Artificial selection and genetic engineering plus an expanding repertoire
and use of agrochemical inputs have allowed a rapid and continuous increase
in crop yield (i.e., volume production per unit area) over the last century,
which is needed to fulfill food demands from a growing human population. However, the
first signs of yield deceleration and stagnation have already been reported
for some globally important crops. Therefore, the study of the drivers of
yield growth and its variation is essential for directing research and
policies aiming at ensuring food security in the forthcoming years. We used
data on mean and variability in annual yield growth for 107 globally
important crops to assess the role of environmental (i.e., climatic region)
and plant intrinsic traits (i.e., type of harvested organ, pollinator
dependence, and life form) as drivers of change in yield growth and its
stability. We applied a comparative approach to control for biases
associated with phylogenetic non-independence among crops, an approach
rarely used in agronomic studies. Average yield growth and its variation
were not phylogenetically structured. Yield growth decreased with increasing
pollinator dependence in tree crops but not in herbaceous and shrubby crops.
Interannual yield variation tended to increase with increasing pollinator
dependence, and it was higher in crops from temperate regions, in those
cultivated for their reproductive organs, and in tree and shrubby crops as
compared with herbaceous ones. Information on ecological correlates of crop
yield growth and interannual yield variation can be used in the design of
more sustainable and diversified agriculture schemes.
Introduction
Human population is increasing steadily, imposing greater demands on
agricultural production (Godfray et al., 2010; Foley et al., 2011).
Replacement of natural habitats by croplands, advances in crop breeding
programs and genetic manipulation, a wider repertoire and more intensive use
of agrochemicals (including pesticides, fungicides, herbicides, and
fertilizers), and a widespread adoption of increasingly sophisticated
irrigation systems have all allowed a rapid growth in total crop production
and crop production per unit area (i.e., crop yield) during the last century
(Miflin, 2000). This growth in yield has played a central role in fulfilling
increasing food demands (Tilman et al., 2002; Aizen and Harder, 2009).
However, human food security depends not only on crop yield but also on its
interannual variation, which can slow down yield growth (Garibaldi et al.,
2011), affecting food prices and limiting people's access to a broad
nutritional diet, especially in developing countries (Ray et al., 2015;
Schauberger et al., 2016). Indeed, first signs of yield deceleration and
stagnation have already been reported for some globally important crops (Ray
et al., 2012; Iizumi et al., 2014). In this context, bringing a comparative
(i.e., including several species), ecological perspective into the crop
science field can be crucial to broaden our knowledge of the factors
limiting crop yield. More specifically, a phylogenetically framed approach
is essential to avoid statistical biases associated with phylogenetic
non-independence due to shared evolutionary history among crops (Revell,
2010; Symonds and Blomberg, 2014) and, thus, to accurately identify global
drivers and correlates of crop yield growth and growth stability (i.e., the
inverse of interannual yield variation). Proper identification of these
ecological drivers and correlates can be useful in designing national and
supra-national policies aiming at guarantying food security in the
forthcoming years. Yet, such an approach has been rarely applied in studies on
crop biology (Kantar et al., 2015; Milla et al., 2015; Milla et al., 2018;
Martin et al., 2019).
Plant growth depends directly on the photosynthetic rate, which is
influenced by temperature as well as by light, water, and soil nutrient
availability (Schulze et al., 2019). Therefore, all else being equal,
tropical and subtropical crops might have higher yield growth than temperate
crops because the effect of temperature on plant growth may impose an upper
limit on yield in temperate zones. In addition, more pronounced daily and
seasonal temperature fluctuations at higher latitudes, including the
occurrence of frosts (Rodrigo, 2000), might predict lower crop yield
stability. Furthermore, low solar radiation may affect soil nutrient uptake
(Schauberger et al., 2016), reducing the potential for yield growth at
higher latitudes. However, plant growing conditions may not be so benign in
the tropics. For instance, an excess of solar radiation towards lower
latitudes could be detrimental for plant development because of increasing
damage risk of the photosynthetic systems (Schauberger et al., 2016; Tan et
al., 2020). Also, tropical soils in humid areas suffer from intense rainfall
and nutrient lixiviation, often becoming nitrogen and phosphorous limited,
and thus are commonly less productive than soils in temperate regions
(Santiago, 2015; Jeffery et al., 2017). Moreover, indirect climate effects,
via higher weed growth and pest occurrence, can also hamper yield growth at
lower latitudes (Rosenzweig and Liverman, 1992). Hence, differences in yield
growth and yield variability between tropical and temperate crops, if any,
might provide first cues to the relative importance of climatic factors as
proximate and ultimate modulators of the growth rate in crop productivity.
Fruit and seed production rely on available resources (i.e., fixated carbon,
nitrogen, phosphorous, etc.) once investment in vegetative growth and
maintenance is secured (Lacey, 1986; Obeso, 2002; Weiner et al., 2009;
Tuller et al., 2018); on different pollen vectors like wind, in
wind-pollinated plants; and on a diversity of animal pollinators, in
pollinator-dependent plants (Willmer, 2011). Consequently, given the expected
higher unpredictability associated with the production of reproductive
organs, we might predict lower average yield growth and higher yield
variability in those crops cultivated for their reproductive parts (i.e.,
either seeds or fruits) than in those cultivated for their vegetative parts
(e.g., tubers, stems, leaves).
Among those crops that are cultivated for their reproductive parts, there is
also a broad variability in pollinator dependence (Klein et al., 2007; Aizen
et al., 2019), ranging from crops that are completely independent of animal
pollinators (e.g., bananas, olives, walnuts, wheat) to crops that are
completely dependent on pollinators (e.g., cocoa, kiwi, vanilla,
watermelon). Globally, ∼75 % of the crops depend, to
different degrees, on both wild and managed pollinators for seed and/or
fruit quantity and/or quality maximization (Klein et al., 2007; Garibaldi et
al., 2013; Rader et al., 2016). Because of evidence that wild and managed
pollinator populations are declining in many regions (Potts et al., 2010)
and that pollinator availability varies naturally over space and time
(Horvitz and Schemske, 1990; Price et al., 2005), crops that rely more
strongly on pollinators are expected to be more susceptible to pollinator
shortages. Accordingly, Garibaldi et al. (2011) reported that crops that
depended more heavily on pollinators had lower mean yield growth and lower
yield stability. However, phylogenetic non-independence among crops was not
accounted for, and the potential roles of more direct, confounded effects
and interactions with associated factors were not evaluated.
