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Import dependence and per capita production are main determinants of economies' food supply robustness under production shocks

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Import dependence and per capita production are the two dominant determinants of an economy's food-supply robustness under production shocks, and reserve or trade tweaks yield only modest, crop-specific improvements.

desk verdict A useful four-crop robustness scan, but the quantitative engine is under-specified and likely mis-accounts for residuals when trade caps bind; major revision, not a desk reject. read the letter →

arxiv 2608.01010 v1 pith:O2HEOW5O submitted 2026-08-02 econ.GN q-fin.EC

Han-Yu Zhu , Maria Cristina Rulli , Wei-Xing Zhou This is my paper
classification econ.GNq-fin.EC
keywords foodsupplyrobustnessproductionshocksglobaltradenetworkimportdependencepercapitacounterfactualpolicyshockpropagationstaplecrops
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that when a major producing economy suffers a staple-food production shock, an economy's resulting food-supply loss is governed above all by two structural features: how much of its food comes from imports and how much it produces per person. The authors simulate shocks of increasing size to every major producer of wheat, rice, maize, and soybean on a calorie-based global trade network built from 2023 data, and measure each economy's robustness as its average retained supply across all shock scenarios. They report that import dependence is the single strongest predictor of low robustness and per capita production the next, that wheat is the most robust crop system and soybean the least, and that two counterfactual interventions—larger reserves and reshuffled import partners—improve robustness only modestly and inconsistently across crops. The broader point a sympathetic reader would take away is that exposure to foreign production failures is mostly a structural condition, not something easily fixed by stockpiling or changing trade partners.

What carries the argument

The key machinery is a calorie-weighted global food-supply network combined with a dynamic shock-propagation loop. Stocks absorb a shock first, with only a fraction $f_s=0.2$ of stocks usable; then a fixed fraction $f_c=0.1$ of the residual is absorbed by cutting domestic consumption; then the remaining shortfall is met by trade adjustment, with exports and imports on unblocked bilateral links scaled in proportion to current link volumes until the residual falls below a threshold $\alpha=0.001\%$. The paper's economy-level output is the robustness index $$R_i = \frac{1}{$f_p^{{\max}}$} \$int_0^{{f_p^{\max}}$} \bar{Q}_i(f_p)\, df_p,$$ the average over all shock origins and magnitudes of economy $i$'s

What would settle it

Take the 2010 Russian wheat shortfall or the 2022 Ukraine wheat shock, run the model with its proportional trade-adjustment rule, and compare the simulated post-shock supply of each importing economy with observed import and supply data for that year; if the observed cross-economy pattern diverges from the simulated ranking, the determinant claim fails.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a ranking plus an explanation. Using 2023 FAO production and bilateral trade data and USDA ending stocks, all converted to calories, the authors extend an existing shock-propagation model with a domestic-stabilization rule: any economy that must draw down stocks to absorb a supply shortfall is marked as shocked and barred from expanding exports. Simulating production losses that scale up to 1% of global production for each major producer, they compute an economic robustness index $R_i$ for every economy and crop system. The index separates economies cleanly into two robust types—those nearly isolated from trade and those with strong domestic

Load-bearing premise

The rankings rest on the assumption that the simulated trade-adjustment loop (Eqs. 13–14)—with fixed, uncalibrated parameters $f_s=0.2$, $f_c=0.1$, $\alpha=0.001\%$ and the rule that shocked economies cannot expand exports—faithfully describes how real food trade responds during a production shock.

