ARTICLE   Open Access    

Mapping tree species distribution in support of China's integrated tree-livestock-crop system

More Information
  • The primary challenge of the contemporary world is to meet accelerating requirements for food. Limited land, competition between crop and livestock farming and climate change are major challenges. Agroforestry offer a form of sustainable agriculture through the direct provision of food by raising farmers’ incomes and through various ecosystem services. The first essential step in adopting agroforestry is the selection of appropriate tree species that fit local climates. In this paper, we mapped 20 fodder trees and important crops in China using the multi-model ensemble and Ecocrop modelling approach. Relying on the intersectional concept of set theory, the fuzzy logic technique was applied to identify regions where candidate trees could be grown with appropriate crops and livestock. The resulting models provide important insights into the climatic suitability of trees and crops and offer knowledge critical to the proper integration of trees with crops and livestock at specific locations. The results offer support for developing appropriate strategies regarding potential land-use within agroforestry systems in order to maximize ecosystem services and the benefits of sustainable agriculture. Model outputs could easily convert into conventional maps with clearly defined boundaries for site-specific planning for tree-crop-livestock integration. The next step for actualizing an integrated system is to investigate specifically what these different species may contribute to the existing farming systems, quantify the benefits and estimate any possible trade-offs.
  • As a major staple crop, today maize accounts for approximately 40% of total worldwide cereal production (http://faostat.fao.org/). Since its domestication ~9,000 years ago from a subgroup of teosinte (Zea mays ssp. parviglumis) in the tropical lowlands of southwest Mexico[1], its cultivating area has greatly expanded, covering most of the world[2]. Human's breeding and utilization of maize have gone through several stages, from landraces, open-pollinated varieties (OPVs), double-cross hybrids (1930s-1950s) and since the middle 1950s, single-cross hybrids. Nowadays, global maize production is mostly provided by single-cross hybrids, which exhibit higher-yielding and better stress tolerance than OPVs and double-cross hybrids[3].

    Besides its agronomic importance, maize has also been used as a model plant species for genetic studies due to its out-crossing habit, large quantities of seeds produced and the availability of diverse germplasm. The abundant mutants of maize facilitated the development of the first genetic and cytogenetic maps of plants, and made it an ideal plant species to identify regulators of developmental processes[46]. Although initially lagging behind other model plant species (such as Arabidopsis and rice) in multi-omics research, the recent rapid development in sequencing and transformation technologies, and various new tools (such as CRISPR technologies, double haploids etc.) are repositioning maize research at the frontiers of plant research, and surely, it will continue to reveal fundamental insights into plant biology, as well as to accelerate molecular breeding for this vitally important crop[7, 8].

    During domestication from teosinte to maize, a number of distinguishing morphological and physiological changes occurred, including increased apical dominance, reduced glumes, suppression of ear prolificacy, increase in kernel row number, loss of seed shattering, nutritional changes etc.[9] (Fig. 1). At the genomic level, genome-wide genetic diversity was reduced due to a population bottleneck effect, accompanied by directional selection at specific genomic regions underlying agronomically important traits. Over a century ago, Beadle initially proposed that four or five genes or blocks of genes might be responsible for much of the phenotypic changes between maize and teosinte[10,11]. Later studies by Doebley et al. used teosinte–maize F2 populations to dissect several quantitative trait loci (QTL) to the responsible genes (such as tb1 and tga1)[12,13]. On the other hand, based on analysis of single-nucleotide polymorphisms (SNPs) in 774 genes, Wright et al.[14] estimated that 2%−4% of maize genes (~800−1,700 genes genome-wide) were selected during maize domestication and subsequent improvement. Taking advantage of the next-generation sequencing (NGS) technologies, Hufford et al.[15] conducted resequencing analysis of a set of wild relatives, landraces and improved maize varieties, and identified ~500 selective genomic regions during maize domestication. In a recent study, Xu et al.[16] conducted a genome-wide survey of 982 maize inbred lines and 190 teosinte accession. They identified 394 domestication sweeps and 360 adaptation sweeps. Collectively, these studies suggest that maize domestication likely involved hundreds of genomic regions. Nevertheless, much fewer domestication genes have been functionally studied so far.

    Figure 1.  Main traits of maize involved in domestication and improvement.

    During maize domestication, a most profound morphological change is an increase in apical dominance, transforming a multi-branched plant architecture in teosinte to a single stalked plant (terminated by a tassel) in maize. The tillers and long branches of teosinte are terminated by tassels and bear many small ears. Similarly, the single maize stalk bears few ears and is terminated by a tassel[9,12,17]. A series of landmark studies by Doebley et al. elegantly demonstrated that tb1, which encodes a TCP transcription factor, is responsible for this transformation[18, 19]. Later studies showed that insertion of a Hopscotch transposon located ~60 kb upstream of tb1 enhances the expression of tb1 in maize, thereby repressing branch outgrowth[20, 21]. Through ChIP-seq and RNA-seq analyses, Dong et al.[22] demonstrated that tb1 acts to regulate multiple phytohormone signaling pathways (gibberellins, abscisic acid and jasmonic acid) and sugar sensing. Moreover, several other domestication loci, including teosinte glume architecture1 (tga1), prol1.1/grassy tillers1, were identified as its putative targets. Elucidating the precise regulatory mechanisms of these loci and signaling pathways will be an interesting and rewarding area of future research. Also worth noting, studies showed that tb1 and its homologous genes in Arabidopsis (Branched1 or BRC1) and rice (FINE CULM1 or FC1) play a conserved role in repressing the outgrowth of axillary branches in both dicotyledon and monocotyledon plants[23, 24].

    Teosinte ears possess two ranks of fruitcase-enclosed kernels, while maize produces hundreds of naked kernels on the ear[13]. tga1, which encodes a squamosa-promoter binding protein (SBP) transcription factor, underlies this transformation[25]. It has been shown that a de novo mutation occurred during maize domestication, causing a single amino acid substitution (Lys to Asn) in the TGA1 protein, altering its binding activity to its target genes, including a group of MADS-box genes that regulate glume identity[26].

    Prolificacy, the number of ears per plants, is also a domestication trait. It has been shown that grassy tillers 1 (gt1), which encodes an HD-ZIP I transcription factor, suppresses prolificacy by promoting lateral bud dormancy and suppressing elongation of the later ear branches[27]. The expression of gt1 is induced by shading and requires the activity of tb1, suggesting that gt1 acts downstream of tb1 to mediate the suppressed branching activity in response to shade. Later studies mapped a large effect QTL for prolificacy (prol1.1) to a 2.7 kb 'causative region' upstream of the gt1gene[28]. In addition, a recent study identified a new QTL, qEN7 (for ear number on chromosome 7). Zm00001d020683, which encodes a putative INDETERMINATE DOMAIN (IDD) transcription factor, was identified as the likely candidate gene based on its expression pattern and signature of selection during maize improvement[29]. However, its functionality and regulatory relationship with tb1 and gt1 remain to be elucidated.

