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    The spatial configuration of biotic interactions shapes coexistence-area relationships in an annual plant community

    Study systemWe conducted our study in Caracoles Ranch, located in Doñana National Park (SW Spain 37° 04′ N, 6° 18′ W). The study area has a Mediterranean climate with mild winters and an average 50-year annual rainfall of 550–570 mm. Vegetation is dominated by annual grassland species, with no perennial species present. A subtle topographic gradient (slope 0.16%) is enough to generate vernal pools at the lower border of the ranch from winter (November–January) to spring (March–May), while upper parts do not get flooded except in exceptionally wet seasons. In our study, an extreme flooding event occurred during the growing season of 2018. A strong soil salinity–humidity gradient is structured along this topographic gradient.In September 2014, we established nine plots of 8.5 m × 8.5 m along a 1 km × 200 m area. Three of these nine plots were located in the upper part of the topographic gradient, three at the middle, and the last three at the lower part. The average distance between these three locations was 300 m and the average distance between plots within each location was 30 m (minimum distance 20 m). In addition, each plot was divided into 36 subplots of 1 m × 1 m with aisles of 0.5 m in between to allow access to subplots where measurements were taken (total of 324 subplots). This spatial design was established to parameterize population models including an intrinsic fecundity component and the effect of intra- and interspecific pairwise interactions. Specifically, the core of the observations involved measuring, for each focal individual, per germinant viable seed production as a function of the number and identity of neighbors within a radius of 7.5 cm including individuals of the same species. This radius is a standard distance used in previous studies to measure competitive interactions among annual plant species29,34, and has been validated to capture the outcome of competition interactions at larger scales (1 m²) under locally homogeneous environmental conditions35. From November 2014 to September 2019, we sampled 19 species present in the study area each year. We sampled one individual per subplot for widespread species and several individuals per subplot when species were rare (max. 324 individuals/species). This sampling design ensured that all species are balanced in terms of number of observations, and that we capture the full range of observed spatial interactions among species across the study area. Furthermore, we obtained independent estimates of seed survival and seed germination rates in 2016 (see17 for details on obtaining these rates). These 19 species belong to disparate taxonomic families and exhibit contrasted functional profiles along the growing season (Supplementary Table 1). The earliest species with small size and open flowers, such as C. fuscatum (Asteraceae), peak at beginning of the growing season (February), while late species with succulent leaves, such as S. soda (Amaranthaceae) and S. splendens (Amaranthaceae), grow during summer and peak at the end of the growing season (September-October). All these species represent up to 99% of plant cover in the study area.Estimating species interaction networks and intrinsic growth ratesWe estimated the effect of nearby individuals on individual fecundity via a Ricker model of population dynamics, which allowed us to estimate the strength of positive or negative interactions among pair of species, and therefore, to build a matrix of interactions among species. This approach has been previously applied to study annual plant systems under Mediterranean-type climates36, and it has also recently been shown to have several advantages compared to other formulations34. For example, this model implemented using a negative-binomial distribution for individual fecundities is more flexible in terms of modeling over-dispersion than a Poisson model, while maintaining predictions as positive integers. The model is of the form$${F}_{i,t}={lambda }_{i}{e}^{-({sum }^{}{alpha }_{i,j}{N}_{j,t})}$$
    (1)
    where ({lambda }_{i}) is the number of seeds produced by species i in the absence of interactions, ({alpha }_{i,j}) is the per capita effect of species j over species i (which can be positive or negative, thus allowing both competitive and facilitative effects), and ({N}_{j,t}) is the number of individuals of species j within 7.5 cm of the focal individual at timestep t. We fitted this model to the empirical data using Bayesian multilevel models with a negative-binomial distribution34. For model fitting, we used non-informative priors with MCMC settings of 5000 iterations (of which 2500 were warm-up) and 6 chains. The model was implemented using the brms R package37. The effect of changes in environmental conditions on species persistence can be phenomenologically evaluated by allowing models to vary in their estimates of species’ intrinsic growth rates and the reorganization of species interactions38. In our case, to evaluate the effect of environmental heterogeneity on species persistence (Question 1), we developed two complementary models. In both cases, we modeled the observed viable seed production per individual as a function of the identity and abundance of neighboring species. For the model assuming that plant species interact within a homogeneous environment across plots, we pooled together observations from the whole study area, and allowed the