Editorial extensions

If this is right

  • An economy that imports a large share of its staple calories and produces little per capita sits at the bottom of every crop-specific shock ranking, not just the aggregate one.
  • Wheat systems are the most robust and soybean the least, so aggregate food-security numbers hide which crop network is actually fragile.
  • Raising reserves or reweighting suppliers toward high-capacity exporters does not reliably fix exposure: both policies help the aggregate system and wheat, while trade reconfiguration can backfire for maize and soybean.
  • Because production levels are hard to change quickly, the structural exposure of import-dependent, low-production economies is a first-order constraint that stock and trade policy can only partially offset.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Beyond the paper: the policy results imply that reserves and trade reconfiguration are palliatives; the binding constraint is the domestic supply base, so interventions that raise production resilience deserve at least as much attention as stockpiling.
  • Beyond the paper: the negative effects of trade reconfiguration for maize and soybean suggest a testable prediction—redirecting imports to a few high-capacity suppliers raises single-source dependence and can backfire when those dominant suppliers are themselves the shocked nodes.
  • Beyond the paper: because per-capita production is largely fixed by agro-climatic endowments, the deterministic rankings could support a worst-case early-warning list of economies that would face catastrophic supply loss under a specific producer shock—something the average-robustness index does not reveal.
  • Beyond the paper: the model averages shocks over all major producers, so a variance or tail-stress analysis might show that robustness rankings change under extreme shocks; the same data would support such a test without new collection.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. Using 2023 FAO/USDA production, stock, and bilateral trade data, the paper constructs a calorie-based global food supply network for wheat, rice, maize, and soybean. It extends the Marchand et al. (2016) shock-cascade model with a domestic-supply-priority rule, simulates production shocks to major producers, and defines an economy-level robustness index R_i (Eq. 17) as the average relative supply over shock intensities. It then ranks economies, uses random forest and SHAP to identify determinants of R_i, and evaluates two counterfactual policies targeting the top 20% import-dependent economies: increasing reserve availability (Policy 1) and reallocating imports toward high-capacity exporters (Policy 2). The headline claims are that import dependence and per capita production are the main determinants of robustness and that both policies yield only modest, crop-heterogeneous improvements.

Significance. If the simulation were correctly specified, the paper would offer a policy-relevant, cross-crop comparison of economies' food-supply robustness and a transparent counterfactual framework built from public data. The comparative design across wheat, rice, maize, and soybean, and the explicit treatment of reserves, consumption, and trade adjustment, are useful extensions of the existing cascade literature. The random-forest and SHAP analyses also provide a clear, interpretable summary of the model's R_i surface. However, the manuscript's contribution is conditional on the shock-propagation dynamics being correct; the printed equations do not yet establish this, and the determinant and policy findings are in part built into the model rather than empirically validated.