Relative investment in reproduction also relates to plant growth form.
Annual plants invest a large proportion of their total resource budget in
reproduction during a single season, whereas shrubs and specially trees are
long lived and make large investments to develop long-lasting supportive and
protective tissues (Petit and Hampe, 2006; Weiner et al., 2009), even before
any resources are devoted to reproduction (Harper, 1977; Kozlowski and
Uchmanski, 1987). Also, fruit and seed production in long-lived plants are
usually characterized by high interannual variation (i.e., masting) that is
often determined by the overall plant's resource availability as affected by the
environment, and possibly by intrinsic resource accumulation–depletion
cycles (Bogdziewicz et al., 2019). Thus, genetic or cultivation improvements
are probably reflected in faster and more constant yield growth in annual
crops as compared to shrubs and trees, which have longer life cycles and
more complex patterns of resource allocation (Cheplick, 2005).
Crop species sharing a common recent ancestor may also show more similar
yield growth just by descent. Indeed, significant phylogenetic signal has
been reported in traits related to photosynthesis and plant growth (Zheng et
al., 2009). Therefore, any analysis of crop yield growth and stability
involving several crops should be evaluated within a phylogenetic context.
Furthermore, because environmental and plant intrinsic traits do not act
independently, the evaluation of possible interactions between them is
fundamental. For example, as previously mentioned, lower yield stability was
reported for those crops that depend more heavily on pollinators (Garibaldi
et al., 2011). However, this pattern may also depend on the region in which
crops are being cultivated, as yield of pollinator-dependent crops may be
more stable in the tropics due to pollinators being mostly active all year (Souza et al., 2018; Rabeling et al., 2019). Also, the effect of
pollinator dependence on yield may be more important in trees than in annual
plants, as the former depend more strongly on the amount and quality of the
pollination service (Knight et al., 2005; Petit and Hampe, 2006; Brittain et
al., 2014).
Here, we conducted a comparative analysis of crop yield growth and its
variation over a 57-year time series for more than 100 globally
important crops. By applying phylogenetically explicit analyses, we assessed
the influence of climatic region (i.e., tropical vs. temperate) and plant
intrinsic factors (i.e., type of harvested organ, pollinator dependence, and
life form) on mean annual yield growth and its variability, as well as
explored possible interactions between these factors. Specifically, we
predicted higher yield growth and/or yield stability in tropical/subtropical
than temperate crops, in crops cultivated for their vegetative parts, in
short-lived crops, and in those with reduced dependence on pollinators. Our
results supported several of these expectations and revealed interesting
interactions, broadening our knowledge of the ecological factors that affect
crop yield growth and stability.
MethodsDatabase and phylogeny construction
Data on crop yield (hectograms/hectare) were obtained from the United Nations
Food and Agriculture Organization database for 113 crops and crop items
(i.e., aggregations of different species; Aizen et al., 2019; Table A1) from
1961 to 2017 (FAOSTAT, 2019). Temporal series for three crops were shorter
(cassava leaves, 1990–2017; kiwi, 1970–2017; triticale, 1975–2017). With
these data we estimated, for each crop, the mean yield growth,
Δyield, as
Δyield=∑t=1N-1[lnyieldt+1-ln(yieldt)]/N-1,
with t being a given year, N the total number of years with data (i.e., 57
years for most crops, starting from 1961), and
ln(yieldt+1)-ln(yieldt) the relative yield growth rate between year t and
year t+1. Similarly, we estimated the variability in yield growth,
SDΔyield, as
SDΔyield=∑t=1N-1lnyieldt+1-lnyieldt-Δyield2N-2,
with SDΔyield being the standard
deviation of ln(yieldt+1)-ln(yieldt). This measure of variability in annual
yield growth is basically a linearly detrended measure of interannual
variation in (log) yield (hereafter “interannual yield variation”) and
thus an inverse measure of yield stability. Also, even though Eq. (1) can be
simplified as [ln(yield2017)-ln(yield1961)]/56, this more complex expression
reveals that Eqs. (1) and (2) depict the first (position) and second (dispersal)
moments of the distribution of annual yield growth, respectively. Since both
Δyield and the SDΔyield are based on differences in log yields, they provide comparable
relative estimates of yield growth and its variation across crops, while
increasing compliance of these response variables with normal-distribution
assumptions underlying the linear tests used to analyze data. Also, these
two measures are unitless, as the difference between log yields is the log
of their ratio. In the case of mean yield growth, 100⋅(eΔyield-1) provides the mean annual percent increase in
yield.
The crop climatic region of origin (i.e., tropical or temperate) was searched
from the literature (Table A2); crops were categorized as belonging to the
two main climatic regions: “tropical-subtropical” when their region of origin never exceeded 35∘ latitude and “temperate” when
their region of origin extended above 35∘ (Jeffery et al., 2017).
In general, tropical crops are being cultivated mainly in the tropics, while
crops originating in temperate regions are being cultivated mainly in
temperate zones (Leff et al., 2004). Even though some temperate crops like
wheat are cultivated both in temperate and tropical latitudes, most of its
production is accounted for by temperate countries (e.g., China, Russia,
Ukraine, USA, Canada, Argentina). Similarly, a greater share of the
global production of subtropical/tropical staple crops like maize and rice
that are widely cultivated in temperate latitudes comes from
tropical/subtropical countries or tropical/subtropical regions within
countries (FAOSTAT, 2019). Therefore, we assume that crop climatic region of
origin reflects the climatic region of cultivation. Also, for
animal-pollinated crops, optimal yields are expected in the region of origin
(Garibaldi et al., 2013; Brown and Cunningham, 2019). Data on type of
harvested organ (i.e., vegetative or reproductive organs) and life form
(i.e., herbs, shrubs, or trees) were also obtained from the literature
(Milla, 2020). Pollinator dependence data for each crop were extracted from
Klein et al. (2007) and Aizen et al. (2019), the latter for crops not
included in the former study, where five classes were defined: “none”, for
no yield reduction in the absence of pollinators; “little”, for the range
0 %–10 % of yield reduction without pollinators; “modest”, for the range
10 %–30 % of yield reduction; “considerable” for the range 40 %–90 % of
yield reduction; and “essential”, for cases where yield reduction in the
absence of pollinators exceeded 90 %.