intercept and slope of the relationships to vary across years by including year as a random effect. Thus, the ({lambda }_{i}) and ({alpha }_{i,j}) values in Eq. 1 vary across years, but are homogeneous for the whole study area. We used the means from the obtained posterior distributions as estimates in the subsequent analyses. For the model that assumes that heterogeneous environments across space and time impact species population dynamics, we included an additional crossed random effect “plot”, thus obtaining spatially and temporally differentiated seed production in the absence of neighbors (({lambda }_{i})) and interaction coefficients (({alpha }_{i,j})). Importantly, our modeling approach does not evaluate the magnitude per se of the spatiotemporal variability in our system. It rather evaluates the response of plant species to changes in environmental conditions through their effects on vital rates and interaction coefficients (see39,40,41 for similar approaches). Likewise, this approach does not model the spatial dynamics of the community or spatially explicit mechanisms such as dispersal, but rather uses observed spatially explicit associations of individuals to infer their vital rates and interaction coefficients. In the following, we refer to the two developed models as “homogeneous parameterization” and “heterogeneous parameterization”, respectively (Fig. 1).The statistical methodology generates a posterior distribution of estimates for each parameter inferred, i.e., for each intrinsic fecundity rate (({lambda }_{i})) and interaction coefficient (({alpha }_{i,j})). These means, by definition, do not capture the full variability obtained with the statistical model, and may potentially be biased, especially for species pairs that have comparatively few observations. To ensure that our results were not biased by using the posterior mean as a fixed value in subsequent analyses, we replicated our analyses using random samples from the posterior distributions instead of the mean values. We generated 100 random draws from each parameterization and compared the obtained curves to the ones derived from the posterior means (Supplementary Note 1 and Supplementary Fig. 3).Finally, we assume that the study system presents a rich soil seed bank but we do not explicitly model its direct influence on driving the spatial pattern of species interactions or intrinsic vital rates: rather we use fixed field estimates of seed survival and germination rates in our modeling framework (see section “Analyzing species persistence”). This assumption implies that we cannot evaluate the contribution of a spatially or temporally varying seed bank to the shape of CARs and SARs, which remains an open question for future studies.Analyzing species persistenceTo analyze which species are predicted to persist and coexist with others in our system, we built communities based on the species’ spatial location. At the smallest spatial scale, given a community of S species observed in the field in a given plot and a given year, we calculated the persistence of each species within every community combination, from 2 species to S. Thus, we obtained for each species, plot, and year, two estimates of persistence, one from the homogeneous and another from the heterogeneous parameterization. To scale-up our predictions of species persistence at increasingly large areas, we aggregated species composition and persistence patterns from increasing numbers of plots. We consistently evaluated species persistence using a structuralist approach because prior work has shown it is compatible with the model used to estimate interaction coefficients (Eq. 1)14. Specifically, for a given community we first used the strength of sign of intra- and interspecific interactions to compute its feasibility domain (note that the structuralist approach can accommodate different signs in the interaction coefficients). Broadly speaking the feasibility domain is the structural analog of niche differences, and it represents the possible range of intrinsic species growth rates compatible with the persistence of individual species and of the entire community14. Indeed, the larger the feasibility domain, the larger the likelihood of species to persist. Yet, computing the feasibility domain does not tell us which species can persist. To obtain such information, we need to check whether the vector containing the observed differences in intrinsic growth rates between species fits within the limits of the feasibility domain. If so, then all species are predicted to coexist. If not, then one or more particular species is predicted to be excluded (see14 for a graphical representation).In order to quantify the feasibility of ecological communities, the intrinsic growth rates and interaction coefficients must be formulated according to a linear Lotka-Volterra model, or an equivalent formulation14. We transformed the parameters obtained from Eq. 1 to an equivalent Lotka-Volterra formulation with the following expression (Supplementary Note 2):$${r}_{i}={log}left(frac{1-(1-{g}_{i}){s}_{i}}{{g}_{i}}right)+{lambda }_{i}$$
    (2)