major comments (5)
  1. [§3, Eq. (14)] The next-iteration shock is not conserved as printed. Conservation requires ΔQ_i^{k+1} = res'_i + (Σ_m ΔV_mi − Σ_j ΔV_ij), i.e., the unabsorbed residual plus the net trade change. Eq. (14) instead adds ΔTvol_i to the bilateral net change. These coincide only when the trade cap is non-binding; when the cap binds—e.g., Tvol_i = 0 for an autarkic economy—Eq. (14) returns 0 although res'_i remains unabsorbed. This artificially inflates the robustness of isolated economies and can distort Table 3 rankings, Table 4 feature importances, and the counterfactual results in Figs. 9–10.
  2. [§3, Eq. (13)] Eq. (13) is unparseable as printed: the term "1(i<shocked)" is not a defined indicator. The intended rule that shocked economies cannot expand exports is central to the model, but the multiplication structure of proportional trade adjustments is not formally specified. Without code or clarified notation, the bilateral flow changes ΔV_ij cannot be reproduced or checked.
  3. [§3 and §5, parameters] The parameters fs=0.2, fc=0.1, α=0.001%, and δ=0.1 are set without calibration or sensitivity analysis. These values directly control how much of a shock is absorbed by stocks, consumption, trade, and policy intensity. The paper reports only point results; it does not show whether the robustness rankings or the 'modest improvement' policy conclusions are robust to plausible parameter choices. This is load-bearing for the quantitative claims.
  4. [§4.3 and §5, circularity] Import dependence enters the model mechanically: trade adjustment is proportional to current link volumes (Eqs. 10–12) and shocks propagate through imports, so economies that import more from shocked producers are constructed to be more exposed. Finding import dependence as the top random-forest predictor in Table 4 is therefore partly a model property rather than an empirical discovery. Similarly, Policy 2 weights in Eqs. (19)–(20) are computed from random-forest importances fit to the same R_i that is then re-simulated, making the policy evaluation in-sample. The authors should reframe these as model-based sensitivity results or validate them against historical shocks.
  5. [Data and code availability] The manuscript does not release code, processed data, or a reproducible workflow. Given the ambiguity in Eqs. (13)–(14), the numerical tables and maps cannot currently be verified or replicated from the text alone. A revised version should provide the implementation and processed networks, or at least a precise, implementable specification of the iteration.
minor comments (5)
  1. [§3, Eq. (7)] The text uses "c f rac" instead of the defined parameter f_c; please fix the notation. Also, f_max in Eq. (17) is not defined in Table 2 or the text.
  2. [Table 3] The table contains rendering artifacts such as "BW A" repeated; a country-code key would improve readability. Some of the 'top 10' entries are not discussed in the text.
  3. [§3, Eqs. (15)–(16)] The notation Q^(j)_i(f_p) vs Q_i(f_p) is inconsistent, and the definition of the scenario set J_i should be made explicit, particularly the exclusion of a major producer's own shock in its scenario set.
  4. [§4.3, Eq. (18)] The logit transformation and the 5% winsorization procedure are described too briefly; because R_i is near 1, the logit scale is highly sensitive, and the winsorization threshold needs a precise definition.
  5. [§5, Policy 2] Eq. (21) uses N_j without definition; it should be clear whether the denominator sums over current suppliers or a broader set of potential exporters, and how zero-flow links are handled in the IPF reallocation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the simulation, determinant ranking, and counterfactual evaluations are self-contained given the paper's stated model; the overlapping-author citation to Marchand et al. is not load-bearing.

full rationale

The paper's central claims are produced by an explicit, externally parameterized simulation model (FAO/USDA data for production, trade, and stocks) combined with a shock-propagation loop (Eqs. 1-17). The robustness index R_i is computed from simulated relative supply, and the random-forest analysis then regresses R_i on economy characteristics. Although 'import dependence' is a feature in that regression, the model equations do not define R_i as a function of import dependence; the trade-adjustment mechanism involves current trade volumes, but the sign and magnitude of the feature importances are emergent outcomes, not algebraic identities. Per capita production is not even present in the shock-propagation dynamics, so its top ranking is a genuine empirical correlation with the simulated outcomes. The counterfactual policies do use random-forest importances to construct weights (Eqs. 19-20), but the policy evaluation is not guaranteed to improve; the paper reports modest, crop-varying, and sometimes negative effects, which is inconsistent with a fitted-input-called-prediction circularity. The citation to Marchand et al. (2016), which shares an author with this paper, supplies the baseline model framework; that model is externally published, not a uniqueness theorem, and is used as a stated modeling assumption rather than as evidence for the paper's conclusions. The suspicious bookkeeping in Eq. 14 (potential loss of unabsorbed shocks when the trade cap binds) is a correctness and reproducibility concern, not a circularity concern, because it does not make the output equivalent to the input by construction. Overall, the derivation chain is self-contained and the central findings are not forced by definition or by self-citation.

