Abstract

The human population is expected to reach ∼9 billion by 2050. The ensuing demands for water, food and energy would intensify land-use conflicts and exacerbate environmental impacts. Therefore we urgently need to reconcile our growing consumptive needs with environmental protection. Here, we explore the potential of a land-use optimisation strategy to increase global agricultural production on two major groups of crops: cereals and oilseeds. We implemented a spatially-explicit computer simulation model across 173 countries based on the following algorithm: on any cropland, always produce the most productive crop given all other crops currently being produced locally and the site-specific biophysical, economic and technological constraints to production. Globally, this strategy resulted in net increases in annual production of cereal and oilseed crops from 1.9 billion to 2.9 billion tons (46%), and from 427 million to 481 million tons (13%), respectively, without any change in total land area harvested for cereals or oilseeds. This thought experiment demonstrates that, in theory, more optimal use of existing farmlands could help meet future crop demands. In practice there might be cultural, social and institutional barriers that limit the full realisation of this theoretical potential. Nevertheless, these constraints have to be weighed against the consequences of not producing enough food, particularly in regions already facing food shortages.

Authors - I am an author
Contributors on Publons
  • 3 authors
  • 4 reviewers
Followers on Publons
Metrics
Publons score (from 1 score)
8.6
Altmetric
Web of Science Core Collection Citations
  • Significance Comment

    There are no issues with the significance of the work, in my opinion. Clearly, the human population will increase, they will need to be fed, and a smarter allocation of crops to farmlands that suit those crops is, in my mind, not simply a supplement to other solutions but an absolute necessity. By providing a simple study of how such allocation could benefit food productions, the authors have taken a major first step in an important direction. I sincerely hope that there will be more.

    Quality Comment

    The paper is well written and the material is presented in a clear manner. The arguments for smarter land-use strategies may not be very rigorous, but in my opinion they do not really need to be, and I think that most readers would agree with the authors’ stated motivation.

    The study carried out is somewhat brute in its simplicity, but the authors themselves are aware of this and state, almost explicitly, that the main contribution of the paper is that of a thought-provoking experiment aimed at answering the basic question of “what are the potential gains of intelligently allocating which crops are grown where so as to maximize productivity?” To this end, I believe that the authors have a high-quality paper.

    I have, however, two major and one minor qualms with the work, some of which will undoubtedly be biased by myself being an optimization-phile, but which I think the authors – and others working on similar problems – would be wise to consider.

    First, the general problem of intelligently allocating resources is one that must have been researched to death in many different mathematical, scientific, and engineering communities, as it is a very classic problem (unfortunately, I speak mostly on gut instinct and not on concrete experience, and so cannot give pointers to any good literature). As such, the one question that kept bugging me as I read this manuscript was: “shouldn’t there already be a whole slew of theory and work on a general allocation problem that the authors’ problem is just a special case of?” If this is so, which I believe it is, then the paper could benefit significantly from referencing such work and showing how it could be applied here, thereby justifying the authors’ proposed strategy of simply filling each farmland only with the crop that grows best on that farmland, or refuting it as being inferior to other strategies that have already been proposed for similar or equivalent problems.

    My other major qualm is with the authors’ chosen optimization criterion and the complete neglecting of constraints. Here, all of the crops are counted equally in the sense that a set of farmlands producing 12 tons instead of 8 is considered more optimal regardless of the diversity of the crops produced in those 12. This is, from the optimization perspective, equivalent to a multi-criterion optimization problem where the weights on the different components are all set to unity. Such a choice of weights must be justified somehow, however – for example, might it not make more sense to judge different crops by vitamin content or some cost-to-nutrition ratio? Additionally, in any real-life situation there will almost certainly be constraints that demand crop diversity and minimal amounts of each crop (e.g., would we really switch to growing rice and only rice if it were found that rice grew the best everywhere?) Any standard optimization strategy must account for such constraints in its formulation, and it is not clear how the strategy proposed here would, and whether or not the presence of such constraints would completely invalidate the strategy in the case that such constraints are too stringent.