    where ({g}_{i}) is the seed germination rate of species i and ({s}_{i}) is its seed survival rate. Thus, we quantified the feasibility of our communities using the ri intrinsic growth rates from Eq. 2 and the ({alpha }_{i,j}) coefficients, which are not modified. For our main analyses, we used empirical estimates of seed survival and germination rates. We further explored the influence of these vital rates in the transformed intrinsic growth rates in Supplementary Note 2 (see also Supplementary Fig. 4 and Supplementary Table 4).The structuralist methodology further allowed us to dissect which specific configuration of species interactions is behind species persistence in our system (Question 2), among three possibilities: first, a given species may be able to persist by itself, and hinder the long-term persistence of neighboring species (category dominant). Second, pairs of species may be able to coexist through direct interactions (category coexistence of species pairs). The classic example of two-species coexistence is when the stabilizing effect of niche differences that arise because intraspecific competition exceeds interspecific competition overcome fitness differences41. Lastly, species may only be able to coexist in more complex communities (category multispecies coexistence)23, thanks to the effect of indirect interactions on increasing the feasible domain of the community14. A classic example of multispecies coexistence is a rock–paper–scissors configuration in which the three species coexist because no species is best at competing for all resources24,42. Because species may be predicted to persist under different configurations in a given community, we assigned their persistence category to the simplest community configuration. For instance, if we predicted that a three-species combination coexists as well as each of the three pairs separately, we assigned these species to the coexistence of pairs category26,43. Finally, if a species is not predicted to persist but it is observed in the system, we classify it as naturally transient, that is, it will tend to become locally extinct no matter what its surrounding community. In order to ascertain our classification of species as transient, we further analyzed whether these species shared ecological traits known to be common to transient species. In particular, a pervasive characteristic of transient species is their comparatively small population sizes. We explored the relationship between our classification as transient and species abundance through a logistic regression with logit link (supplementary Table 3).In addition to our main analyses, based on the structuralist approach, we explored the local stability44 of the observed communities, which evaluates their asymptotic response to infinitesimal perturbations, and thus provides a complementary view to the potential coexistence of the system (Supplementary Note 3).Species–area and coexistence–area relationshipsTo answer Question 3, we obtained standard SARs for each year, by calculating the average diversity observed when moving from 1 plot (72 m²) to 9 plots (650 m²) of our system. In the classification of SAR types proposed by Scheiner et al.45, the curves from our system are thus of type III-B, i.e., plots in a non-contiguous grid, with diversity values obtained using averages from all possible combinations of plots. Likewise, the yearly CARs were built taking the average number of coexisting species in each combination from 1 to 9 plots. In this case, a species was taken to persist in a given area if it was persisting alone or if it was part of at least one coexisting community within that area. We obtained CARs for the two parameterizations, i.e., assuming homogeneous interaction coefficients and individual fecundity throughout the study area, or explicitly including spatial variability in these terms. We fitted the CARs from Fig. 2 to power-law functions and obtained their associated parameters (Supplementary Table 2) using the mmSAR v1.0 package in R46. In the last step of the analyses, to evaluate the role of species identity in driving these empirical fits of CARs, we compared them to two complementary null models that reshuffle the strengths of per capita interactions between species pairs across the interaction matrix. In particular, as baseline we took the CARs from the homogeneous parameterization, in order to have a unique interaction strength value per species pair. In the first null model, and taking the inferred interaction matrix from a given plot and year, we redistributed the pairwise interaction coefficients randomly. That is, we fixed the number of species observed in a certain plot and year, as well as the structure of the interaction matrix, but randomized the magnitude of observed pairwise interactions (both intra and interspecific interactions) in that community. The second null model is similar, but keeping the diagonal coefficients of the interaction matrix, i.e., the intraspecific terms, fixed. While the first null model accounted for the effect of interspecific competitive responses, as well as self-limiting processes on driving CARs, the second null maintained self-limiting processes fixed by avoiding changes in the diagonal elements of our interaction matrices. We ran 100 replicates of each model for each year, and obtained the average CARs across replicates. All analyses were carried out in R v3.6.3, using packages tidyverse47 v.1.3.1 for data manipulation and visualization, and foreach48 v1.5.1 and doParallel49 v1.0.16 for parallelizing computationally intensive calculations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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    The answer to the biodiversity crisis is not more debt

    EDITORIAL
    26 October 2021

    The answer to the biodiversity crisis is not more debt

    Funding pledges from China and other countries need to be given in grants — which must include research grants — and not as a reward for taking out loans.