Assumptions & free parameters 6 free parameters · 5 assumptions · 1 invented entities

The central findings rest on hand-set simulation parameters, inherited model assumptions, and a constructed robustness index. The paper introduces no new physical entities, but the robustness metric and the behavioral rule are model-internal constructs.

free parameters (6)
  • fs = 0.2
    Fraction of stocks available to absorb shocks; set in the text without calibration or sensitivity analysis.
  • fc = 0.1
    Fraction of residual shock absorbed by domestic consumption; set without justification.
  • alpha = 0.001%
    Minimum residual-shock threshold triggering trade adjustment; set without empirical basis.
  • delta = 0.1
    Policy intensity for trade reallocation in Policy 2; no sensitivity analysis.
  • f_max = 1% of global production
    Maximum shock magnitude in the robustness index; arbitrary scale choice.
  • Policy 1 stock increase = +20%, 5th percentile baseline
    Counterfactual magnitude and target threshold (top 20% import dependence) chosen by the authors.
assumptions (5)
  • domain assumption 2023 FAO production and bilateral trade data plus USDA-PSD ending stocks accurately measure the global staple food system.
    All results depend on these data; no validation against other years or datasets.
  • domain assumption Caloric conversion factors allow quantities of wheat, rice, maize, and soybean to be compared on a common calorie basis.
    Conversion factors are referenced but not listed; cross-crop aggregation and comparisons rely on them.
  • domain assumption The Marchand et al. (2016) cascade model, with the added domestic-supply-stabilization rule, is a valid representation of shock absorption and trade reallocation.
    The model is inherited and not independently validated; Eqs. (13)-(14) are ambiguous as printed.
  • ad hoc to paper Economies prioritize domestic supply and, once shocked, cannot expand exports.
    Introduced as the new behavioral rule; plausible but unvalidated.
  • domain assumption Random forest feature importance and SHAP values identify determinants rather than mere correlates.
    Used to declare import dependence and per-capita production the main determinants; no causal identification.
invented entities (1)
  • Economic robustness index R_i
    purpose: Quantify average relative food supply under production shocks for each economy.
    Defined directly from the model's own simulated outputs (Eq. 17); it is a constructed measurement with no external benchmark.

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Cite this review

Pith. "Pith review of Import dependence and per capita production are main determinants of economies' food supply robustness under production shocks." pith.science (2026). https://pith.science/paper/O2HEOW5O

@misc{pith2026260801010,
  author       = {Pith},
  title        = {Pith review of: Import dependence and per capita production are main determinants of economies' food supply robustness under production shocks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O2HEOW5O}},
  note         = {Machine review of arXiv:2608.01010}
}
read the original abstract

Food supply shocks in major producing economies can propagate through trade networks and generate uneven impacts across the global food system. This study examines the robustness of economies' food supply under production shocks to major producers in the global staple food system. Using 2023 production, reserve, and bilateral trade data for wheat, rice, maize, and soybean, we construct a calorie-based global food supply network across economies. We extend a dynamic shock propagation framework and then simulate production shocks to major producing economies, tracing how supply losses propagate. The results show substantial heterogeneity in robustness across crops and economies. Wheat exhibits the highest overall robustness, whereas soybean shows the lowest. Economies with high robustness tend to be either relatively isolated from the trade network or actively engaged in trade while maintaining strong and stable domestic production, whereas low-robustness economies are predominantly those with high import dependence. Import dependence and per capita production emerge as the most important determinants of robustness. Based on these findings, we design two counterfactual policies targeting highly import-dependent economies: increasing reserve availability and adjusting trade linkages. Counterfactual experiments show that the two policies yield only modest overall improvements, with effects varying substantially across crops. Both policies improve robustness in the aggregated system and wheat, trade adjustment is more effective for rice, and it brings limited or even negative effects for maize and soybean.

Figures

Figures reproduced from arXiv: 2608.01010 by the authors.