    Lastly, I think it would be nice to see the simulation files and models used to run the experiments, as they appear to be quite interesting and most likely too complex for a typical reader like me to code (but not to use). This would allow people from optimization fields to try their hand at solving this problem via alternate methods. This is a minor complaint, however.

    Summary

    I will leave here additional remarks that came to mind while reading the paper, as well as any points that I wasn’t able to include above:

    • The country-dependent preferences (such as those discussed for Asia and Africa) are again something that could be properly handled by formally defining an optimization criterion and weighting production differently based on the kinds of crops produced.

    • I don’t believe that it is correct to use the term “optimal land use” since the land-use strategy proposed is heuristic in nature and not necessarily optimal. To say that a strategy is optimal, one would need to define an optimization criterion and then show that the proposed strategy achieves an allocation plan which cannot be improved upon (i.e., no other allocation plan yields a better criterion value).

    • Changing land-use policies will inevitably be a very gradual procedure, as no farmer (and probably not many governments) would suddenly accept to completely stop planting a certain crop and start planting another. In optimization terms, this would simply mean placing rate-of-change constraints in the problem formulation (e.g., no change of more than 3% of a certain crop is allowed from year to year). This also has a nice geometrical interpretation in the Figures 4 and 5 of the paper, in that one can think of all the circles starting on the diagonal line and slowly diverging from year to year (possibly converging to the solution given by the authors).

    • Given all of the uncertainty present in the real world, and the fact that one can measure the productivity obtained by a certain allocation at the end of every farm year, it may be wiser to consider an iterative experimental optimization strategy that adapts the allocation from year to year based on how past adaptations have affected productivity, as such a strategy may be shown to converge to the optimal land use in theory (unlike the heuristic strategy of the authors). Some of the early work on this topic may be found in the 1950-1960 work of George Box (on “design of experiments”) and the paper of Nelder-Mead (on the “simplex method”).

    Published in
    Reviewed by
    Ongoing discussion
  • Basic reporting

    No comment

    Experimental design

    Please could the authors clarify the following?

    How is the optimal yield for each crop/land area calculated & what assumptions are made/what factors are taken into account?

    What countries/regions are included in Figure 1? Is it all regions (including western Europe, Canada, US etc. as Figures 3&4 appear to show, or only the 'food insecure' regions specified in Figure 2?

    Also, the data set used for 'current' land use appears to be 13 years old. Are the authors aware of how land-use/farming systems may have changed in any regions during that period & of any impacts said changes may have on their results/conclusions?

    Validity of the findings

    This is an interesting concept, the implications of which are perhaps oversimplified in the conclusions.

    Although the authors recognise that there may be limitations to optimisation, I am concerned that as it stands at the moment some of the most important practical limitations (and potential impacts) are not acknowledged, and need to be discussed.
    These include limitations caused by water scarcity (and any potential impacts of the proposed model on water security), any requirement for rotations to manage productivity, trade limitations/impacts, environmental impacts & any impact on nutritional deficiencies.

    I am unsure whether the statement in the final sentence is directly relevant to the findings and would be more comfortable with conclusions that encouraged elements of the optimisation approach to be adopted where feasible.

    Comments for the author

    This is an interesting concept, the implications of which are perhaps oversimplified in the conclusions.

    Although the authors recognise that there may be limitations to optimisation, I am concerned that as it stands at the moment some of the most important practical limitations (and potential impacts) are not acknowledged, and need to be discussed.
    These include limitations caused by water scarcity (and any potential impacts of the proposed model on water security), any requirement for rotations to manage productivity, trade limitations/impacts, environmental impacts & any impact on nutritional deficiencies.

    I am unsure whether the statement in the final sentence is directly relevant to the findings and would be more comfortable with conclusions that encouraged elements of the optimisation approach to be adopted where feasible.

    Published in
    Reviewed by
    Ongoing discussion
  • Basic reporting

    I must stress that I am not an expert on agricultural ecosystems or crop yields and what constrains them. That said, I'm interested in global environmental issues and understand both estimates of production and diversity that form the core of this paper. I accepted the chance to review this manuscript because I have followed the senior author's work closely and admire his creative approaches to many problems.