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    The Alichur Valley in Tajikistan is among a number of ecologically sensitive areas that researchers say could be affected by China’s Belt and Road Initiative.Credit: Alamy

    Funding for biodiversity is getting some attention at last.In September, nine philanthropic organizations, most of them in the United States, pledged a total of US$5 billion over a decade towards projects that will help to preserve the richness of Earth’s species.This month, Chinese President Xi Jinping announced the allocation of 1.5 billion yuan ($235 million) to the new Kunming Biodiversity Fund. This will have a goal of funding projects, such as protected areas, that will contribute to slowing down and eventually reversing the loss of species and ecosystems.More details are awaited from China, along with further information on a promise made by the European Union to double its funding for biodiversity. Contributions to the Kunming fund should be given as grants, not loans; they should have a research component; and they should be pooled and managed through international organizations. Moreover, the rules for access need to be transparent and fair to all applicants. These are important factors to emphasize, because there seems to be a trend towards providing environmental finance as loans — many of them to some of the world’s poorest countries, which are often already highly indebted.
    The broken $100-billion promise of climate finance – and how to fix it
    The pledges were timed to coincide with the first part of the China-hosted United Nations biodiversity conference, COP15, which ended on 24 October. Collectively, the sums, although not insignificant, will amount to little more than a 1–2% increase on the roughly $133 billion a year that the world currently spends on biodiversity. Well over half of this is spent by China, the EU, Japan and the United States.Spending on biodiversity needs to increase in all regions, according to a report by the UN Environment Programme, published in May (see go.nature.com/3ekaopk). For comparison, money earmarked for tackling climate change totalled $632 billion per year in 2019–20, according to a Nature analysis (Nature 598, 400–402; 2021).The reasons that finance for biodiversity is lower than that for its climate cousin include a relative dearth of finance in low- and middle-income countries and the fact that more than half of all climate funds take the form of loans. Both public and private investors know that in financing projects such as solar energy plants or batteries research and development, they will probably see a return on their investments. By contrast, protecting a watershed or a wetland is more of a public service — and so is more likely to be funded from taxation. Partly as a result, some 86% of biodiversity funding currently comes from public sources, in the form of grants.But that might be about to change. Researchers, corporations, bankers and policymakers have been exploring how to create financial investment products — from both private and public sources — in biodiversity, as well as how to better protect nature from the negative environmental impacts of big infrastructure projects. Most industrial sectors rely on biodiversity to some extent. Food producers, forestry, clothing manufacturers and hydropower, for example, would all struggle without healthy soils, pollinators or predictable water supplies. If nature continues to degrade, the world’s economic output will begin to suffer sooner or later.
    Global climate action needs trusted finance data
    One idea being studied is how to create an internationally agreed reporting system so that any entity — a bank, a government or a corporation — would need to publish data on whether its investments could lead to ecological damage. Such disclosures would probably prompt financiers to think twice before taking on investments that might be environmentally harmful. Earlier this year, an organization called Taskforce on Nature-related Financial Disclosures began work to develop such a system. It is co-chaired by Elizabeth Mrema, the executive secretary of the UN biodiversity convention secretariat, and is based in Montreal, Canada.Another idea under study is called Nature Performance Bonds (NPBs). According to this model, indebted countries would be eligible for more-favourable loan repayment terms if they could commit to spending the cash saved on environmental protection.Last month, a study commissioned by the China Council for International Cooperation on Environment and Development, an organization of policymakers that advises China’s government, recommended that China become a global leader in NPBs (see go.nature.com/3pekzk7). The study says that some 52 low- and middle-income countries owe China a combined total of more than $100 billion in loans. These include loans for projects that are part of China’s Belt and Road Initiative (BRI) to upgrade energy sources, roads, railways and airports, mainly in low- and middle-income countries. Many of China’s BRI investments are in ecologically sensitive areas.The terms of China’s $235-million biodiversity announcement have not yet been confirmed. But it would be wise if this funding were not linked to the debts of countries whose biodiversity is being affected by BRI projects. Otherwise it would seem that China’s main motivation is the greening of its own investments, when, as the host of COP15, it needs to think and act more globally, and work towards creating a fund by and for all nations.