Figure 1
Figure 1. Geographical distribution of staple crop production (2023). Reserves and trade both serve as key buffers when production shocks occur (Marchand et al., 2016). Stocks pro￾vide an additional cushion: releasing food stocks can temporarily supplement domestic availability, reduce immediate shortfalls when production or imports decline, and help stabilize prices, thereby enhancing food security (Wright, 2011). However, m… view at source ↗
Figure 2
Figure 2. Distribution of year-on-year production declines (1986–2023) among major producers (≥ 1% of global production in 2023). Taken together, these considerations motivate the core focus of this study. When staple production is concentrated and production shocks occur, economies’ supply outcomes depend on how the shock is absorbed domestically and how shortfalls are transmitted and reallocated through trade linkages. We t… view at source ↗
Figure 3
Figure 3. The flow chart of the simulation model. The simulation model is the same as Marchand et al. (2016), except that we incorporate domestic supply stabilization (the red part). Economies utilizing internal stocks are classified as shocked and cannot increase exports. more than 1% of global total production are selected as candidates for the initial shock. For a shocked major producing economy i, the production shock is … view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Robustness of economies under production shocks to major staple crops. In each panel, darker blue denotes higher robustness and darker red denotes lower robustness. Red circles identify the 10 least robust economies, whereas blue triangles indicate the 10 most robust e…
Figure 5
Figure 5. Figure 5: Economic robustness across different income groups. Rows show the shocked major producer economies, while columns show the income groups of affected economies. L, LM, UM, and H denote low-income, lower-middle-income, upper-middle￾income, and high-income groups, respect…
Figure 6
Figure 6. Figure 6: Economic robustness across different region groups. Rows show the shocked major producer economies, while columns show the region groups of affected economies. Colors indicate the robustness of each region group under shocks originating from different major producers. …
Figure 7
Figure 7. Figure 7: Correlation matrix of staple crop robustness and determinant variables across economies. To clarify the directional impact of these features on supply robustness, we generated SHAP summary plots as illustrated in [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: SHAP-based feature analysis of the robustness of economies economy to obtain pi j: pi j = SCi P k∈Nj SCk (21) A policy intensity parameter δ is introduced to control the degree of convergence toward the target structure, yielding the final trade share s ′ i j: s ′ i j …
Figure 9
Figure 9. Figure 9: Effects of Policy 1 on robustness. Policy 1 increases food stocks for the top 20% most import-dependent economies, and colors indicate the proportional change in robustness relative to the baseline. Africa show noticeable improvements, suggesting that, compared with Po…
Figure 10
Figure 10. Figure 10: Effects of Policy 2 on robustness. Policy 2 reoptimizes import allocations for the top 20% most import-dependent economies toward exporters with higher supply capacity, and colors indicate the proportional change in robustness relative to the baseline. The results on …

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Works this paper leans on

27 extracted references · 24 canonical work pages

  1. [2]

    , author Qiu, F

    author An, H. , author Qiu, F. , author Zheng, Y. , year 2016 . title How do export controls affect price transmission and volatility spillovers in the ukrainian wheat and flour markets? journal Food Policy volume 62 , pages 142--150 . :10.1016/j.foodpol.2016.06.002

    work page doi:10.1016/j.foodpol.2016.06.002 2016

  2. [3]

    , author Debucquet, D.L

    author Bouet, A. , author Debucquet, D.L. , year 2012 . title Food crisis and export taxation: the cost of non-cooperative trade policies . journal Rev. World Econ. volume 148 , pages 209--233 . :10.1007/s10290-011-0108-8

    work page doi:10.1007/s10290-011-0108-8 2012

  3. [4]

    , author Schweitzer, F

    author Burkholz, R. , author Schweitzer, F. , year 2019 . title International crop trade networks: the impact of shocks and cascades . journal Environ. Res. Lett. volume 14 , pages 114013 . :10.1088/1748-9326/ab4864

    work page doi:10.1088/1748-9326/ab4864 2019

  4. [5]

    , author Tu, Y

    author Chen, B. , author Tu, Y. , author An, J. , author Wu, S. , author Lin, C. , author Gong, P. , year 2024 . title Quantification of losses in agriculture production in eastern ukraine due to the russia-ukraine war . journal Commun. Earth Environ. volume 5 , pages 336 . :10.1038/s43247-024-01488-3

    work page doi:10.1038/s43247-024-01488-3 2024

  5. [6]