A synthesis-based phylogeny (i.e., a phylogeny that is obtained by
sub-setting an already available mega-phylogeny) was built to represent
phylogenetic relations among crops. This kind of phylogenies has proved
robust to detect even weak phylogenetic signals and to be useful in
community phylogenetic analyses (Li et al., 2019). The first step to obtain
the synthesis-based tree involved checking crop species names to match
accepted names in The Plant List (Table A1). An initial phylogeny including
all checked species was then obtained using the function “phylo.maker”
from the R package V.PhyloMaker, using the GBOTB.extended mega-phylogeny
(74 531 tips) as backbone, and the option “scenario 1”, which adds missing
species as basal polytomies within their genera or families (Jin and Qian,
2019). Missing species were identified by comparing the focal species list
with the tips in the mega-phylogeny. The phylogenetically closest taxa of
these missing species were searched in the literature and were used as input information when running the function “phylo.maker”. This procedure
has been shown to improve the placement of missing taxa into the resulting
phylogeny (Jin and Qian, 2019).
A phylogenetic tree with 157 tips was obtained with the methodology
described above. This tree was then modified to match FAO crop items (i.e.,
113 items) by collapsing those nodes that included species belonging to the
same FAO items. Six FAO items (i.e., “anise, badian, fennel, and
coriander”, “nutmeg, mace, and cardamoms”, “mangoes, mangosteens, and guavas”, “green chilies and peppers”, “dry chilies and peppers”, and
“millets”) included species phylogenetically dispersed, even belonging to
unrelated families (Table A1); these items were excluded from the analyses.
In some cases, FAO items comprised species phylogenetically dispersed (Table A1) but that could be represented as soft polytomies including a few items
(e.g., “Brassicaceae clade”, which included the items “rapeseed”,
“mustard seed”, “cauliflowers and broccoli”, and “cabbages”); these
cases were kept in the analyses (see Fig. A1). The clade comprising the
crops “potatoes”, “tomatoes”, and “eggplants” was resolved according to
Milla (2020).
Phylogenetic signal and phylogenetically controlled regressions
Phylogenetic signals in mean yield growth and interannual yield variation
were calculated with Blomberg's K index (Blomberg et al., 2003). This index
quantifies the amount of phylogenetic signal in the data relative to a
Brownian motion (BM) model of trait evolution, with K=0 indicating a
random distribution of the trait along the phylogeny (i.e., absence of
phylogenetic signal) and K=1 reflecting a BM pattern of trait evolution
(i.e., presence of phylogenetic signal). K estimates were obtained with the
R package phytools (Revell, 2012), and their statistical significance was assessed by
generating 10 000 randomized values of K. Furthermore, as phylogenetic signal
may vary across the phylogeny, we looked for the existence of local hotspots
of phylogenetic autocorrelation by estimating local indicators of
phylogenetic association scores (LIPA scores hereafter) for each crop in the
phylogeny (Anselin, 1995; Keck et al., 2016). Positive LIPA scores for a
given trait of interest indicate that the phylogenetic neighborhood of a
focus tip in the phylogeny is more similar for that trait than expected by
chance. LIPA scores and their statistical significance were obtained with
the R package phylosignal, on the basis of patristic phylogenetic distances and 999
randomizations (Keck et al., 2016).
Even in the absence of phylogenetic signal in the response variables,
accounting for species-shared evolutionary history is recommended in
interspecific comparisons (Felsenstein, 1985; Revell, 2010). Therefore, the
associations of yield growth and interannual yield variation with climatic
region, type of harvested organ, pollinator dependence, and life form were
evaluated by conducting phylogenetic least squares (PGLS; Paradis, 2012)
regressions, which account for phylogenetic non-independence among species.
Phylogenetic correlations among species are made explicit in these
regressions by including a residual variance–covariance matrix, which can be
constructed by assuming different evolutionary models. PGLS regressions were
ran using the ape v. 5.3 (Paradis and Schliep, 2019) and nlme v 3.1-140 (Pinheiro
et al., 2019) R packages, and the fit of the residuals to different
evolutionary models (as available in the ape R package) was compared in terms
of the Akaike information criterion (AIC). Following a step-up analysis
strategy (West et al., 2007), mean yield growth and interannual yield
variation were regressed, alternatively, against single predictors (i.e.,
climatic region, type of harvested organ, pollinator dependence, or crop
life form) and against combinations of two predictors to account for
predictor interactions and potential confounding effects (Mazer, 1989).
Higher factorial designs were not analyzed due to being too incomplete
and unbalanced. Also, the tree category was excluded from the “type of
harvested organ + life form” combination given that only reproductive
organs (i.e., fruits or seeds) are harvested from the included tree crops.
Finally, the two-predictor combination “type of harvested organ +
pollinator dependence” was not tested, as the development of the vegetative
organs that we consume does not depend on pollination, and thus these two
factors do not cross. All predictors were treated as categorical variables
in the analyses; however, the existence of a linear trend was evaluated for
the relations between pollinator dependence and yield growth and yield
stability given that pollinator dependence itself has an increasing (or
decreasing) order across categories. In these cases, the five different
categories of pollinator dependency were assigned discrete values from 0 to
4 for “none”, “little”, “modest”, “considerable”, and “essential”,
respectively. Model-estimated means and their standard errors were
calculated using the R package effects (Fox and Weisberg, 2019).