    Where climate cash is flowing and why it’s not enough
    The Kunming Biodiversity Fund needs to be a stand-alone grant fund, ideally managed by a mechanism involving all countries, and with transparent rules of access. It also needs to have a dedicated research component — something that is not possible through loan finance. And other nations must contribute.The need for research funding is especially acute. There are often few funding opportunities from national research bodies for researchers in low- and middle-income countries that are rich in biodiversity. The UN’s official biodiversity funder, the Global Environment Facility, based in Washington DC, does not have a dedicated research facility. It does fund some science, but that is a part of a small-grants programme (see go.nature.com/3mgu8io) that is mainly focused on funding for conservation.It is clear that biodiversity will be getting more finance. But loan finance must not crowd out or replace grant funding. There is a precedent for this. It is already happening in climate finance, for which a much-delayed $100 billion pledged to be provided annually to low- and middle-income countries will be mainly in the form of loans.A step change in biodiversity finance is needed and the Kunming Biodiversity Fund will be a welcome move in the right direction. But it will be inequitable if most of the promised finance ends up committed to loans. Finding an answer to the biodiversity crisis should not mean the poorest countries having to take on yet more debt.

    Nature 598, 539-540 (2021)
    doi: https://doi.org/10.1038/d41586-021-02891-y

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    Optimal virtual water flows for improved food security in water-scarce countries

    Crop production, water productivity, and virtual waterA method to calculate the water needed for crops is the water footprint (WF). The WF has a color-based classification: green water (precipitation), blue water (ground and surface water), and grey water (water to dilute polluted water to accepted water quality standards). A manual on how to calculate WFs has been published12. Calculations of WFs integrate green and blue crop water use (evapotranspiration by crops) over the growing period of specific crops and express results per unit of yield (m3 kg−1). The difference between crop-water use and effective rainfall is applied as a proxy for blue WFs when no data on actual irrigation water supply are available. WFs of specific crops vary greatly among countries, and even within countries45. This means that water can be saved when crops are smartly traded. This may also be possible within a country if crops are grown where water productivity is the highest.Calculation of the water footprintWater footprints (WFs) are calculated as green and blue water footprints (WFgreen, WFblue, respectively) adopting the method from the WF manual12. This study assumes that the difference between crop water requirement and evapotranspiration of green water (ETGreen) in crops is equal to the evapotranspiration of blue water (ETblue); therefore, crop water requirements are met with irrigation water. The crop water requirements are estimated with the Food and Agriculture Organization’s CROPWAT model46. The selected methods for calculating the reference evapotranspiration (ET0) and effective precipitation (Peff) are the FAO Penman–Monteith method47,48 and the USDA’s SCS method48, respectively. Calculations were performed at the provincial scale for each crop. Equations (1) through (4) are applied to calculate WFgreen and WFblue for the crops included in this study:Actual crop evapotranspiration from reference evapotranspiration:$$ ET_{c} = sum_{t} {ET_{0} times K_{c} } $$
    (1)
    Reference evapotranspiration:$$ ET_{0} = frac{{0.408Delta left( {R_{n} – G} right) + gamma frac{900}{{T + 273}}U_{2} left( {e_{a} – e_{d} } right)}}{{Delta + gamma left( {1 + 0.34U_{2} } right)}} $$
    (2)
    $$ WF_{green} = 10 times frac{{min left[ {ET_{c} ,P_{eff} } right]}}{Y} $$
    (3)
    $$ WF_{blue} = 10 times frac{{max left[ {0,ET_{c} – P_{eff} } right]}}{Y} $$
    (4)