    , author Haigh, T

    author Church, S.P. , author Haigh, T. , author Widhalm, M. , author de Jalon, S.G. , author Babin, N. , author Carlton, J.S. , author Dunn, M. , author Fagan, K. , author Knutson, C.L. , author Prokopy, L.S. , year 2017 . title Agricultural trade publications and the 2012 midwestern u.s. drought: a missed opportunity for climate risk communication . jour...

    work page doi:10.1016/j.crm.2016.10.006 2017

  6. [7]

    , author Laio, F

    author Distefano, T. , author Laio, F. , author Ridolfi, L. , author Schiavo, S. , year 2018 . title Shock transmission in the international food trade network . journal PLoS One volume 13 , pages e0200639 . :10.1371/journal.pone.0200639

    work page doi:10.1371/journal.pone.0200639 2018

  7. [8]

    , author Otto, C

    author Falkendal, T. , author Otto, C. , author Schewe, J. , author Jagermeyr, J. , author Konar, M. , author Kummu, M. , author Watkins, B. , author Puma, M.J. , year 2021 . title Grain export restrictions during COVID -19 risk food insecurity in many low- and middle-income countries . journal Nat. Food volume 2 , pages 11--14 . :10.1038/s43016-020-00211-7

    work page doi:10.1038/s43016-020-00211-7 2021

  8. [9]

    , author van Oort, P.A.J

    author Fonteijn, H.M.J. , author van Oort, P.A.J. , author Hengeveld, G.M. , year 2024 . title Darts: evolving resilience of the global food system to production and trade shocks . journal J. Artif. Soc. Soc. Simul. volume 27 , pages 5307 . :10.18564/jasss.5307

    work page doi:10.18564/jasss.5307 2024

Show all 27 references
  1. [10]

    , author Rocha, N

    author Giordani, P.E. , author Rocha, N. , author Ruta, M. , year 2016 . title Food prices and the multiplier effect of trade policy . journal J. Int. Econ. volume 101 , pages 102--122 . :10.1016/j.jinteco.2016.04.001

    2016 doi

  2. [11]

    , author Mangioni, G

    author Grassia, M. , author Mangioni, G. , author Schiavo, S. , author Traverso, S. , year 2022 . title Insights into countries' exposure and vulnerability to food trade shocks from network-based simulations . journal Sci. Rep. volume 12 , pages 4644 . :10.1038/s41598-022-08419-2

    2022 doi

  3. [12]

    , year 2011

    author Headey, D. , year 2011 . title Rethinking the global food crisis: the role of trade shocks . journal Food Policy volume 36 , pages 136--146 . :10.1016/j.foodpol.2010.10.003

    2011 doi

  4. [13]

    , author Puma, M.J

    author Heslin, A. , author Puma, M.J. , author Marchand, P. , author Carr, J.A. , author Dell'Angelo, J. , author D'Odorico, P. , author Gephart, J.A. , author Kummu, M. , author Porkka, M. , author Rulli, M.C. , author Seekell, D.A. , author Suweis, S. , author Tavoni, A. , y...

    2020

  5. [14]

    , author Forero, O.A

    author Huang, I.Y. , author Forero, O.A. , author Wagner-Medina, Erika, V. , author Diaz, H.F. , author Tremma, O. , author Fargetton, X. , author Lowenberg-DeBoer, J. , year 2025 . title Resilience of food supply systems to sudden shocks: a global review and narrative synthes...

    2025

  6. [15]

    , author Femia, F

    author Hunt, E. , author Femia, F. , author Werrell, C. , author Christian, J.I. , author Otkin, J.A. , author Basara, J. , author Anderson, M. , author White, T. , author Hain, C. , author Randall, R. , author McGaughey, K. , year 2021 . title Agricultural and food security i...

    2021

  7. [16]

    , author Xia, Z

    author Jia, N. , author Xia, Z. , author Li, Y. , author Yu, X. , author Wu, X. , author Li, Y. , author Su, R. , author Wang, M. , author Chen, R. , author Liu, J. , year 2024 . title The russia-ukraine war reduced food production and exports with a disparate geographical imp...