Results
Mean annual yield growth was about 0.008 (equivalent to 0.802 % yr-1) and
highly variable across crops, ranging from an average logarithmic increase
of 0.025 (2.546 % yr-1) in kiwis to a decrease in yield of -0.037 (-3.614 % yr-1) in currants (Table A3). The most stable crop in terms of yield
variation was lettuce, with an interannual variation of 0.019, followed by
sugar cane and rice (0.020 and 0.022, respectively). On the contrary, the
greatest interannual yield variation, 0.425, was observed in jojoba, which
could relate to the quite extreme arid conditions present in the regions
where it is mostly grown (Al-Obaidi et al., 2017; FAOSTAT, 2019). This crop
departed clearly from the rest (about 50 % more variable than Brazil nuts,
0.291, and pistachios, 0.282, the following most variable crops; see Fig. A2), and thus it was considered as an outlier and excluded from the analyses
of the determinants of yield growth stability. Mean annual yield growth and
interannual yield variation were negatively, albeit weakly correlated across
crops (r=-0.362, p<0.001). Thus, those crops with more variable
yields also tended to exhibit lower yield grow rates.
Differences in crop yield growth could not be explained by crop similarity
due to common ancestry, as there was no evidence of an overall phylogenetic
signal in this trait (K=1.426×10-6, p=0.331). Accordingly, only
6 % of all crops, mainly belonging to the Prunus clade, were surrounded by
phylogenetic neighborhoods with similar yield growths (i.e., significant
local positive phylogenetic autocorrelation; Fig. A3). Phylogenetically
controlled regressions showed no significant effects of climatic region or
type of harvested organ on the rate of yield growth (Table A4). Yield growth
tended to decrease with pollinator dependence (Table A4; linear trend
contrast: β=-0.017, SE = 0.008, p=0.047) and was about
64 % and 88 % lower in shrubs and trees than herbs, respectively (β=-0.008, SE = 0.003, p=0.015 for shrubs; and β=-0.011,
SE = 0.002, p<0.001 for trees). However, a significant
statistical interaction between pollinator dependence and life form (Table A4) indicated that the reduction in yield growth associated with pollinator
dependence is only evident in tree crops (Fig. 1; linear contrasts with
β=0.005, SE = 0.011, p=0.642 for herbs; β=0.028,
SE = 0.023, p=0.225 for shrubs; and β=-0.037, SE = 0.013,
p=0.006 for trees). Therefore, tree crops that are highly dependent on
pollinators showed, on average, the lowest growth in yield.
Joint effect of pollinator dependence and crop life form
on mean yield growth (Δyield). The single significant
linear trend found (in tree crops; see results) is indicated with a
regression line. Points depict model-adjusted estimated means, while dotted
lines model-adjusted standard errors. Sample sizes are shown for each
category. Note that Δyield is unitless (see Methods).
Crop similarity due to common ancestry was not reflected in yield stability,
as there was no evidence of phylogenetic signal in interannual yield
variation globally (K=6.479×10-7, p=0.651). Local phylogenetic
autocorrelation was detected but involved only ca. 10 % of all crops, with
significant LIPA positive scores detected in all members of the
[Amaryllidaceae + Asparagaceae] clade as well as in other six
phylogenetically dispersed crops (Fig. A3). Interannual yield variation was
33 % higher in temperate than tropical crops (β=0.027, SE = 0.011, p=0.020; Fig. 2a) and 65 % higher in those crops cultivated
for consumption of their reproductive organs than in crops cultivated for
their vegetative organs (β=0.039, SE = 0.015, p=0.010; Fig. 2b). These results were not confounded by the inclusion of tree crops that
are only harvested for their seeds or fruits, as a significant difference in
yield variability persisted after excluding trees and controlling for the
remaining growth forms (Table A4; Fig. A4). Interannual yield variation
tended to increase with increasing pollinator dependence (Fig. 2c; linear
contrast: β=0.105, SE = 0.048, p=0.032), despite the
overall test not being statistically significant (Table A4). Crops highly
dependent on animal pollination were, on average, about 50 % more variable
in yield that those that do not depend, or depend little, on pollinators.
Finally, interannual yield variation was 37 % higher in trees than in
herbaceous crops (β=0.030, SE = 0.013, p=0.023), with
shrubs showing values of yield variability closer to trees than to herbs
(Fig. 2d).
Effects of (a) climatic region, (b) type of harvested
organ, (c) pollinator dependence, and (d) crop life form on interannual yield
variation (SD(Δyield)). Model-adjusted
estimated means are illustrated with points and standard errors with dotted
lines. Sample sizes are shown for each category. The regression line in panel (c) illustrates the positive linear trend found for the relation between
interannual yield variation and pollinator dependence (see Results).
Significant differences among categories are indicated with different
letters (Table A4). Pollinator dependence categories are abbreviated as
“non” (none), “lit” (little), “mod” (moderate), “con” (considerable)
and “ess” (essential). Note that SD (Δyield) is unitless (see Methods).
Discussion
Increasing crop yield to fulfill the continuous rising food demands from a
growing human population, while minimizing the impact on natural ecosystems,
is one the main challenges of modern agriculture (Tilman, 1999; Tilman et
al., 2002; Foley et al., 2011; Bommarco et al., 2013). Genetic and
management innovations have boosted a steady increase in crop yield since
the middle of the last century (Evenson and Gollin, 2003; Garibaldi et al.,
2011; Hatfield and Walthall, 2015). However, the first signs of yield
deceleration and stagnation have already been reported for some globally
important crops (Ray et al., 2012; Kucharik et al., 2020). Under this
scenario, an ecological perspective can be crucial to broaden our knowledge
of the factors affecting crop yield growth. By applying a comparative
approach, here we uncovered the influence of environmental and plant
intrinsic traits on average yield growth and on its interannual variation.
Specifically, we found that yield growth decreased with increasing
pollinator dependence in tree crops but not in herbaceous and shrubby crops.
Interannual yield variation was higher in crops from temperate regions, in
those cultivated for their reproductive organs, and in tree and shrubby crops as
compared with herbaceous ones, and it tended to increase with increasing
pollinator dependence. Unexpectedly, average yield growth and its variation
were not phylogenetically structured. Therefore, our results revealed new
and intriguing patterns, which we discuss below.