    where ETc denotes the actual crop evapotranspiration (mm) during the growth period (t), ET0 represents the reference evapotranspiration (mm day−1), and Kc denotes the crop coefficient based on crop type and development stages (initial, middle, and late stages). In Eq. (2) ea (kPa), ed (kPa), Δ (kPa °C−1), G (MJ m−2 day−1), T (°C), Rn (MJ m−2 day−1), U2 (m s−1), and γ (kPa °C−1) denote the saturation vapor pressure, the actual vapor pressure, the slope of the saturation-vapor pressure curve, the soil heat flux, the average air temperature, the net radiation on the crop surface, the wind speed measured at a height of 2 m above ground level, and the psychrometric constant, respectively. Equations (3) and (4) calculate the green and blue water footprints (m3 ton−1), in which Peff (mm), Y (ton ha−1), and 10, are represent effective precipitation, the crop yield, and a conversion factor from mm to m3 ha−1, respectively. WFgreen and WFblue occur in irrigated cultivation; however, there is only WFgreen in rainfed cultivation.Optimization of crop productionAll the steps of the methods used in this work were coded in MATLAB for use by decision-makers, planners, and interested organizations.Balancing the agricultural systemAn internal trade network was created to organize and remedy the weaknesses of the trade network. The lack of a comprehensive trade network has caused the crops to be exported regardless of the country’s demands, which eventually leads to the import of the same crops. The production and demand amounts of each crop in each province and their WFgreen and WFblue are determined with the following equations applied to N = 51 crops in J = 31 provinces:$$ {CP}_{(i,j)} = {ICP}_{(i,j)} + {RCP}_{(i,j)} $$
    (5)
    $$ {ICP}_{(i,j)} = left( {{BCY}_{(i,j)} times {ICA}_{(i,j)} } right) $$
    (6)
    $$ {RCP}_{(i,j)} = left( {{GCY}_{(i,j)} times {RCA}_{(i,j)} } right) $$
    (7)
    $$ {CD}_{(i,j)} = left( {{PCD}_{i} times {POP}_{J} } right) $$
    (8)
    $$ {TWF}_{blue(i,j)} = {ICP}_{(i,j)} times {WF}_{blue(i,j)} $$
    (9)
    $$ {TWF}_{green(i,j)} = {ICP}_{(i,j)} times {WF}_{green(i,j)} $$
    (10)
    where (i=1, 2,ldots , N;j=1, 2, ldots, J,) CP(i,j) (ton), ICP(i,j) (ton), RCP(i,j) (ton), BCY(i,j) (ton.ha−1), GCY(i,j) (ton.ha−1), ICA(i,j) (ha), RCA(i,j) (ha), CD(i,j) (ton), PCDi (ton.person−1), POPj (person), TWFblue(i,j) (m3), and TWFgreen(i,j) (m3) denote the production of crop i in province j, crop production of irrigated land, crop production in rainfed cultivation, irrigated crop yield, rainfed crop yield, irrigated acreage, rainfed areas acreage, demand for crop i in province j, per capita diet, population of province j, the blue WF of crop i in province j corresponding to irrigated cultivation, and the green WFs of crop i in province j corresponding to irrigated cultivation, respectively.TWFblue(i,j) equals zero in rainfed cultivation, and TWFgreen(i,j) is calculated with Eq. (10) based on RCP(i,j). The deficit or surplus over the demand of the provinces were determined by comparing CP(i,j) and CD(i,j) for each crop in each province. Equation (11) implies that CS(i,j) is the amount of crop i supplied in province j (ton), which involves the export and import of crops:$$ {CS}_{(i,j)} = {CP}_{(i,j)} – {CD}_{(i,j)} $$
    (11)
    where (i=1, 2,ldots , N;j=1, 2, ldots, J) .The internal trade network is formed once the deficit and surplus for each crop in the provinces is determined, and crops are traded based on the shortest distance between the provinces. The developed trade network would improve the country’s agricultural system and reduce transportation costs between the provinces. Each province adds to or subtracts Ti,j (ton) from its crop amounts, where imports imply an addition and exports a subtraction of crop amounts. The internal exports and imports of WFs and the net water footprints trade (NWFT) in each province are calculated as follows:$$ {WFT}_{(x,r,i)} = T_{(x,r,i)} times left( {{WF}_{green} + {WF}_{blue} } right)_{(x,i)} $$
    (12)
    $$EW{F}_{(x)}={sum }_{r,i}WF{T}_{(x,r,i)}$$
    (13)
    $$ IWF_{(r)} = sumlimits_{x,i} {WFT_{(x,r,i)} } $$
    (14)
    $$ {NWFT}_{(j)} = IWF_{(j)} – EWF_{(j)} $$
    (15)
    where (i=1, 2,ldots , N;j, x=1, 2, ldots, J, r=x-1), WFT(x,r,i) (m3), T(x,r,i) (ton), (WFgreen + WFblue)(x,i) (m3 ton−1), EWF(x) (m3), IWF(r) (m3), and NWFT(j) (m3) denote the WFs traded for crop i from exporting province x to importing province r, the amount of crop i exported from province x to province r, the blue and green WFs related to crop i in exporting province x, the WFs exported from province x by the trade of crops, the WFs imported into province r by the trade of crops, and the net water footprints trade in province j, respectively.The positive and negative values ​​of NWFT(j) represent the import and export of WFs to province j, respectively. The calculation of the internal trade between provinces with Eq. (11) permits determining the deficits and surpluses for each crop in the provinces nationally. At this juncture the provinces may resort to international trade to cope with deficits and surpluses. However, from this work’s premise of improving food security and self-sufficiency the cropping patterns of surplus crops in the provinces are modified as described in the next section.Modifying exports to optimize the cropping patternThe multi-objective optimization approach to increase food security and self-sufficiency redirects the resources