    2024 doi

  8. [17]

    , author Sakurai, T

    author Kafando, W.A. , author Sakurai, T. , year 2025 . title Effects and mechanisms of armed conflict on agricultural production: spatial evidence from terrorist violence in burkina faso . journal J. Agric. Econ. volume 76 , pages 24--44 . :10.1111/1477-9552.12613

    2025

  9. [18]

    , author Kubiczek, P

    author Kuhla, K. , author Kubiczek, P. , author Otto, C. , year 2025 . title Understanding agricultural market dynamics in times of crisis: the dynamic agent-based network model agrimate . journal Ecol. Econ. volume 231 , pages 108546 . :10.1016/j.ecolecon.2025.108546

    2025

  10. [19]

    , author Kinnunen, P

    author Kummu, M. , author Kinnunen, P. , author Lehikoinen, E. , author Porkka, M. , author Queiroz, C. , author Roos, E. , author Troell, M. , author Well, C. , year 2020 . title Interplay of trade and food system resilience: gains on supply diversity over time at the cost of...

    2020

  11. [20]

    , author Rowhani, P

    author Lesk, C. , author Rowhani, P. , author Ramankutty, N. , year 2016 . title Influence of extreme weather disasters on global crop production . journal Nature volume 529 , pages 84--+ . :10.1038/nature16467

    2016 doi

  12. [21]

    , author Schlenker, W

    author Lobell, D.B. , author Schlenker, W. , author Costa-Roberts, J. , year 2011 . title Climate trends and global crop production since 1980 . journal Science volume 333 , pages 616--620 . :10.1126/science.1204531

    2011 doi

  13. [22]

    , author Carr, J.A

    author Marchand, P. , author Carr, J.A. , author Dell'Angelo, J. , author Fader, M. , author Gephart, J.A. , author Kummu, M. , author Magliocca, N.R. , author Porkka, M. , author Puma, M.J. , author Ratajczak, Z. , author Rulli, M.C. , author Seekell, D.A. , author Suweis, S....

    2016 doi

  14. [23]

    , author An, H

    author Rude, J. , author An, H. , year 2015 . title Explaining grain and oilseed price volatility: the role of export restrictions . journal Food Policy volume 57 , pages 83--92 . :10.1016/j.foodpol.2015.09.002

    2015 doi

  15. [24]

    , author Moreira, A.A

    author Schneider, C.M. , author Moreira, A.A. , author Andrade, J.S. , author Havlin, S. , author Herrmann, H.J. , year 2011 . title Mitigation of malicious attacks on networks . journal Proc. Natl. Acad. Sci. U.S.A. volume 108 , pages 3838--3841 . :10.1073/pnas.1009440108

    2011 doi

  16. [25]

    , year 1996

    author Simon, M. , year 1996 . title Food security: A post-modern perspective . journal Food Policy volume 21 , pages 155--170 . :https://doi.org/10.1016/0306-9192(95)00074-7

    1996 doi

  17. [26]

    , author Suweis, S

    author Tu, C. , author Suweis, S. , author D'Odorico, P. , year 2019 . title Impact of globalization on the resilience and sustainability of natural resources . journal Nat. Sustain. volume 2 , pages 283--289 . :10.1038/s41893-019-0260-z

    2019 doi

  18. [27]

    , author Porkka, M

    author Wassenius, E. , author Porkka, M. , author Nystrom, M. , author Jorgensen, P.S. , year 2023 . title A global analysis of potential self-sufficiency and diversity displays diverse supply risks . journal Glob. Food Secur. volume 37 , pages 100673 . :10.1016/j.gfs.2023.100673

    2023

  19. [28]

    , year 2011

    author Wright, B.D. , year 2011 . title The economics of grain price volatility . journal Appl. Econ. Perspect. Policy volume 33 , pages 32--58 . :10.1093/aepp/ppq033

    2011 doi

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