    Smaller leaf angle and thus more compact plant architecture is a desired trait for modern maize varieties. Tian et al.[30] used a maize-teosinte BC2S3 population and cloned two QTLs (Upright Plant Architecture1 and 2 [UPA1 and UPA2]) that regulate leaf angle. Interestingly, the authors showed that the functional variant of UPA2 is a 2-bp InDel located 9.5 kb upstream of ZmRAVL1, which encodes a B3 domain transcription factor. The 2-bp Indel flanks the binding site of the transcription factor Drooping Leaf1 (DRL1)[31], which represses ZmRAVL1 expression through interacting with Liguleless1 (LG1), a SBP-box transcription factor essential for leaf ligule and auricle development[32]. UPA1 encodes brassinosteroid C-6 oxidase1 (brd1), a key enzyme for biosynthesis of active brassinolide (BR). The teosinte-derived allele of UPA2 binds DRL1 more strongly, leading to lower expression of ZmRAVL1 and thus, lower expression of brd1 and BR levels, and ultimately smaller leaf angle. Notably, the authors demonstrated that the teosinte-derived allele of UPA2 confers enhanced yields under high planting densities when introgressed into modern maize varieties[30, 33].

    Maize plants exhibit salient vegetative phase change, which marks the vegetative transition from the juvenile stage to the adult stage, characterized by several changes in maize leaves produced before and after the transition, such as production of leaf epicuticular wax and epidermal hairs. Previous studies reported that Glossy15 (Gl15), which encodes an AP2-like transcription factor, promotes juvenile leaf identity and suppressing adult leaf identity. Ectopic overexpression of Gl15 causes delayed vegetative phase change and flowering, while loss-of-function gl15 mutant displayed earlier vegetative phase change[34]. In another study, Gl15 was identified as a major QTL (qVT9-1) controlling the difference in the vegetative transition between maize and teosinte. Further, it was shown that a pre-existing low-frequency standing variation, SNP2154-G, was selected during domestication and likely represents the causal variation underlying differential expression of Gl15, and thus the difference in the vegetative transition between maize and teosinte[35].

    A number of studies documented evidence that tassels replace upper ears1 (tru1) is a key regulator of the conversion of the male terminal lateral inflorescence (tassel) in teosinte to a female terminal inflorescence (ear) in maize. tru1 encodes a BTB/POZ ankyrin repeat domain protein, and it is directly targeted by tb1, suggesting their close regulatory relationship[36]. In addition, a number of regulators of maize inflorescence morphology, were also shown as selective targets during maize domestication, including ramosa1 (ra1)[37, 38], which encodes a putative transcription factor repressing inflorescence (the ear and tassel) branching, Zea Agamous-like1 (zagl1)[39], which encodes a MADS-box transcription factor regulating flowering time and ear size, Zea floricaula leafy2 (zfl2, homologue of Arabidopsis Leafy)[40, 41], which likely regulates ear rank number, and barren inflorescence2 (bif2, ortholog of the Arabidopsis serine/threonine kinase PINOID)[42, 43], which regulates the formation of spikelet pair meristems and branch meristems on the tassel. The detailed regulatory networks of these key regulators of maize inflorescence still remain to be further elucidated.

    Kernel row number (KRN) and kernel weight are two important determinants of maize yield. A number of domestication genes modulating KRN and kernel weight have been identified and cloned, including KRN1, KRN2, KRN4 and qHKW1. KRN4 was mapped to a 3-kb regulatory region located ~60 kb downstream of Unbranched3 (UB3), which encodes a SBP transcription factor and negatively regulates KRN through imparting on multiple hormone signaling pathways (cytokinin, auxin and CLV-WUS)[44, 45]. Studies have also shown that a harbinger TE in the intergenic region and a SNP (S35) in the third exon of UB3 act in an additive fashion to regulate the expression level of UB3 and thus KRN[46].

    KRN1 encodes an AP2 transcription factor that pleiotropically affects plant height, spike density and grain size of maize[47], and is allelic to ids1/Ts6 (indeterminate spikelet 1/Tassel seed 6)[48]. Noteworthy, KRN1 is homologous to the wheat domestication gene Q, a major regulator of spike/spikelet morphology and grain threshability in wheat[49].

    KRN2 encodes a WD40 domain protein and it negatively regulates kernel row number[50]. Selection in a ~700-bp upstream region (containing the 5’UTR) of KRN2 during domestication resulted in reduced expression and thus increased kernel row number. Interestingly, its orthologous gene in rice, OsKRN2, was shown also a selected gene during rice domestication to negatively regulate secondary panicle branches and thus grain number. These observations suggest convergent selection of yield-related genes occurred during parallel domestication of cereal crops.

    qHKW1 is a major QTL for hundred-kernel weight (HKW)[51]. It encodes a CLAVATA1 (CLV1)/BARELY ANY MERISTEM (BAM)-related receptor kinase-like protein positively regulating HKW. A 8.9 Kb insertion in its promoter region was find to enhance its expression, leading to enhanced HKW[52]. In addition, Chen et al.[53] reported cloning of a major QTL for kernel morphology, qKM4.08, which encodes ZmVPS29, a retromer complex component. Sequencing and association analysis revealed that ZmVPS29 was a selective target during maize domestication. They authors also identified two significant polymorphic sites in its promoter region significantly associated with the kernel morphology. Moreover, a strong selective signature was detected in ZmSWEET4c during maize domestication. ZmSWEET4c encodes a hexose transporter protein functioning in sugar transport across the basal endosperm transfer cell layer (BETL) during seed filling[54]. The favorable alleles of these genes could serve as valuable targets for genetic improvement of maize yield.

    In a recent effort to more systematically analyze teosinte alleles that could contribute to yield potential of maize, Wang et al.[55] constructed four backcrossed maize-teosinte recombinant inbred line (RIL) populations and conducted detailed phenotyping of 26 agronomic traits under five environmental conditions. They identified 71 QTL associated with 24 plant architecture and yield related traits through inclusive composite interval mapping. Interestingly, they identified Zm00001eb352570 and Zm00001eb352580, both encode ethylene-responsive transcription factors, as two key candidate genes regulating ear height and the ratio of ear to plant height. Chen et al.[56] constructed a teosinte nested association mapping (TeoNAM) population, and performed joint-linkage mapping and GWAS analyses of 22 domestication and agronomic traits. They identified the maize homologue of PROSTRATE GROWTH1, a rice domestication gene controlling the switch from prostrate to erect growth, is also a QTL associated with tillering in teosinte and maize. Additionally, they also detected multiple QTL for days-to-anthesis (such as ZCN8 and ZmMADS69) and other traits (such as tassel branch number and tillering) that could be exploited for maize improvement. These lines of work highlight again the value of mining the vast amounts of superior alleles hidden in teosinte for future maize genetic improvement.

    Loss of seed shattering was also a key trait of maize domestication, like in other cereals. shattering1 (sh1), which encodes a zinc finger and YABBY domain protein regulating seed shattering. Interesting, sh1 was demonstrated to undergo parallel domestication in several cereals, including rice, maize, sorghum, and foxtail millet[57]. Later studies showed that the foxtail millet sh1 gene represses lignin biosynthesis in the abscission layer, and that an 855-bp Harbinger transposable element insertion in sh1 causes loss of seed shattering in foxtail millet[58].