There was no evidence that climatic region was related to yield growth, with
temperate crops exhibiting similar mean growth rates to tropical-subtropical
crops (0.72 % yr-1 vs. 0.83 % yr-1). Under the assumption that the climatic region
where a crop (or its ancestors) originated reflects the climatic conditions
where the crop is most commonly cultivated, higher temperature and an
overall more benign environment for plant growth in the tropics was not
reflected in significantly higher rates of yield growth. As proposed in the
Introduction, it might well be that more intense weed competition and,
perhaps, higher herbivore pressure at low latitudes counteract the positive
effect of a more benign and stable climate on crop development (Rosenzweig
and Liverman, 1992). Alternatively, crop genetic and management improvements
could have been more prevalent and intense among temperate crops (e.g.,
wheat, canola). However, this seems unlikely as several tropical-subtropical
crops have also been genetically manipulated intensively and are grown
industrially (e.g., soybean, rice, maize; Aizen et al., 2019). Despite no
apparent differences in yield growth, we did find that interannual yield
variation was lower in tropical-subtropical than temperate crops, probably
reflecting higher climatic stability at lower latitudes. Previous studies
focusing in four of the most economically important crops (i.e., wheat,
maize, soybean, and rice) have identified temperature and precipitation
variability as the main factors explaining yield variation (Ray et al.,
2015; Schauberger et al., 2016). While water availability may limit plant
growth in both zones (Kramer, 2019), temperature amplitude is greater in
temperate zones, and occasional extreme temperatures may occur more
frequently in these zones (e.g., occurrence of frosts; Rodrigo, 2000; Snyder
and de Melo-Abreu, 2005). The stability and performance of many
physiological and biochemical processes affecting plant growth – including
photosynthesis, stomatal conductance, and nutrient uptake – depend on an
optimal temperature range and can be disturbed by the occurrence of
temperatures outside this range (Schauberger et al., 2016; Slot and Winter,
2017). Given that the greater yield variability observed among temperate
crops was independent of whether the crops were harvested for their
vegetative or reproductive organs (Table A4), higher yield variation in
temperate regions may be explained by extreme temperatures affecting
biochemical processes involved in the development of both vegetative and
reproductive organs.
Yield growth seems to be independent of whether a crop is cultivated for its
vegetative or for its reproductive parts. However, crops cultivated for
their vegetative parts show more stable yields than those cultivated for
either their fruits or seeds. This lower variation may result from the fact
that investment in vegetative parts (i.e., leaves, stems, roots, tubers,
rhizomes) is quintessential to plant survivorship, and even though it can be
regulated, it cannot be postponed (Horvitz and Schemske, 2002; Obeso, 2002).
On the contrary, variable resource availability can determine high
variability in fruit and seed production among reproductive seasons in
iteroparous species (Weiner et al., 2009; Hulshof et al., 2012), and even in
annual plants, in which sexual reproduction in a given year can fail
entirely (Kho, 2000). Besides resource availability as an intrinsic source
of variability, most fruit and seed crops depend to different degrees on an
external agent, either wind or insect pollinators, for pollen transfer
between flowers (Klein et al., 2007). Particularly, restricted pollen
transfer due to pollinator scarcity can be an important factor limiting
fruit and seed set in a large fraction of wild plants and crops. In fact,
pollen limitation (i.e., the failure of achieving maximum seed set as a
consequence of inadequate pollination; Knight et al., 2005) affects more
than a half of all crops (Aizen et al., 2008). As a consequence, pollinator
availability, which is highly variable in time and space (Herrera, 1988;
Price et al., 2005), can also determine high variability in yield. Agreeing
with this hypothesis, we report a trend of increasing interannual yield
variation with increasing dependence on animal pollination. This confirms
results from previous reports (Garibaldi et al., 2011), although our
conservative analysis controlled for phylogenetic non-independence among
crops.
Pollen limitation has also been proposed to limit yield growth in addition
to decreasing yield stability. In fact, a proposed global pollinator decline
is expected to affect total agriculture production by decelerating yield
growth or even decreasing the yield of pollinator-dependent crops (Aizen et
al., 2008; Potts et al., 2010). Even though previous studies have reported
that yield growth decreased with increasing crop pollinator dependence
(Aizen et al., 2008; Garibaldi et al., 2009, 2011), these studies did not
account for potentially confounding factors and phylogenetic
non-independence. In particular, by accounting for growth form, we found
that this trend was only apparent in trees, in which high pollinator
dependence seems to hinder yield growth. In fact, several
tropical-subtropical and temperate tree crops that depend to a large extent
on pollinators to produce either fruits or seeds, or even fail to produce
fruits or seeds without pollinators, have shown negative growth rates during
the last decades (e.g., Brazil nuts, kola nuts, cashew, plums, cherries,
quinces, blueberries; Table A3), supporting the view that a global
pollinator decline is affecting crop yield. However, it is puzzling that no
trend for decreasing yield was observed among herbaceous crops, and
apparently a concave trend was found for shrubs (Fig. 1). Several factors
could explain these discrepancies. First, independent of their pollinator
dependence, there seems to be lower pollen limitation in herbaceous than
woody plants (Knight, 2005). Therefore, and perhaps associated with much
larger floral displays and higher incidence of self-incompatibility, greater
pollinator abundance is needed to maximize crop yield in trees than in
herbaceous crops (Garibaldi et al., 2020). Second, hand and other forms of
artificial pollination constitute a customary procedure in highly valuable
herbaceous and shrubby crops like vanilla and kiwi, respectively (Arditti et
al., 2009, Sáez et al., 2019), which lowers their dependence on
declining insect pollinators. Although there are some accounts of fruit
production based on hand pollination for highly pollinator dependent tree
crops (e.g., apples in China; Partap et al., 2001; Partap and Ya, 2012),
this is a highly laborious and geographically restricted procedure.
