Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity

Round 1

Reviewer 1 Report

I have reviewed and the manuscript does read better with the edits. Please proceed with accepting the manuscript.

Reviewer 2 Report

The authors provide only minor update of documents. After explanation in answer of authors to my review, I understand, that focus of paper is more on statistical and analytical methods (not all methods can be understand as statistical methods – for example back propagation) then on real needs of agriculture. From point of view of comparison of statistical methods results could be consider as relevant.

However, from point of view of precision farming my previous concerns are till now valid.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Am not an expert on many of the statistical methods used in this article and on GIS methods but the article was well articulated and it contributes to current discussions on precision agriculture and big data 

The beginning of the articles is a bit vague and too narrative which can easily distract a reader from the main problem statement. The introduction needs to be concise to improve coherence.

Abstract needs to include a concluding statement that shows either how this research can be up-scaled or applied.  

Comments for author File: Comments.pdf

Reviewer 2 Report

The paper Integration of precision farming data and spatial statistical modelling to interpret field-scale maize grain yield variability in New Zealand is focusing on very important subject how to forecast yield on the base of available data. Paper is describing usage of statistical methods and methods of artificial intelligence (back propagation algorithm).

Authors selected number measurement, which was analyzed on three fields in three seasons, when maize were produced. Interesting is usage of SAVI index. What is not well explain, how and why will be selected other parameters. There is also necessary to consider, that SAVI index is giving information about biomass, but most of others parameters, which were measured directly influenced yield. There is also not deeply analyzed link between biomass and yield, it is known that in certain climatic conditions is not good correlation. Also is not clear, why for example was not measured content of nutarian in soil.

My concern is in conclusion, where influence of single parameters were analyzed, but yield is result of interaction of many parameter’s.

My biggest concern is about meteorological data. From paper is not clear, how data with completely different spatial resolution was compared. In most of situation the meteorological parameters are measured for all field and then is not possible to compare this data with detail data from yield monitor. And on other side the , if we will take average yield for all filed, the number of samples is too small to be possible give so strong conclusion as are in paper.