    In addition to morphological traits, a number of physiological and nutritional related traits have also been selected during maize domestication. Based on survey of the nucleotide diversity, Whitt et al.[59] reported that six genes involved in starch metabolism (ae1, bt2, sh1, sh2, su1 and wx1) are selective targets during maize domestication. Palaisa et al.[60] reported selection of the Y1 gene (encoding a phytoene synthase) for increased nutritional value. Karn et al.[61] identified two, three, and six QTLs for starch, protein and oil respectively and showed that teosinte alleles can be exploited for the improvement of kernel composition traits in modern maize germplasm. Fan et at.[62] reported a strong selection imposed on waxy (wx) in the Chinese waxy maize population. Moreover, a recent exciting study reported the identification of a teosinte-derived allele of teosinte high protein 9 (Thp9) conferring increased protein level and nitrogen utilization efficiency (NUE). It was further shown that Thp9 encodes an asparagine synthetase 4 and that incorrect splicing of Thp9-B73 transcripts in temperate maize varieties is responsible for its diminished expression, and thus reduced NUE and protein content[63].

    Teosintes is known to confer superior disease resistance and adaptation to extreme environments (such as low phosphorus and high salinity). de Lange et al. and Lennon et al.[6466] reported the identification of teosinte-derived QTLs for resistance to gray leaf spot and southern leaf blight in maize. Mano & Omori reported that teosinte-derived QTLs could confer flooding tolerance[67]. Feng et al.[68] identified four teosinte-derived QTL that could improve resistance to Fusarium ear rot (FER) caused by Fusarium verticillioides. Recently, Wang et al.[69] reported a MYB transcription repressor of teosinte origin (ZmMM1) that confers resistance to northern leaf blight (NLB), southern corn rust (SCR) and gray leaf spot (GLS) in maize, while Zhang et al.[70] reported the identification of an elite allele of SNP947-G ZmHKT1 (encoding a sodium transporter) derived from teosinte can effectively improve salt tolerance via exporting Na+ from the above-ground plant parts. Gao et al.[71] reported that ZmSRO1d-R can regulate the balance between crop yield and drought resistance by increasing the guard cells' ROS level, and it underwent selection during maize domestication and breeding. These studies argue for the need of putting more efforts to tapping into the genetic resources hidden in the maize’s wild relatives. The so far cloned genes involved in maize domestication are summarized in Table 1. Notably, the enrichment of transcription factors in the cloned domestication genes highlights a crucial role of transcriptional re-wiring in maize domestication.

    Table 1.  Key domestication genes cloned in maize.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    tb1Plant architectureTCP transcription factorIncreased expression~60 kb upstream of tb1 enhancing expression[1822]
    tga1Hardened fruitcaseSBP-domain transcription factorProtein functionA SNP in exon (K-N)[25, 26]
    gt1Plant architectureHomeodomain leucine zipperIncreased expressionprol1.1 in 2.7 kb upstream of the promoter region increasing expression[27, 28]
    Zm00001d020683Plant architectureINDETERMINATE DOMAIN transcription factorProtein functionUnknown[29]
    UPA1Leaf angleBrassinosteroid C-6 oxidase1Protein functionUnknown[30]
    UPA2Leaf angleB3 domain transcription factorIncreased expressionA 2 bp indel in 9.5 kb upstream of ZmRALV1[30]
    Gl15Vegetative phase changeAP2-like transcription factorAltered expressionSNP2154: a stop codon (G-A)[34, 35]
    tru1Plant architectureBTB/POZ ankyrin repeat proteinIncreased expressionUnknown[36]
    ra1Inflorescence architectureTranscription factorAltered expressionUnknown[37, 38]
    zflPlant architectureTranscription factorAltered expressionUnknown[40, 41]
    UB3Kernel row numberSBP-box transcription factorAltered expressionA TE in the intergenic region;[4446]
    SNP (S35): third exon of UB3
    (A-G) increasing expression of UB3 and KRN
    KRN1/ids1/Ts6Kernel row numberAP2 Transcription factorIncreased expressionUnknown[47, 48]
    KRN2Kernel row numberWD40 domainDecreased expressionUnknown[50]
    qHKW1Kernel row weightCLV1/BAM-related receptor kinase-like proteinIncreased expression8.9 kb insertion upstream of HKW[51, 52]
    ZmVPS29Kernel morphologyA retromer complex componentProtein functionTwo SNPs (S-1830 and S-1558) in the promoter of ZmVPS29[53]
    ZmSWEET4cSeed fillingHexose transporterProtein functionUnknown[54]
    ZmSh1ShatteringA zinc finger and YABBY transcription factorProtein functionUnknown[57, 58]
    Thp9Nutrition qualityAsparagine synthetase 4 enzymeProtein functionA deletion in 10th intron of Thp9 reducing NUE and protein content[63]
    ZmMM1Biotic stressMYB Transcription repressorProtein functionUnknown[69]
    ZmHKT1Abiotic stressA sodium transporterProtein functionSNP947-G: a nonsynonymous variation increasing salt tolerance[70]
    ZmSRO1d-RDrought resistance and productionPolyADP-ribose polymerase and C-terminal RST domainProtein functionThree non-synonymous variants: SNP131 (A44G), SNP134 (V45A) and InDel433[71]
     | Show Table
    DownLoad: CSV

    After its domestication from its wild progenitor teosinte in southwestern Mexico in the tropics, maize has now become the mostly cultivated crop worldwide owing to its extensive range expansion and adaptation to diverse environmental conditions (such as temperature and day length). A key prerequisite for the spread of maize from tropical to temperate regions is reduced photoperiod sensitivity[72]. It was recently shown that CENTRORADIALIS 8 (ZCN8), an Flowering Locus T (FT) homologue, underlies a major quantitative trait locus (qDTA8) for flowering time[73]. Interestingly, it has been shown that step-wise cis-regulatory changes occurred in ZCN8 during maize domestication and post-domestication expansion. SNP-1245 is a target of selection during early maize domestication for latitudinal adaptation, and after its fixation, selection of InDel-2339 (most likely introgressed from Zea mays ssp. Mexicana) likely contributed to the spread of maize from tropical to temperate regions[74].

    ZCN8 interacts with the basic leucine zipper transcription factor DLF1 (Delayed flowering 1) to form the florigen activation complex (FAC) in maize. Interestingly, DFL1 was found to underlie qLB7-1, a flowering time QTL identified in a BC2S3 population of maize-teosinte. Moreover, it was shown that DLF1 directly activates ZmMADS4 and ZmMADS67 in the shoot apex to promote floral transition[75]. In addition, ZmMADS69 underlies the flowering time QTL qDTA3-2 and encodes a MADS-box transcription factor. It acts to inhibit the expression of ZmRap2.7, thereby relieving its repression on ZCN8 expression and causing earlier flowering. Population genetic analyses showed that DLF1, ZmMADS67 and ZmMADS69 are all targets of artificial selection and likely contributed to the spread of maize from the tropics to temperate zones[75, 76].