It has been proposed that reduced yield growth could be a consequence of
higher variability of a crop's input resource (i.e., pollinators) via what
is known as Jensen's inequality (Jensen, 1906; Ruel and Ayres, 1999), which
is predicted when there is a positive but saturating response of yield with
an increase in an agriculture input (Garibaldi et al., 2011). In this case,
variability in an agriculture input resource is expected to be reflected in
higher yield variation, resulting in a decrease in average yield over years.
In fact, higher variability in yield associated with larger reproductive
uncertainty has been proposed as an explanation of reduced yield in highly
pollinator dependent crops (Garibaldi et al., 2011). Although here we report
an overall negative relation between yield growth and variability, as
expected due to Jensen's inequality, the interactive effects of growth form
and pollinator dependence on yield growth still persisted after accounting
for yield variation (Table A5). Thus, reduced yield growth in highly
pollinator dependent trees can be interpreted as a direct consequence of
their dependence on this external biotic input rather than of the yield
uncertainty introduced by this dependence.
Lower pollen limitation in herbaceous than woody plants might also explain
why in general yield growth and yield stability decreased along a gradient
of crop increasing woodiness. Moreover, herbs and trees differ in the length
of their life cycles, which influences the pattern of resource allocation
between vegetative and reproductive structures. Life cycles are shorter in
herbs, and resources are invested mainly in vegetative growth and
reproduction. Trees have longer life cycles, they grow taller, and have
higher costs of maintenance as resources are needed for the development of
supportive and protective tissues, which reduces growth rates (Petit and
Hampe, 2006). Furthermore, woody species are often characterized by a
variable and synchronized reproductive behavior known as “masting”, which
may increase plant fitness by increasing either pollination success, seed
dispersal, or offspring survival through predator satiation (Pearse et al.,
2016). This reproductive strategy, although beneficial for wild plant
populations, is highly undesirable in agriculture, as it can increase
interannual yield variation and price peaks (Smith and Samach, 2013; Ray et
al., 2015). All tree crops included in this study are being cultivated for
exploitation of their fruits or seeds, and their interannual variation in
yield may reflect, in addition to higher pollination limitation, yearly
fluctuations in resources invested in reproduction.
Finally, it is interesting that neither mean yield growth nor its variation
were conditioned by crop common ancestry in the majority of the analyzed
crops. This is a meaningful finding that indicates that crop genetic and
management improvements could overcome several developmental and ecological
conditionings, besides those analyzed here, which may be phylogenetically
structured. As a consequence, phylogenetically related crops that probably
have been manipulated genetically to different extents can exhibit
contrasting yield growth rates (e.g., apples vs. quinces; Table A3).
Concluding remarks
The strength of a comparative approach lies in revealing general patterns.
This approach has been rarely applied in inter-specific studies of crop
yield, despite the need for identifying global factors limiting crop yield
growth and yield stability. This is of paramount importance in a context of
decelerating yield growth and increasing yield variability expected under
climate change (Chalinor et al., 2014, Zhao et al., 2017) and global
pollinator decline (Aizen et al., 2008; Potts et al., 2010). By analyzing
yield growth and interannual yield variation of more than 100 globally important crops, here we identified ecological factors associated
with climate, resource allocation, and pollination limitation that affect
crop yield. Despite the ultimate processes and mechanisms underlying these
patterns, the ecological correlates of yield growth and yield variation
detected here provide a useful starting point for establishing management
guidelines for crop selection. For instance, even though
pollinator-dependent tree crops have relatively high market values (Bauer
and Wing, 2016), agronomic schemes based solely on the cultivation of this
type of crops is a risky business because of their reduced yield growth and
high interannual variability, particularly in temperate regions. In any
event, the relative reduced yield growth and high yield variability of
highly pollinator dependent tree crops (i.e., those in the moderate and
essential categories) call for the need to apply a more active and
knowledge-based pollinator management than is commonly performed today.
This requires not only the management of domesticated pollinators (e.g.,
Apis mellifera), but also the enhancement of wild pollinators through infield and outfield
strategies that involve habitat management at different spatial scales
(Garibaldi et al., 2014). On the other hand, agronomic schemes based
exclusively on crops cultivated for either their vegetative parts or with
low levels of pollinator dependence could show adequate levels of yield
growth and stability, but their production is of a poorer nutritional quality
and has lower market prices than the one represented by the seeds and fruits
of many pollinator-dependent crops (Gallai et al., 2009; Eilers et al.,
2011). Overall, our results advocate for more diverse agriculture that
involves the cultivation of different crops with different ecological
features, nutritional quality, and market value at all spatial scales. In
addition to contributing to increasing food security in quantitative and
qualitative terms, this more diversified agriculture will ensure a more
sustainable agriculture and the preservation of different ecosystem services
(Aizen et al., 2019).
FAO crops/items included in the study, with their corresponding
species names and family. Listed species names are accepted names according
to The Plant List (TPL). Items indicated with an asterisk were not included
in the analyses, as they include species phylogenetically dispersed.
Bibliographical sources for the climatic region of origin of the
crops included in the study.