to be used to cultivate export crops to the cultivation of crops that are in deficit (i.e., whose production is less than demand). This modification of cropping patterns in the provinces is based on their traditional cropping patterns. For this purpose, the internal trade network is linked to the optimization method to manage cropping patterns of the regions based on the output of the trade network, and on the goals of achieving food security and preventing water crisis. These two goals are pertinent in many countries where water scarcity is a limiting factor to achieve food security49. Therefore, concerning available agricultural water it is imperative to pay attention to the type of water (green or blue) used. Specifically, WFblue can be used in several areas of consumption; however, WFgreen is not controllable in the same manner. The usage of WFgreen by crops depends on the growing season, and the maximum use can be achieved by choosing the optimal crops. Therefore, this work treats WFgreen and WFblue as indicators of water crisis and food security, which were chosen as objective functions. In other words, controlling and managing WFs prevent its waste (thus reducing the water deficit and crisis). Selecting optimal crops based on WFs will increase production and food security. The water crisis and food security serve as the benchmark for comparison between the reference situation (without optimization) and the results of this new method. The reference situation refers to the initial state of food security and water crisis, which occurs before optimizing the cropping patterns.The food-security objective function is expressed as follows:$$F{S}_{i}=frac{{sum }_{j=1}^{J}C{P}_{(i,j)}}{{sum }_{j=1}^{J}C{D}_{(i,j)}}$$
    (16)
    The water-crisis objective function is written as follows:$${WC}_{j}=frac{sum_{i=1}^{N}{TWF}_{blue(i,j)}}{{RWR}_{j}}$$
    (17)
    where (i=1, 2,ldots, N=51;j=1, 2, ldots, J=31,) FSi, CP(i,j) (ton), CD(i,j) (ton), WCj, TWFblue(i,j) (m3), and RWRj (m3) denote the food security for crop i, production of crop i in province j, the demand of crop i in province j, the water crisis in province j, the blue WFs of crop i in province j, and the renewable water resources in province j, respectively.Maximizing the FS index and minimizing the WC index represent the ideal situation. The maximizing function was converted to a minimization function for the purpose of multiobjective optimization. The final form of the objective functions i given by the following equations:$$Min({Z}_{1})=frac{1}{N}{sum }_{i=1}^{N}(1-F{S}_{i})begin{array}{cc},& where,, F{S}_{i}end{array}=Minleft(frac{{sum }_{j=1}^{J}C{P}_{(i,j)}}{{sum }_{j=1}^{J}C{D}_{(i,j)}},1right)$$
    (18)
    $$Min({Z}_{2})=frac{1}{J}{sum }_{j=1}^{J}W{C}_{j}$$
    (19)
    where (i=1, 2,ldots , N=51;j=1, 2, ldots, J=31.) The objective function Z1 is calculated based on the food security index expressed as an average for all crops, and the objective function Z2 is calculated as the average of the water crisis indexes in the 31 provinces. Both objective functions are affected by cropping patterns and cultivation areas. The water and land used must be calculated prior to modifying the cropping patterns. The amounts of surplus crops in the provinces and their equivalent water and land are calculated using the following equations:$$ {SCP}_{(i,j)} = Max({CP}_{(i,j)} – {CD}_{(i,j)} + T_{(i,j)} ,0) $$
    (20)
    $$ {BCY}_{(i,j)} times (X_{1(i,j)} times ICA_{(i,j)} ) + {GCY}_{(i,j)} times (X_{2(i,j)} times RCA_{(i,j)} ) = {SCP}_{(i,j)} $$
    (21)
    where (i=1, 2,ldots, N;j=1, 2, ldots, J,) SCP(i,j), (X_{1(i,j)}), (X_{2(i,j)}) denote the surplus crop i in province j (ton) determined based on demand and trade in the province, and the percentage of crop i in province j that must be removed from irrigated and rainfed cultivation, respectively. The amount of water and land available for new cultivation are calculated as follows:$$ ICA_{j}^{free} = sumlimits_{i = 1}^{51} {X_{1(i,j)} times ICA_{(i,j)} } $$
    (22)
    $$ RCA_{j}^{free} = sumlimits_{i = 1}^{51} {X_{2(i,j)} times RCA_{(i,j)} } $$
    (23)
    $$ TWF_{blue,j}^{free} = sumlimits_{i = 1}^{51} {{WF}_{blue(i,j)} times BCY_{(i,j)} } times ICA_{j}^{free} $$
    (24)
    where (i=1, 2,ldots , N;j=1, 2, ldots, J,) ICAjfree and RCAjfree denote the total available area of ​​irrigated and rainfed cultivation (ha) in province j, respectively, and TWFblue,jfree represents the total amount of blue WFs available in province j (m3). It is noteworthy that the water and land available in irrigated cultivation can be altered. On the other hand, only the available land is controllable under rainfed cultivation.The objective functions of the proposed method [Eqs. (18) and (19)] were subjected to a set of constraints introduced next.