    In addition, a few genes regulating the photoperiod pathway and contributing to the acclimation of maize to higher latitudes in North America have been cloned, including Vgt1, ZmCCT (also named ZmCCT10), ZmCCT9 and ZmELF3.1. Vgt1 was shown to act as a cis-regulatory element of ZmRap2.7, and a MITE TE located ~70 kb upstream of Vgt1 was found to be significantly associated with flowering time and was a major target for selection during the expansion of maize to the temperate and high-latitude regions[7779]. ZmCCT is another major flowering-time QTL and it encodes a CCT-domain protein homologous to rice Ghd7[80]. Its causal variation is a 5122-bp CACTA-like TE inserted ~2.5 kb upstream of ZmCCT10[72, 81]. ZmCCT9 was identified a QTL for days to anthesis (qDTA9). A Harbinger-like TE located ~57 kb upstream of ZmCCT9 showed the most significant association with DTA and thus believed to be the causal variation[82]. Notably, the CATCA-like TE of ZmCCT10 and the Harbinger-like TE of ZmCCT9 are not observed in surveyed teosinte accessions, hinting that they are de novo mutations occurred after the initial domestication of maize[72, 82]. ZmELF3.1 was shown to underlie the flowering time QTL qFT3_218. It was demonstrated that ZmELF3.1 and its homolog ZmELF3.2 can form the maize Evening Complex (EC) through physically interacting with ZmELF4.1/ZmELF4.2, and ZmLUX1/ZmLUX2. Knockout mutants of Zmelf3.1 and Zmelf3.1/3.2 double mutant presented delayed flowering under both long-day and short-day conditions. It was further shown that the maize EC promote flowering through repressing the expression of several known flowering suppressor genes (e.g., ZmCCT9, ZmCCT10, ZmCOL3, ZmPRR37a and ZmPRR73), and consequently alleviating their inhibition on several maize florigen genes (ZCN8, ZCN7 and ZCN12). Insertion of two closely linked retrotransposon elements upstream of the ZmELF3.1 coding region increases the expression of ZmELF3.1, thus promoting flowering[83]. The increase frequencies of the causal TEs in Vgt1, ZmCCT10, ZmCCT9 and ZmELF3.1 in temperate maize compared to tropical maize highlight a critical role of these genes during the spread and adaptation of maize to higher latitudinal temperate regions through promoting flowering under long-day conditions[72,8183].

    In addition, Barnes et al.[84] recently showed that the High Phosphatidyl Choline 1 (HPC1) gene, which encodes a phospholipase A1 enzyme, contributed to the spread of the initially domesticated maize from the warm Mexican southwest to the highlands of Mexico and South America by modulating phosphatidylcholine levels. The Mexicana-derived allele harbors a polymorphism and impaired protein function, leading to accelerated flowering and better fitness in highlands.

    Besides the above characterized QTLs and genes, additional genetic elements likely also contributed to the pre-Columbia spreading of maize. Hufford et al.[85] proposed that incorporation of mexicana alleles into maize may helped the expansion of maize to the highlands of central Mexico based on detection of bi-directional gene flow between maize and Mexicana. This proposal was supported by a recent study showing evidence of introgression for over 10% of the maize genome from the mexicana genome[86]. Consistently, Calfee et al.[87] found that sequences of mexicana ancestry increases in high-elevation maize populations, supporting the notion that introgression from mexicana facilitating adaptation of maize to the highland environment. Moreover, a recent study examined the genome-wide genetic diversity of the Zea genus and showed that dozens of flowering-related genes (such as GI, BAS1 and PRR7) are associated with high-latitude adaptation[88]. These studies together demonstrate unequivocally that introgression of genes from Mexicana and selection of genes in the photoperiod pathway contributed to the spread of maize to the temperate regions.

    The so far cloned genes involved in pre-Columbia spread of maize are summarized in Fig. 2 and Table 2.

    Figure 2.  Genes involved in Pre-Columbia spread of maize to higher latitudes and the temperate regions. The production of world maize in 2020 is presented by the green bar in the map from Ritchie et al. (2023). Ritchie H, Rosado P, and Roser M. 2023. "Agricultural Production". Published online at OurWorldInData.org. Retrieved from: 'https:ourowrldindata.org/agricultural-production' [online Resource].
    Table 2.  Flowering time related genes contributing to Pre-Columbia spread of maize.
    GeneFunctional annotationCausative changeReferences
    ZCN8Florigen proteinSNP-1245 and Indel-2339 in promoter[73, 74]
    DLF1Basic leucine zipper transcription factorUnknown[75]
    ZmMADS69MADS-box transcription factorUnknown[76]
    ZmRap2.7AP2-like transcription factorMITE TE inserted ~70 kb upstream[7779]
    ZmCCTCCT-domain protein5122-bp CACTA-like TE inserted ~2.5 kb upstream[72,81]
    ZmCCT9CCT transcription factorA harbinger-like element at 57 kb upstream[82]
    ZmELF3.1Unknownwo retrotransposons in the promote[84]
    HPC1Phospholipase A1 enzymUnknown[83]
    ZmPRR7UnknownUnknown[88]
    ZmCOL9CO-like-transcription factorUnknown[88]
     | Show Table
    DownLoad: CSV

    Subsequent to domestication ~9,000 years ago, maize has been continuously subject to human selection during the post-domestication breeding process. Through re-sequencing analysis of 35 improved maize lines, 23 traditional landraces and 17 wild relatives, Hufford et al.[15] identified 484 and 695 selective sweeps during maize domestication and improvement, respectively. Moreover, they found that about a quarter (23%) of domestication sweeps (107) were also selected during improvement, indicating that a substantial portion of the domestication loci underwent continuous selection during post-domestication breeding.

    Genetic improvement of maize culminated in the development of high planting density tolerant hybrid maize to increase grain yield per unit land area[89, 90]. To investigate the key morphological traits that have been selected during modern maize breeding, we recently conducted sequencing and phenotypic analyses of 350 elite maize inbred lines widely used in the US and China over the past few decades. We identified four convergently improved morphological traits related to adapting to increased planting density, i.e., reduced leaf angle, reduced tassel branch number (TBN), reduced relative plant height (EH/PH) and accelerated flowering. Genome-wide Association Study (GWAS) identified a total of 166 loci associated with the four selected traits, and found evidence of convergent increases in allele frequency at putatively favorable alleles for the identified loci. Moreover, genome scan using the cross-population composite likelihood ratio approach (XP-CLR) identified a total of 1,888 selective sweeps during modern maize breeding in the US and China. Gene ontology analysis of the 5,356 genes encompassed in the selective sweeps revealed enrichment of genes related to biosynthesis or signaling processes of auxin and other phytohormones, and in responses to light, biotic and abiotic stresses. This study provides a valuable resource for mining genes regulating morphological and physiological traits underlying adaptation to high-density planting[91].