Crop nameClimatic region of originReferencealmondstemperateZeinalabedini et al. (2010), Khoury et al. (2016)applestemperateHarris et al. (2002)apricotstemperateDecroocq et al. (2016)areca nutstropical-subtropicalKhoury et al. (2016)asparagustemperateMilla (2020)avocadostropical-subtropicalKhoury et al. (2016)Bambara beanstropical-subtropicalMilla (2020)bananastropical-subtropicalKhoury et al. (2016)barleytemperateMilla (2020)berriestemperateMilla (2020)blueberriestemperateMilla (2020)Brazil nutstropical-subtropicalMilla (2020)broad beanstropical-subtropicalCaracuta et al. (2016)buckwheattropical-subtropicalGondola and Papp (2010)cabbagestropical-subtropicalArias et al. (2014)carrotstemperateMilla (2020)cashew nutstropical-subtropicalMilla (2020)cashew applestropical-subtropicalMilla (2020)cassavatropical-subtropicalMilla (2020)cassava leavestropical-subtropicalMilla (2020)castor oil seedstropical-subtropicalKhoury et al. (2016), van der Vossen and Mkamilo (2007)cauliflowers and broccolitropical-subtropicalArias et al. (2014)cherriestemperateMilla (2020)chestnutstemperateMilla (2020)chick peastemperateMilla (2020)chicory rootstemperateMilla (2020)cocoa beanstropical-subtropicalMilla (2020)coconutstropical-subtropicalKhoury et al. (2016), Milla (2020)coffeetropical-subtropicalKhoury et al. (2016), Milla (2020)cowpeastropical-subtropicalMilla (2020)cranberriestemperateKhoury et al. (2016), Milla (2020)cucumbers and gherkinstropical-subtropicalSebastian et al. (2010), Milla (2020)currantstemperateMilla (2020)datestropical-subtropicalMilla (2020)dry peastemperateMilla (2020)dry onionstemperateKhoury et al. (2016), Milla (2020)eggplantstropical-subtropicalMilla (2020)figstropical-subtropicalKhoury et al. (2016)garlictemperateKhoury et al. (2016), Milla (2020)gooseberriestemperateMilla (2020)grapefruitstropical-subtropicalMilla (2020)grapestemperateToffolatti et al. (2018), Milla (2020)green beanstropical-subtropicalMilla (2020)green maizetropical-subtropicalKhoury et al. (2016), Milla (2020)green onions and shallotstemperateMilla (2020)green peastemperateMilla (2020)groundnutstropical-subtropicalKhoury et al. (2016), Milla (2020)hazelnutstemperateMilla (2020)jojoba seedstemperateKumar et al. (2012)karite nutstropical-subtropicalMilla (2020)kiwitropical-subtropicalMilla (2020)kola nutstropical-subtropicalMilla (2020)leekstemperateHirschegger et al. (2010), Milla (2020)lemons and limestropical-subtropicalKhoury et al. (2016), Wu et al. (2018)lentilstemperateMilla (2020)lettucetropical-subtropicalKřístková et al. (2008)
Continued.
Crop nameClimatic region of originReferencelinseedtemperateMilla (2020)maizetropical-subtropicalKhoury et al. (2016), Milla (2020)mandarins, tangerines, clementines, and satsumastropical-subtropicalWang et al. (2018), Wu et al. (2018)melons and cantaloupestropical-subtropicalKhoury et al. (2016), Milla (2020)melonseedtropical-subtropicalKhoury et al. (2016), Milla (2020)mustard seedtemperateMilla (2020)oatstemperateKhoury et al. (2016) Milla (2020)okratropical-subtropicalMilla (2020)olivestemperateMilla (2020)orangestropical-subtropicalWu et al. (2018)palm fruit oiltropical-subtropicalMilla (2020)papayastropical-subtropicalKhoury et al. (2016), Milla (2020)peaches and nectarinestemperateFaust and Timon (1995)pearstemperateMilla (2020)peppertropical-subtropicalMilla (2020)persimmonstemperateGuo et al. (2006), Soriano et al. (2006)pigeon peastropical-subtropicalMilla (2020)pineapplestropical-subtropicalKhoury et al. (2016), Milla (2020)pistachiostemperateMilla (2020)plums and sloestemperateMilla (2020)poppy seedstemperateTeteni (1995)potatoestropical-subtropicalKhoury et al. (2016), Milla (2020)pumpkins, squash, and gourdstropical-subtropicalSanjur et al. (2002), Khoury et al. (2016), Milla (2020)quincestemperateMilla (2020)quinoatropical-subtropicalMilla (2020)rapeseedtemperateMilla (2020)ricetropical-subtropicalMilla (2020)ryetemperateMilla (2020)safflower seedstropical-subtropicalMilla (2020)seed cottontropical-subtropicalMilla (2020)sesame seedstropical-subtropicalBedigian et al. (1985), Milla (2020)sorghumtropical-subtropicalMilla (2020)sour cherriestemperateMilla (2020)soybeanstropical-subtropicalGuo et al. (2010), Khoury et al. (2016), Milla (2020)spinachtemperateMilla (2020)strawberriestemperateKhoury et al. (2016), Milla (2020)string beanstropical-subtropicalMilla (2020)sugar beettemperateMilla (2020)sugar canetropical-subtropicalKhoury et al. (2016), Milla (2020)sunflower seedstemperateKhoury et al. (2016), Milla (2020)sweet potatoestropical-subtropicalKhoury et al. (2016), Milla (2020)tarotropical-subtropicalKhoury et al. (2016), Milla (2020)tomatoestropical-subtropicalKhoury et al. (2016), Milla (2020)triticaletemperatevanillatropical-subtropicalKhoury et al. (2016), Milla (2020)walnutstemperateMilla (2020)watermelonstropical-subtropicalChomicki and Renner (2015), Milla (2020)wheattemperateMilla (2020)yamstropical-subtropicalMilla (2020), APG IV.yautiatropical-subtropicalMilla (2020)
Mean yield growth (Δyield), interannual yield variation
(SD(Δyield)), and mean annual percent increase in yield (% Δyield) for the FAO crops/items included in the study. Data were obtained
from the FAOSTAT database and covered a 57-year period (from 1961 to
2017) for all but three crops (see Methods for details).
