale. As such, the results of this study reveal important findings regarding the impact of century-old biochar on crop health and yield (in particular over the senescence period). However, since the approach described in this paper is only suitable for small-sized fields of a few hectares, further research should consider extending this analysis across a wider area, including a range of crops under contrasting weather conditions, using available fine resolution optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1 Synthetic-aperture radar (SAR)) satellite images.

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**Figure 11.** (**a**) Relationship between the multispectral (MSP) simplified canopy chlorophyll content index (s-CCCI) on 24 June and the harvested crop yield on 20 July. The black circles represent the harvested samples and the brown line indicates the linear regression fit. (**b**) Map of the predicted MSP crop yield computed from the relationship between the MSP s-CCCI and the harvested crop yield. (**c**) A comparison of the predicted MSP crop yield of all of the 11 century-old biochar plots (red) versus the 11 reference plots. The black circle and cross represent the mean and median MSP crop yield, respectively. The error-bar represents the corresponding standard deviation. The bottom and top black pluses (+) indicate the minimum and maximum MSP crop yield, respectively. A comparison of the predicted MSP crop yield of the good (green), moderate (yellow), and poor (red) crop health **Figure 11.** (**a**) Relationship between the multispectral (MSP) simplified canopy chlorophyll content index (s-CCCI) on 24 June and the harvested crop yield on 20 July. The black circles represent the harvested samples and the brown line indicates the linear regression fit. (**b**) Map of the predicted MSP crop yield computed from the relationship between the MSP s-CCCI and the harvested crop yield. (**c**) A comparison of the predicted MSP crop yield of all of the 11 century-old biochar plots (red) versus the 11 reference plots. The black circle and cross represent the mean and median MSP crop yield, respectively. The error-bar represents the corresponding standard deviation. The bottom and top black pluses (+) indicate the minimum and maximum MSP crop yield, respectively. A comparison of the predicted MSP

crop yield of the good (green), moderate (yellow), and poor (red) crop health classes derived from the MSP (**d**) and RGB (**e**) sensors. The black circle and cross represent the mean and median MSP crop yield, respectively. The error-bar represents the corresponding standard deviation. The bottom and top black pluses (+) indicate the minimum and maximum MSP crop yield, respectively. Outside of the plot, asterisks \*, \*\*, \*\*\*, and \*\*\*\* reveal the statistical levels of significance and the acronym NS stands for non-significant.

### **4. Conclusions**

This paper explored the potential of using high-resolution UAV-based imagery to assess the impacts of century-old biochar on winter wheat crop performance. The combination of crop growth and health monitoring, through RGB and multispectral images, provided new insights into the alteration in crop dynamics at the canopy level associated with century-old biochar presence. Based on the RGB data, a significant positive impact (*p*-value = 0.00007) of century-old biochar on the evolution of winter wheat canopy cover was highlighted. Moreover, century-old biochar was found to slightly increase wheat height throughout the growing season. Multispectral OSAVI imagery underlined a significant positive effect on crop performance as a consequence of century-old biochar only at the beginning of the season (*p*-values < 0.01), although the impact was not remarkable at the end of the monitoring period.

Century-old biochar showed a significant positive impact on winter wheat crop growth stages (*p*-value of 0.01265). However, the effect of century-old biochar on harvested crop yield was not significant. Harvested crop yield exhibited a significant correlation with the multispectral vegetation indices, particularly s-CCCI and NDRE (*p*-value(Kendall) of 0.001 and 0.0008, respectively). The predicted UAV-based multispectral crop yield was not significantly different between the century-old biochar plots compared to the reference plots. Hence, other factors, such as inherent pedological variations and agricultural practices, appear to be of higher importance than the presence of century-old biochar in determining the crop health and crop yield variability at the within-field scale.

**Author Contributions:** Conceptualization, R.H.D., A.D., J.M.; Investigation, R.H.D.; data curation, R.H.D., V.B., J.F. and E.P.G.; resources, R.H.D.; methodology, R.H.D. and J.M.; software, R.H.D.; formal analysis, R.H.D.; validation, R.H.D.; writing—original draft preparation, R.H.D.; writing—review and editing, R.H.D. and J.M.; supervision, J.M.; project administration, J.-T.C., J.M., F.N. and A.D.; funding acquisition, J.-T.C. and J.M.; conceptualization, R.H.D., J.M. and A.D. All authors have read and approved the published version of the manuscript.

**Funding:** This research was funded through the ARC grant 17/21-03 for Concerted Research Actions by the French Community of Belgium within the framework of the CHAR project at the University of Liège.

**Acknowledgments:** The authors thank the landowner for providing access to the CHAR project experimental farm. Thanks to Aurore Houtart within the framework of the CHAR project. Thanks also to Lammert Kooistra and Gilles Colinet for the helpful suggestions and supporting our research. The authors also acknowledge the editor and the anonymous reviewers for their insightful comments.

**Conflicts of Interest:** The authors declare no conflict of interest.

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