    In another study, Li et al.[92] identified ZmPGP1 (ABCB1 or Br2) as a selected target gene during maize domestication and genetic improvement. ZmPGP1 is involved in auxin polar transport, and has been shown to have a pleiotropic effect on plant height, stalk diameter, leaf length, leaf angle, root development and yield. Sequence and phenotypic analyses of ZmPGP1 identified SNP1473 as the most significant variant for kernel length and ear grain weight and that the SNP1473T allele is selected during both the domestication and improvement processes. Moreover, the authors identified a rare allele of ZmPGP1 carrying a 241-bp deletion in the last exon, which results in significantly reduced plant height and ear height and increased stalk diameter and erected leaves, yet no negative effect on yield[93], highlighting a potential utility in breeding high-density tolerant maize cultivars.

    Shade avoidance syndrome (SAS) is a set of adaptive responses triggered when plants sense a reduction in the red to far-red light (R:FR) ratio under high planting density conditions, commonly manifested by increased plant height (and thus more prone to lodging), suppressed branching, accelerated flowering and reduced resistance to pathogens and pests[94, 95]. High-density planting could also cause extended anthesis-silking interval (ASI), reduced tassel size and smaller ear, and even barrenness[96, 97]. Thus, breeding of maize cultivars of attenuated SAS is a priority for adaptation to increased planting density.

    Extensive studies have been performed in Arabidopsis to dissect the regulatory mechanism of SAS and this topic has been recently extensively reviewed[98]. We recently showed that a major signaling mechanism regulating SAS in Arabidopsis is the phytochrome-PIFs module regulates the miR156-SPL module-mediated aging pathway[99]. We proposed that in maize there might be a similar phytochrome-PIFs-miR156-SPL regulatory pathway regulating SAS and that the maize SPL genes could be exploited as valuable targets for genetic improvement of plant architecture tailored for high-density planting[100].

    In support of this, it has been shown that the ZmphyBs (ZmphyB1 and ZmphyB2), ZmphyCs (ZmphyC1 and ZmphyC2) and ZmPIFs are involved in regulating SAS in maize[101103]. In addition, earlier studies have shown that as direct targets of miR156s, three homologous SPL transcription factors, UB2, UB3 and TSH4, regulate multiple agronomic traits including vegetative tillering, plant height, tassel branch number and kernel row number[44, 104]. Moreover, it has been shown that ZmphyBs[101, 105] and ZmPIF3.1[91], ZmPIF4.1[102] and TSH4[91] are selective targets during modern maize breeding (Table 3).

    Table 3.  Selective genes underpinning genetic improvement during modern maize breeding.
    GenePhenotypeFunctional annotationSelection typeCausative changeReferences
    ZmPIF3.1Plant heightBasic helix-loop-helix transcription factorIncreased expressionUnknown[91]
    TSH4Tassel branch numberTranscription factorAltered expressionUnknown[91]
    ZmPGP1Plant architectureATP binding cassette transporterAltered expressionA 241 bp deletion in the last exon of ZmPGP1[92, 93]
    PhyB2Light signalPhytochrome BAltered expressionA 10 bp deletion in the translation start site[101]
    ZmPIF4.1Light signalBasic helix-loop-helix transcription factorAltered expressionUnknown[102]
    ZmKOB1Grain yieldGlycotransferase-like proteinProtein functionUnknown[121]
     | Show Table
    DownLoad: CSV

    In a recent study to dissect the signaling process regulating inflorescence development in response to the shade signal, Kong et al.[106] compared the gene expression changes along the male and female inflorescence development under simulated shade treatments and normal light conditions, and identified a large set of genes that are co-regulated by developmental progression and simulated shade treatments. They found that these co-regulated genes are enriched in plant hormone signaling pathways and transcription factors. By network analyses, they found that UB2, UB3 and TSH4 act as a central regulatory node controlling maize inflorescence development in response to shade signal, and their loss-of-function mutants exhibit reduced sensitivity to simulated shade treatments. This study provides a valuable genetic source for mining and manipulating key shading-responsive genes for improved tassel and ear traits under high density planting conditions.

    Nowadays, global maize production is mostly provided by hybrid maize, which exhibits heterosis (or hybrid vigor) in yields and stress tolerance over open-pollinated varieties[3]. Hybrid maize breeding has gone through several stages, from the 'inbred-hybrid method' stage by Shull[107] and East[108] in the early twentieth century, to the 'double-cross hybrids' stage (1930s−1950s) by Jones[109], and then the 'single-cross hybrids' stage since the 1960s. Since its development, single-cross hybrid was quickly adopted globally due to its superior heterosis and easiness of production[3].

    Single-cross maize hybrids are produced from crossing two unrelated parental inbred lines (female × male) belonging to genetically distinct pools of germplasm, called heterotic groups. Heterotic groups allow better exploitation of heterosis, since inter-group hybrids display a higher level of heterosis than intra-group hybrids. A specific pair of female and male heterotic groups expressing pronounced heterosis is termed as a heterotic pattern[110, 111]. Initially, the parental lines were derived from a limited number of key founder inbred lines and empirically classified into different heterotic groups (such as SSS and NSS)[112]. Over time, they have expanded dramatically, accompanied by formation of new 'heterotic groups' (such as Iodent, PA and PB). Nowadays, Stiff Stalk Synthetics (SSS) and PA are generally used as FHGs (female heterotic groups), while Non Stiff Stalk (NSS), PB and Sipingtou (SPT) are generally used as the MHGs (male heterotic groups) in temperate hybrid maize breeding[113].

    With the development of molecular biology, various molecular markers, ranging from RFLPs, SSRs, and more recently high-density genome-wide SNP data have been utilized to assign newly developed inbred lines into various heterotic groups, and to guide crosses between heterotic pools to produce the most productive hybrids[114116]. Multiple studies with molecular markers have suggested that heterotic groups have diverged genetically over time for better heterosis[117120]. However, there has been a lack of a systematic assessment of the effect and contribution of breeding selection on phenotypic improvement and the underlying genomic changes of FHGs and MHGs for different heterotic patterns on a population scale during modern hybrid maize breeding.

    To systematically assess the phenotypic improvement and the underlying genomic changes of FHGs and MHGs during modern hybrid maize breeding, we recently conducted re-sequencing and phenotypic analyses of 21 agronomic traits for a panel of 1,604 modern elite maize lines[121]. Several interesting observations were made: (1) The MHGs experienced more intensive selection than the FMGs during the progression from era I (before the year 2000) to era II (after the year 2000). Significant changes were observed for 18 out of 21 traits in the MHGs, but only 10 of the 21 traits showed significant changes in the FHGs; (2) The MHGs and FHGs experienced both convergent and divergent selection towards different sets of agronomic traits. Both the MHGs and FHGs experienced a decrease in flowering time and an increase in yield and plant architecture related traits, but three traits potentially related to seed dehydration rate were selected in opposite direction in the MHGs and FHGs. GWAS analysis identified 4,329 genes associated with the 21 traits. Consistent with the observed convergent and divergent changes of different traits, we observed convergent increase for the frequencies of favorable alleles for the convergently selected traits in both the MHGs and FHGs, and anti-directional changes for the frequencies of favorable alleles for the oppositely selected traits. These observations highlight a critical contribution of accumulation of favorable alleles to agronomic trait improvement of the parental lines of both FHGs and MHGs during modern maize breeding.