    This manuscript addresses a disarmingly simple question: can we grow more crops if we adjusted the mix to maximise productivity. The answers answer an emphatic "yes" for cereals while improvements for oilseeds are much smaller. All that said, the most interesting aspects are why countries have not optimised productivity. The authors suggest a variety of possibilities. At the risk of asking them to expand a paper that is short and to the point, it seems that a more complete examination of what limits optimal production is warranted.

    1. I consider the issue of spatial scale in the next section.

    2. Would an optimal production lead to greater profits for the farmers? And to what extent are allocations driven by national subsidies?

    3. Crop diversity is important. I found figures 3 and 4 to be most informative. Clearly most production is either close to optimal diversity or exceeds it considerably. The USA and China, for example, would need to move towards much less diverse croplands if they were to improve cereal production. Spain would need to become a cereal crop monoculture, for example.

    4. Large food producers are unlikely to wish to modify current allocations to feed other countries. I would like to see the improvements of production ranked by the net balance of food exports and imports. Could food importers avoid such dependency? And at what cost in terms of crop specialization?

    Experimental design

    1. My first worry is about scale. 10 x 10km is fine scale, certainly, but I'd like to be reassured that the following possibility is excluded. For such a pixel, quite possibly irrigated rice may attain the highest productivity within a small piece of that. Extrapolating such productivity across the pixel may be impossible. Just think of what happens along the Nile, for example, where one can stand with one foot in very productive crops and the other in desert. Yes, irrigated rice is more productive than rainfed wheat, but that doesn't mean one can grow rice everywhere within the pixel.

    2. The largest changes proposed would be to replace maize with wheat in Central Africa — for a huge increase in production — and to reduce wheat in China, but grow more rice. The authors mention these changes (page 5), but do not further investigate why the changes haven't been made. Water may well prevent rice from replacing wheat in China, and soil nutrients (and water) may well prevent wheat from replacing maize in Africa, especially one considers the scale issues I have already mentioned.

      The way to investigate these possibilities is to examine a sample of pixels that seem particularly suboptimal —where, for example, rice production is high per unit area within the pixel, but only a small fraction of the pixel grows rice. If that's an irrigation issue, then the authors need to assess how large an error this causes.

    Validity of the findings

    See concerns expressed above.

    Comments for the author

    I view this as being most interesting as a way of documenting what the limitations are to increased production. The bottom line — substantial improvements — are subject to many caveats. The value of this manuscript is to list what some of them are.

    Published in
    Reviewed by
    Ongoing discussion
  • Basic reporting

    The article is clearly written, interesting, and well prepared. The figures support the reported conclusions.

    A few points of clarification are needed:

    1) Introduction, line 1: According to the UN Population Division, the human population exceeded 7 billion in October 2012 (on Halloween, notably, although this is obviously just an approximation).

    2) Introduction, on the assumption of the fungibility of crops: Obviously, this is quite a large assumption in the context of the present analysis. One could imagine lots of reasons for farmers electing to have multiple crops, ranging from balancing their dietary requirements to bet-hedging against crop-specific pathogens, weather, and crop-price fluctuations. Some brief discussion of this later in the paper would be warranted.

    3) Results section: One point on which I was not clear was crop transport. Some crops might be produced near to where their demand is concentrated, even if that locale is suboptimal. Is this factored into the analysis? I presume not. Again, this might be mentioned briefly in the Discussion.

    Experimental design

    The design of the analysis is effective and well considered, and falls within the scope of the journal. The paper contains a great deal of interesting analysis and interpretation.

    Validity of the findings

    My sense is that the analyses are reasonably robust and effectively interpreted, using the best available information and data sets at hand. The conclusions seem broadly justified by the analyses, notwithstanding the need for some minor points of clarification as indicated above.

    Comments for the author

    I found much of interest in this paper. It is appropriately framed as a sort of thought experiment, and addresses some very big and important questions.

    Published in
    Ongoing discussion