Abstract

Abstract. The Department of Agriculture – Region VII reports that many mango orchards in Cebu province are dying because of the absence of required post-harvest attention. Lacklustre yields and erratic pest infestations have driven some farmers and growers to abandon mango orchards. To help revive low-yielding mango orchards, there is a need to distinguish actively bearing mango trees from those that remain dormant throughout the year. Using remote sensing techniques, mango trees from separate orchards in Brgy. Cantipay, Carmen, Cebu were mapped and studied using multi-temporal Sentinel-2 data (from January 2018 through May 2019). Prior to that, a field visit was conducted to survey the area using UAVs and field observation, and in the process, was able to identify an abandoned mango orchard. Pixel-based Normal Difference Vegetation Index (NDVI) values were extracted from each of the 822 geotagged mango trees with an average of 16 trees among 53 divisions. Time series were derived from the average of the NDVI values from each division and plotted per month of extraction from oldest to latest. Clustering was applied to the time series data using Hierarchical Clustering with Ward’s Minimum Variance as an algorithm to determine the divisions with the closest time series. Using the resulting dendrogram as basis, two major clusters were selected based on the value of their distances with each other: Cluster 1 containing 29 Divisions, and Cluster 2 containing 24 Divisions. Cluster 1 contains most of the Divisions in and around the biggest active mango orchard. In contrast, Cluster 2 contains most of the Divisions that are in and around the previously identified abandoned mango orchard. An alternative dendrogram was also created by using Complete Linkage algorithm in Hierarchical Clustering, after which 3 relevant clusters were selected. The second dendrogram highlights the stark difference between Division 1, contained in Cluster 3, from the rest of the other clustered divisions at 2.17 units from the next closest one. Notably, Division 1 is located smack in the middle of the abandoned orchard The remaining clusters, Cluster 2 with 21 divisions containing most of the divisions in the abandoned orchard, is 2.46 distance units away from Cluster 1, which has 31 and hosting most of the divisions in the active mango orchards. Two major clusters emerged from using the two algorithms. Divisions with higher and more variant NDVI values seemed to come from the mango trees which were more active during the fruiting cycle. Divisions from the abandoned mango orchards were observed to have lower and less varied NDVI values because of minimal activity in the trees. Other Divisions clustered under the abandoned orchard could have been juveniles based on their size.

Highlights

  • Cebu is known as one of the top mango producing provinces in the Philippines

  • Using orthophotos from the Phil-LiDAR Project and as shown in the left image in Figure 5, mango trees were geotagged manually using points so extracted values would be more accurate per tree, and mango trees that are too close to other types of vegetation will be excluded

  • In dividing the mango trees using polygons, it was ensured that the trees were close to each other, belong to the same orchard, and no polygons should overlap

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Summary

Introduction

In 2018, Cebu produced over 43,480.80 metric tons of mangoes or 6.1% of the overall production share in the country. A number of mango orchards have been reported to be dormant or unable to bear fruits because either: they were affected by previous infestation; their health are deteriorating from over inducing and pesticide abuse; or the trees weren’t sprayed with flower inducers at all because of farmers demotivated by small to no profits (Fernandez-Stark et al, 2017). By the end of their contracts, these leaseholders leave the farms in a damaged state This short-sighted, unsustainable farming practice can lead to the deterioration of the trees’ health, which could result to long time dormancy or the imminent death of the trees (Fernandez-Stark et al, 2017)

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