    Moreover, FST statistics showed increased genetic differentiation between the respective MHGs and FHGs of the US_SS × US_NSS and PA × SPT heterotic patterns from era I to era II. Further, we detected significant positive correlations between the number of accumulated heterozygous superior alleles of the differentiated genes with increased grain yield per plant and better parent heterosis, supporting a role of the differentiated genes in promoting maize heterosis. Further, mutational and overexpressional studies demonstrated a role of ZmKOB1, which encodes a putative glycotransferase, in promoting grain yield[121]. While this study complemented earlier studies on maize domestication and variation maps in maize, a pitfall of this study is that variation is limited to SNP polymorphisms. Further exploitation of more variants (Indels, PAVs, CNVs etc.) in the historical maize panel will greatly deepen our understanding of the impact of artificial selection on the maize genome, and identify valuable new targets for genetic improvement of maize.

    The ever-increasing worldwide population and anticipated climate deterioration pose a great challenge to global food security and call for more effective and precise breeding methods for crops. To accommodate the projected population increase in the next 30 years, it is estimated that cereal production needs to increase at least 70% by 2050 (FAO). As a staple cereal crop, breeding of maize cultivars that are not only high-yielding and with superior quality, but also resilient to environmental stresses, is essential to meet this demand. The recent advances in genome sequencing, genotyping and phenotyping technologies, generation of multi-omics data (including genomic, phenomic, epigenomic, transcriptomic, proteomic, and metabolomic data), creation of novel superior alleles by genome editing, development of more efficient double haploid technologies, integrating with machine learning and artificial intelligence are ushering the transition of maize breeding from the Breeding 3.0 stage (biological breeding) into the Breeding 4.0 stage (intelligent breeding)[122, 123]. However, several major challenges remain to be effectively tackled before such a transition could be implemented. First, most agronomic traits of maize are controlled by numerous small-effect QTL and complex genotype-environment interactions (G × E). Thus, elucidating the contribution of the abundant genetic variation in the maize population to phenotypic plasticity remains a major challenge in the post-genomic era of maize genetics and breeding. Secondly, most maize cultivars cultivated nowadays are hybrids that exhibit superior heterosis than their parental lines. Hybrid maize breeding involves the development of elite inbred lines with high general combining ability (GCA) and specific combining ability (SCA) that allows maximal exploitation of heterosis. Despite much effort to dissect the mechanisms of maize heterosis, the molecular basis of maize heterosis is still a debated topic[124126]. Thirdly, only limited maize germplasm is amenable to genetic manipulation (genetic transformation, genome editing etc.), which significantly hinders the efficiency of genetic improvement. Development of efficient genotype-independent transformation procedure will greatly boost maize functional genomic research and breeding. Noteworthy, the Smart Corn System recently launched by Bayer is promised to revolutionize global corn production in the coming years. At the heart of the new system is short stature hybrid corn (~30%−40% shorter than traditional hybrids), which offers several advantages: sturdier stems and exceptional lodging resistance under higher planting densities (grow 20%−30% more plants per hectare), higher and more stable yield production per unit land area, easier management and application of plant protection products, better use of solar energy, water and other natural resources, and improved greenhouse gas footprint[127]. Indeed, a new age of maize green revolution is yet to come!

    This work was supported by grants from the Key Research and Development Program of Guangdong Province (2022B0202060005), National Natural Science Foundation of China (32130077) and Hainan Yazhou Bay Seed Lab (B21HJ8101). We thank Professors Hai Wang (China Agricultural University) and Jinshun Zhong (South China Agricultural University) for valuable comments and helpful discussion on the manuscript. We apologize to authors whose excellent work could not be cited due to space limitations.

  • The authors declare that they have no conflict of interest. Haiyang Wang is an Editorial Board member of Seed Biology who was blinded from reviewing or making decisions on the manuscript. The article was subject to the journal's standard procedures, with peer-review handled independently of this Editorial Board member and his research groups.

  • ESM 1 List of literature cited to understand the characteristic of selected fodder trees mentioned in Table 1.
    ESM 2 Presence absence map generated based on a threshold (see Table 2) defined by maximizing the sum of the true presence and true absence rates (maxTPR+TNR) for 20 fodder tree species as estimated by the ensemble modelling. ‘A’ to ‘T’ are tree species codes for the fodder tree species as listed in Table 2.
    ESM 3 Fuzzy map produced for different livestock to show suitable regions for integration with tree potential distribution and cropland. ‘A’ to ‘T’ are tree species codes for the fodder tree species as listed in Table 2.
    ESM 4 Description of Bioclim variables and the contributing variables used in their calculation.
    ESM 5 Results of VIF analysis for selection of least correlate bioclimatic variables.
  • [1]

    UN. 2017. The impact of population momentum on future population growth. Population Facts No. 2017/4. United Nations Department of Economic and Social Affairs Population Division. pp. 2. Available from: https://www.un.org/

    [2]

    Alexandratos N, Bruinsma J. 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working Papers 12-03, Food and Agriculture Organization of the United Nations, Rome. pp. 147 https://doi.org/10.22004/ag.econ.288998

    [3]

    O’Mara FP. 2012. The role of grasslands in food security and climate change. Ann. Bot. 110:1263−70

    doi: 10.1093/aob/mcs209

    CrossRef   Google Scholar

    [4]

    Godfray HCJ, Garnett T. 2014. Food security and sustainable intensification. Philos. Trans. R. Soc. B. 369:20120273

    doi: 10.1098/rstb.2012.0273

    CrossRef   Google Scholar

    [5]

    Thornton PK, Herrero M. 2015. Adapting to climate change in the mixed crop and livestock farming systems in sub-Saharan Africa. Nat. Clim. Chang. 5:830−6

    doi: 10.1038/nclimate2754

    CrossRef   Google Scholar

    [6]

    Herrero M, Thornton PK, Notenbaert AM, Wood S, Msangi S, et al. 2010. Smart investments in sustainable food production: Revisiting mixed crop-livestock systems. Science 327:822−5

    doi: 10.1126/science.1183725

    CrossRef   Google Scholar

    [7]

    Herrero M, Havlík P, Valin H, Notenbaert AM, Rufino M, et al. 2013. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl. Acad. Sci. 110:20888−93

    doi: 10.1073/pnas.1308149110

    CrossRef   Google Scholar

    [8]

    Hou FJ, Nan ZB, Xie YZ, Li XL, Lin HL, et al. 2008. Integrated crop-livestock production systems in China. Rangel. J. 30:221−31

    doi: 10.1071/RJ08018

    CrossRef   Google Scholar

    [9]

    Kremen C, Miles A. 2012. Ecosystem Services in Biologically Diversified versus Conventional Farming Systems: Benefits, Externalitites, and Trade-Offs. Ecol. Soc. 17:40

    doi: 10.5751/ES-05035-170440

    CrossRef   Google Scholar

    [10]