    (i)

    Modification of the cropping patterns

    The available land in each province is allocated to crops that feature a deficit in the country and are part of the traditional cropping patterns of the provinces. The set of cultivable crops is determined using the following equation:$$ P = left{ {pleft| {p in i,sum_{j = 1}^{31} {SCP_{(p,j)} < 0} } right.} right} $$ (25) where p denotes the set of crops with deficit conditions in the country and SCP(i,j) was defined above. Letting traditional irrigated and rainfed cropping patterns be denoted by Aj and Bj in province j, respectively, the set of irrigated and rainfed crops cultivable in province j was calculated as follows:$$ IC_{j} = P cap A_{j} begin{array}{*{20}c} {} & {(j = 1,2,3,ldots,31)} \ end{array} $$ (26) $$ RC_{j} = P cap B_{j} begin{array}{*{20}c} {} & {(j = 1,2,3,ldots,31)} \ end{array} $$ (27) where (j=1, 2, ldots, J), ICj and RCj denote the irrigated and rainfed crops cultivable in province j, respectively. (ii) Constraint on cultivation area A fraction of ICAjfree can be used in irrigated lands:$$ 0 le M times sumlimits_{i = 1}^{51} {{(X}_{1(i,j)} times ICA_{(i,j)} ) le ICA_{j}^{free} } begin{array}{*{20}c} , & {0 le M le 1} & {} \ end{array} $$ (28) where (j=1, 2, ldots, J), and M denotes the fraction of blue water available. (iii) Constraint on water use The amount of water used to modify the cropping pattern in the provinces is limited:$$ sumlimits_{i = 1}^{51} {TWF_{blue(i,j)}^{m} le RWR_{j} - sumlimits_{i = 1}^{51} {TWF_{blue(i,j)} + } } TWF_{blue,j}^{free} $$ (29) where (left(j=1,2,3,ldots,Jright),) TWFmblue(i,j) denotes the blue WFs used to modify the cultivation in province j, and TWFblue(i,j) represents the initial blue WFs consumed in province j to cultivate crops before changing the cropping pattern.Ideal solution and pareto optimalityThis work applied the multi-objective optimization Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The NSGA is based on the Genetic Evolutionary Algorithm and the Selection, Crossover, and Mutation operations50. The NSGA was introduced by Deb et al.51,Srinivas and Deb52, then improved to the NSGA-II51. The NSGA-II has been widely studied in water resources management53,54,55.The NSGA-II produces a Pareto front of solutions, in which, each point represents a management scenario. The decision-maker selects a scenario based on the objective functions and situational analysis. Multi-criteria decision-making methods (MCDM) can be applied to select an efficient point on the Pareto front curve56,57. This work implements the technique for order preference by similarity to ideal solution (TOPSIS) as the MCDM employed for that purpose. A description of the TOPSIS method is presented in the appendix.The NSGA-II parameters were determined based on a trial-and-error process. Multiple runs of the algorithm were used to adjust the parameters to reduce uncertainty. For this purpose, the population size and maximum iteration were set equal to 400 and 500, respectively, and the crossover and mutation rates were set equal to 0.8 and 0.1, respectively. The flowchart of the proposed approach is displayed in Fig. 1.Figure 1Flowchart of the methodology.Full size image More

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