Crop nameΔyieldSD(Δyield)% Δyieldlemons and limes0.003919870.059232420.39275624lentils0.013955210.077200881.40530396lettuce0.00649080.01884230.65119091linseed0.01639610.102166151.65312496maize0.019395430.067733641.95847432mandarins, tangerines, clementines, and satsumas0.008137990.098460820.8171193melons and cantaloupes0.015238150.027648631.53548469melonseed-0.003680310.16040998-0.3673541mustard seed0.009680230.19053560.97272304oats0.012051590.065660671.21244981okra0.002979390.093564810.29838309olives-0.008703670.20172565-0.86658988oranges0.007224090.052509870.72502428palm fruit oil0.024526540.044707932.4829791papayas0.016715540.052055541.68560251peaches and nectarines0.009611110.068592120.96574497pears0.009177360.094774550.92196006pepper0.017131920.106350091.72795082persimmons-0.007384060.0861261-0.73568611pigeon peas0.003050910.15118450.30555716pineapples0.015663510.049525551.57868253pistachios0.016156740.282300551.62879686plums and sloes-0.022219580.13529796-2.19745443poppy seeds0.004575410.139601830.4585889potatoes0.008902410.056645440.89421546pumpkins, squash, and gourds0.019343290.049593081.95315795quinces-0.002818920.09398141-0.28149458quinoa0.005651920.191933310.56679232rapeseed0.023985230.075855272.42751895rice0.016087590.022050441.62176955rye0.017336970.081866811.74881273safflower and seeds0.011487340.129019111.15535747seed cotton0.017160080.051910931.73081567sesame seeds0.011803640.066903491.18735767sorghum0.008302870.073522810.8337432sour cherries-0.005318480.1607349-0.53043584soybeans0.016566520.065229051.67045047spinach0.0217340.145311352.19719053strawberries0.019056680.062636651.92394179string beans0.010743310.041433961.08012242sugar beet0.017431040.061272321.75838492sugar cane0.006138990.020321830.61578703sunflower seeds0.010137510.079569271.01890654sweet potatoes0.00914350.066153560.91854323taro0.0000320.078812590.00322584tomatoes0.014779030.025464811.48887824triticale0.008911080.168856130.89508998vanilla-0.000236240.22315504-0.02362095walnuts0.002220870.123239140.22233346watermelons0.023512960.044400712.37915679wheat0.021008390.052679962.1230618yams0.002940950.091746460.29452742yautia0.014909230.092418041.50209298
Analysis of variance of PGLS fitted models for (a) mean yield
growth (Δyield) and (b) interannual yield variation (SD(Δyield)), with climatic region, type of harvested organ, pollinator
dependence, and crop life form as sources of variation. Marginal sums of
squares (type III) were estimated for models with more than one source of
variation by applying the R function “anova” (with the option
“type = marginal”) on the PGLS fitted objects. Interaction terms were only
kept in the models when their inclusion increased model fit in terms of AIC
(ΔAIC ≥2); all interactions between predictors not informed
were statistically non-significant (p>0.05). Significant tests
(p<0.05) are indicated in bold type.
(a)Source of variationDFnumDFdenFpClimatic region11040.2400.625Harvested organ11050.3230.570Pollinator dependence41022.9630.023Life form210213.448<0.001Climatic region + harvested organ Climatic region11030.2870.593Harvested organ11030.3770.540Climatic region + pollinator dependence Climatic region11000.2240.637Pollinator dependence41002.7950.030Climatic region + life form Climatic region11000.5110.476Life form210013.250<0.001Harvested organ + life form* Harvested organ1680.6650.418Life form1688.0660.006Pollinator dependence + life form Pollinator dependence4900.5880.672Life form2900.9080.407Pollinator dependence ⋅ life form8902.8730.007
Continued.
(b)Source of variationDFnumDFdenFpClimatic region11035.6110.020Harvested organ11046.9080.010Pollinator dependence41011.4040.238Life form21013.0350.052Climatic region + harvested organ Climatic region11026.6050.012Harvested organ11028.0660.005Climatic region + pollinator dependence Climatic region1995.8410.017Pollinator dependence4991.4160.234Climatic region + life form Climatic region1995.8940.017Life form2993.1170.049Harvested organ + life form* Harvested organ1675.6490.020Life form1672.1970.143Pollinator dependence + life form Pollinator dependence4971.0340.394Life form2972.1570.121
* Tree crops were not included in the analysis.
Analysis of variance of a PGLS fitted model constructed to assess
whether the slowdown in mean yield growth found in tree crops could be
explained by Jensen's inequality effect. Interannual yield variation was
used as a proxy reflecting the effect of variable pollen delivery (i.e., a
resource needed for seeds or fruit production) on yield growth.
Source of variationDFnumDFdenFpPollinator dependence4880.7850.538Life form2880.0790.924Interannual yield variation18814.703<0.001Pollinator dependence ⋅ life form8883.2520.003
Phylogenetic relationships among all FAO items included
in the study. Data on climatic region, harvested organ, pollinator
dependence, and crop life form are indicated for each tip. Unfilled circles
depict tip data that have been excluded from the analyses, as they
corresponded to FAO items comprising species that differ in a given trait.
Boxplots showing the distribution of the interannual
yield variation against climatic region, type of harvested organ, pollinator
dependence, and crop life form. Boxes delimit the lower and upper quartiles;
the black horizontal line within the boxes shows the median, the white
circle the mean, the vertical lines the whiskers, and the points outside the
boxes the outlying points. The red outlying points correspond to the FAO
item “jojoba seeds”.
Local indicators of phylogenetic association (LIPA)
scores for (a)Δyield and (b) SD(Δyield),
depicted with a bar for each crop. Significant values (p<0.05) are
indicated with colored bars.
Effect of type of harvested organ on interannual
yield variation, SD(Δyield), for
herbaceous and shrubby crops. Model-adjusted estimated means are illustrated
with points and standard errors with dotted lines. Sample sizes are shown
for each category. Significant differences are indicated with different
letters (see Table A4).
Data availability
The data used in this study can be obtained from sources cited in the main text and also from Appendix A.
Author contributions
GG and MAA conceived the study and designed the methodology; GG analyzed the
data; GG and MAA wrote the original manuscript; AS, GG, NLdC, and MAA
contributed substantially to the final manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank Rubén Milla for providing very helpful comments on a
previous version of this paper and acknowledge the support of the
SURPASS2 project funded under the Newton Fund Latin America Biodiversity
Programme: Biodiversity – Ecosystem services for sustainable
development grants awarded by the Natural Environment Research Council of
Great Britain (NERC; grant no. NE/S011870/1), the National Scientific and Technical
Research Council of Argentina (CONICET; grant no. RD 1984/19), and the National Fund
for Scientific and Technological Research of Argentina (FONCYT; grant nos. PICT
2015-2333, PICT 2018-2145, and PICT-2018-03559).
Review statement
This paper was edited by Daniel Montesinos and reviewed by Rubén Milla and one anonymous referee.
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