Utilisation of Intrinsic and Extrinsic Soil Information to Derive Soil Nutrient Management Zones for Banana Production in a Smallholder Farm

Round 1

Reviewer 1 Report

L16 : "The roots of a banana plant can reach a depth of 5.5 meters, why do you limit yourself to 30cm ?"

L23-28 : how to explain that P, Mg and Zn contents were high in the northeast part and low in the northwest part of the banana plantation and Soil TN was high in the west part and low in the east-northeast part across the plantation?

Author Response

Point 1: L16 : "The roots of a banana plant can reach a depth of 5.5 meters, why do you limit yourself to 30cm ?"

Response 1: Thanks for the comment. To answer the question, we are not really limiting ourselves to 30 cm. This work is a continuation of our study entitled “Unlocking the Land Capability and Soil Suitability of Makuleke Farm for Sustainable Banana Production”. In the study, we were checking if the soil properties are capable for agricultural production and specifically suitable for Banana production. The key properties we used to match with the Banana requirements included depth. We dug pits up to the limiting layer to check if the depth do meet the one required by Banana. In this study, we are focusing on how variable are the essential nutrients for bananas across the plantation. The reason behind that was to help us derive management zones so that the farmer can be able to manage the nutrients better, by not under-applying and over-applying the fertilisers to supplement the Bananas.

Point 2: L23-28 : how to explain that P, Mg and Zn contents were high in the northeast part and low in the northwest part of the banana plantation and Soil TN was high in the west part and low in the east-northeast part across the plantation?

Response 2: Thanks for the comment. What we have noticed in this work is that even though the nutrients may highly vary across the plantation, there are areas that are high compared to others. Mostly such areas were underlain by specific soils. And the nutrients behaved differently on particular soils. What we mean is that soil P might be high in Valsrivier and low in Westleigh, and when you look at soil TN is high in Westleigh and low in Valsrivier to give an example. The reasons behind such cases are given in the discussion section.

Author Response File: Author Response.docx

Reviewer 2 Report

please find the attached comments

Comments for author File: Comments.pdf

Author Response

Point 1: Figure 1: Clearly indicate the image source of the study area, specifying whether it is directly from Google Earth or any other specific satellite image or UAV?

Response 1: Thanks for the comment. We have now indicated the source of the aerial photograph of the study which is from Google Earth Pro. We have included the text on the Figure 1 caption (lines 145 and 146).

Point 2: Figure 7: Enhance the description of the figure to ensure clarity. Utilize a legend to illustrate the varying shades of green and red, representing the corresponding degrees of excess or deficit.

Response 2: Thanks for the comment. The description of the figure was enhanced to ensure clarity (lines 396, 397 and 398).

Point 3: The findings reveal pronounced spatial variability in numerous nutrients, notably K and Ca. Nevertheless, a singular management zone encompasses the entirety of the study area for all nutrients, with the exception of K, which the authors have divided into two distinct management zones. Kindly justify the reason for not suggesting distinct management zones within the four identified uniform soil units for each nutrient?

Response 3: Thanks for the comment. We have delineated the management zones based on the spatial prediction maps and the optimal requirements for bananas. To take a few steps back, we first used regression kriging (RK) to make the spatial prediction maps of the essential nutrients irrespective of the underlying soils. Once we have done that and noticed the variability across the farm, we then derived a raster with the constant values for the optimal nutrient’s requirement for Bananas. We used the raster and subtracted the optimal content from the prediction maps, and we delineated management zones based on that. From the management zones maps no nutrient has one management zone. We could not suggest the Management zones based on the soil units and the reason behind that is that some soils did not have much of a difference for the nutrient in particular. Also, the classes from the management zones maps showed which ones are visible on the maps. 

Point 4: Line 194 and 195 : Correct the extension abbreviation : KMl to KML.

Response 4: Thanks for the instruction. We have now corrected the abbreviation to “KML” (lines 194 and 195).

Point 5: In Line 85: Remove the bracket.

Response 5: Thanks for the instruction. We have now removed the bracket (line 85).

Point 6: In Line 118: change the first letter to uppercase as "Fig 2a."

Response 6: Thanks for the instruction. We have now changed the first letter to uppercase (line 118).

Point 7: In Line 512: Change the first letter to lowercase as "mulch."

Response 7: Thanks for the instruction. We have now changed mulch to lowercase (line 512).

Point 8: In Line 517: Insert a comma to read as "nutrients, identify."

Response 8: Thanks for the instruction. We have now added a comma to read as “nutrients, identify” (line 517).

Author Response File: Author Response.docx