    Bryan E, Ringler C, Okoba B, Koo J, Herrero M, et al. 2013. Can agriculture support climate change adaptation, greenhouse gas mitigation and rural livelihoods? insights from Kenya Clim. Change 118:151−65

    doi: 10.1007/s10584-012-0640-0

    CrossRef   Google Scholar

    [11]

    Palm C., Blanco-Canqui H., DeClerck F, Gatere L, Grace P. 2014. Conservation agriculture and ecosystem services: An overview. Agric. Ecosyst. Environ. 187:87−105

    doi: 10.1016/j.agee.2013.10.010

    CrossRef   Google Scholar

    [12]

    Lacombe G, Bolliger AM, Harrisson RD, To Thi Thu Ha. 2016. Integrated tree, crop and livestock technologies to conserve soil and water, and sustain smallholder farmers’ livelihoods in Southeast Asian uplands. In Integrated Systems Research for Sustainable Smallholder Agriculture in the Central Mekong: Achievements and challenges of implementing integrated systems research, ed. L Hiwasaki, A Bolliger, G Lacombe, J Raneri, M Schut, et al., Chapter 3. Hanoi, Viet Nam: World Agroforestry Centre. pp. 41−64 https://hdl.handle.net/10568/78362

    [13]

    Ranjitkar S, Bu D, Van Wijk M, Ma Y, Ma L, et al. 2020. Will heat stress take its toll on milk production in China? Clim. Change 161:637−52

    doi: 10.1007/s10584-020-02688-4

    CrossRef   Google Scholar

    [14]

    Djanibekov U, Villamor GB, Dzhakypbekova K, Chamberlain J, Xu J. 2016. Adoption of sustainable land uses in post-soviet central Asia: The case for agroforestry. Sustain. 8:1030

    doi: 10.3390/su8101030

    CrossRef   Google Scholar

    [15]

    Rudel T, Kwon O-J, Paul B, Boval M, Rao I, et al. 2016. Do Smallholder, Mixed Crop-Livestock Livelihoods Encourage Sustainable Agricultural Practices? A Meta-Analysis. Land 5:6

    doi: 10.3390/land5010006

    CrossRef   Google Scholar

    [16]

    Toop TA, Ward S, Oldfield T, Hull M, Kirby ME, et al. 2017. AgroCycle – Developing a circular economy in agriculture. Energy Procedia 123:76−80

    doi: 10.1016/j.egypro.2017.07.269

    CrossRef   Google Scholar

    [17]

    Luedeling E, Kindt R, Huth NI, Koenig K. 2014. Agroforestry systems in a changing climate – challenges in projecting future performance. Curr. Opin. Environ. Sustain. 6:1−7

    doi: 10.1016/j.cosust.2013.07.013

    CrossRef   Google Scholar

    [18]

    Marris E. 2009. Planting the forest of the future. Nature 459:906−8

    doi: 10.1038/459906a

    CrossRef   Google Scholar

    [19]

    Gray LK, Hamann A. 2013. Tracking suitable habitat for tree populations under climate change in western North America. Clim. Change 117:289−303

    doi: 10.1007/s10584-012-0548-8

    CrossRef   Google Scholar

    [20]

    Elith J, Leathwick JR. 2009. Species Distribution Models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40:677−97

    doi: 10.1146/annurev.ecolsys.110308.120159

    CrossRef   Google Scholar

    [21]

    Berhanu B, Seleshi Y, Demisse SS, Melesse AM. 2016. Bias correction and characterization of climate forecast system re-analysis daily precipitation in Ethiopia using fuzzy overlay. Meteorol. Appl. 23:230−43

    doi: 10.1002/met.1549

    CrossRef   Google Scholar

    [22]

    Ranjitkar S, Sujakhu NM, Lu Y, Wang Q, Wang M, et al. 2016. Climate modelling for agroforestry species selection in Yunnan Province, China. Environ. Model. Softw. 75:263−72

    doi: 10.1016/j.envsoft.2015.10.027

    CrossRef   Google Scholar

    [23]

    Hattab T, Ben Rais Lasram F, Albouy C, Sammari C, Romdhane MS, et al. 2013. The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning. PLoS ONE 8(10):e76430

    doi: 10.1371/journal.pone.0076430

    CrossRef   Google Scholar

    [24]

    Barbosa AM, Real R. 2012. Applying fuzzy logic to comparative distribution modelling: A case study with two sympatric amphibians. Sci. World J. 2012:428206

    doi: 10.1100/2012/428206

    CrossRef   Google Scholar

    [25]

    Santos NR, Katz JVE, Moyle PB, Viers JH. 2014. A programmable information system for management and analysis of aquatic species range data in California. Environ. Model. Softw. 53:13−26

    doi: 10.1016/j.envsoft.2013.10.024

    CrossRef   Google Scholar

    [26]

    Haile KK, Tirivayi N, Tesfaye W. 2019. Farmers’ willingness to accept payments for ecosystem services on agricultural land: The case of climate-smart agroforestry in Ethiopia. Ecosyst. Serv. 39:100964

    doi: 10.1016/j.ecoser.2019.100964

    CrossRef   Google Scholar

    [27]

    Evans JM, Fletcher RJ, Alavalapati J. 2010. Using species distribution models to identify suitable areas for biofuel feedstock production. GCB Bioenergy 2:63−78

    doi: 10.1111/j.1757-1707.2010.01040.x

    CrossRef   Google Scholar

    [28]

    Kim H, Hyun SW, Hoogenboom G, Porter CH, Kim KS. 2018. Fuzzy union to assess climate suitability of annual ryegrass (Lolium multiflorum), alfalfa (Medicago sativa) and sorghum (Sorghum bicolor). Sci. Rep. 8:10220

    doi: 10.1038/s41598-018-28291-3

    CrossRef   Google Scholar

    [29]

    Qiu F, Chastain B, Zhou Y, Zhang C, Sridharan H. 2014. Modeling land suitability/capability using fuzzy evaluation. GeoJournal 79:167−82

    doi: 10.1007/s10708-013-9503-0

    CrossRef   Google Scholar

    [30]

    Sujakhu NM, Ranjitkar S, Niraula RR, Pokharel BK, Schmidt-Vogt D, et al. 2016. Farmers' perceptions of and adaptations to changing climate in the Melamchi Valley of Nepal. Mt. Res. Dev. 36:15−30

    doi: 10.1659/MRD-JOURNAL-D-15-00032.1

    CrossRef   Google Scholar

    [31]

    de Roest K, Ferrari P, Knickel K. 2018. Specialisation and economies of scale or diversification and economies of scope? Assessing different agricultural development pathways J. Rural Stud. 59:222−31

    doi: 10.1016/j.jrurstud.2017.04.013

    CrossRef   Google Scholar

    [32]

    Bos J. 2002. Comparing specialised and mixed farming systems in the clay areas of the Netherlands under future policy scenarios: an optimisation approach. Thesis. Wageningen University, The Netherlands.

    [33]

    Garnett T. 2009. Livestock-related greenhouse gas emissions: impacts and options for policy makers. Environ. Sci. Policy 12:491−503

    doi: 10.1016/j.envsci.2009.01.006

    CrossRef   Google Scholar

    [34]

    Tang K, Hailu A, Kragt ME, Ma C. 2018. The response of broadacre mixed crop-livestock farmers to agricultural greenhouse gas abatement incentives. Agric. Syst. 160:11−20

    doi: 10.1016/j.agsy.2017.11.001

    CrossRef   Google Scholar

    [35]

    Marton SMRR, Zimmermann A, Kreuzer M, Gaillard G. 2016. Comparing the environmental performance of mixed and specialised dairy farms: The role of the system level analysed. J. Clean. Prod. 124:73−83

    doi: 10.1016/j.jclepro.2016.02.074

    CrossRef   Google Scholar

    [36]

    Sanou J, Bayala J, Teklehaimanot Z, Bazié P. 2012. Effect of shading by baobab (Adansonia digitata) and néré (Parkia biglobosa) on yields of millet (Pennisetum glaucum) and taro (Colocasia esculenta) in parkland systems in Burkina Faso, West Africa. Agrofor. Syst. 85:431−41

    doi: 10.1007/s10457-011-9405-4

    CrossRef   Google Scholar

    [37]

    Bayala J, Balesdent J, Marol C, Zapata F, Teklehaimanot Z, et al. 2006. Relative contribution of trees and crops to soil carbon content in a parkland system in Burkina Faso using variations in natural 13C abundance. Nutr. Cycl. Agroecosys. 76:193−201

    Google Scholar

    [38]

    Schiere H, Kater L. 2001. Mixed Crop Livestock Farming: A Review of Traditional Technologies based on Literature and Field Experiences. Report, In FAO Animal production and health paper 152, FAO, Rome, Italy

    [39]

    Kahane R, Hodgkin T, Jaenicke H, Hoogendoorn C, Hermann M, et al. 2013. Agrobiodiversity for food security, health and income. Agron. Sustain. Dev. 33:671−93

    doi: 10.1007/s13593-013-0147-8

    CrossRef   Google Scholar

    [40]

    Franzel S, Carsan S, Lukuyu B, Sinja J, Wambugu C. 2014. Fodder trees for improving livestock productivity and smallholder livelihoods in Africa. Curr. Opin. Environ. Sustain. 6:98−103

    doi: 10.1016/j.cosust.2013.11.008

    CrossRef   Google Scholar

    [41]

    Saito K, Linquist B, Keobualapha B, Shiraiwa T, Horie T. 2009. Broussonetia papyrifera (paper mulberry): Its growth, yield and potential as a fallow crop in slash-and-burn upland rice system of northern Laos. Agrofor. Syst. 76:525−32

    doi: 10.1007/s10457-009-9206-1

    CrossRef   Google Scholar

    [42]

    Amatya SM, Cedamon E, Nuberg I. 2018. Agroforestry systems and practices in Nepal. Rampur, Nepal: Faculty of forestry, Agriculture and Forestry University (AFU). 108pp. Download from: https://www.iufro.org/download/file/29095/1317/Agroforestry_Systems_and_Practices__in_Nepal__2018__pdf/

    [43]

    Ma Y-h, Fu S-l, Zhang X-p, Zhao K, Chen HYH. 2017. Intercropping improves soil nutrient availability, soil enzyme activity and tea quantity and quality. Appl. Soil Ecol. 119:171−8

    doi: 10.1016/j.apsoil.2017.06.028

    CrossRef   Google Scholar

    [44]

    Zeng DH, Mao R, Chang SX, Li LJ, Yang D. 2010. Carbon mineralization of tree leaf litter and crop residues from poplar-based agroforestry systems in Northeast China: A laboratory study. Appl. Soil Ecol. 44:133−7

    doi: 10.1016/j.apsoil.2009.11.002

    CrossRef   Google Scholar

    [45]

    Quinn LD, Gordon DR, Glaser A, Lieurance D, Flory SL. 2015. Bioenergy Feedstocks at Low Risk for Invasion in the USA: a “White List” Approach. Bioenergy Res. 8:471−81

    doi: 10.1007/s12155-014-9503-z

    CrossRef   Google Scholar

    [46]

    Gordon AM, Newman SM, Coleman B. (Eds.). 2018. Temperate Agroforestry Systems. Second. Oxfordshire, UK: CABI. pp. 325

    [47]

    Rahman SA, Sunderland T, Kshatriya M, Roshetko JM, Pagella T, et al. 2016. Towards productive landscapes: Trade-offs in tree-cover and income across a matrix of smallholder agricultural land-use systems. Land Use Policy 58:152−64

    doi: 10.1016/j.landusepol.2016.07.003

    CrossRef   Google Scholar

    [48]

    Luedeling E, Smethurst PJ, Baudron F, Bayala J, Huth NI, et al. 2016. Field-scale modeling of tree-crop interactions: Challenges and development needs. Agric. Syst. 142:51−69

    doi: 10.1016/j.agsy.2015.11.005

    CrossRef   Google Scholar

    [49]

    Basavaraju T, Gururaja Pao MR. 2000. Tree-crop interactions in agroforestry systems: a brief review. Indian For. 126:51−69 http://www.indianforester.co.in/index.php/indianforester/article/view/3308

    [50]

    Murgueitio E, Calle Z, Uribe F, Calle A, Solorio B. 2011. Native trees and shrubs for the productive rehabilitation of tropical cattle ranching lands. For. Ecol. Manage. 261:1654−63

    doi: 10.1016/j.foreco.2010.09.027

    CrossRef   Google Scholar

    [51]

    Broom DM, Galindo FA, Murgueitio E. 2013. Sustainable, efficient livestock production with high biodiversity and good welfare for animals. Proc. Biol. Sci. 280:20132025

    doi: 10.1098/rspb.2013.2025

    CrossRef   Google Scholar

    [52]

    Broxton PD, Zeng X, Sulla-Menashe D, Troch PA. 2014. A Global Land Cover Climatology Using MODIS Data. J. Appl. Meteorol. Climatol. 53:1593−605

    doi: 10.1175/JAMC-D-13-0270.1

    CrossRef   Google Scholar

    [53]

    Fang J, Wang Z, Tang Z. 2011. Atlas of Woody Plants in China. Beijing and Springer-Verlag Berlin Heidelberg: Higher Education Press

    [54]

    Robinson TP, William Wint GR, Conchedda G, Van Boeckel TP, Ercoli V, et al. 2014. Mapping the global distribution of livestock. PLoS ONE 9:e96084

    doi: 10.1371/journal.pone.0096084

    CrossRef   Google Scholar

    [55]

    Kriticos DJ, Webber BL, Leriche A, Ota N, Macadam I, et al. 2012. CliMond: Global high-resolution historical and future scenario climate surfaces for bioclimatic modelling. Methods Ecol. Evol. 3:53−64

    doi: 10.1111/j.2041-210X.2011.00134.x

    CrossRef   Google Scholar

    [56]

    Hijmans RJ. 2012. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology 93:679−88

    doi: 10.1890/11-0826.1

    CrossRef   Google Scholar

    [57]

    Ranjitkar S, Kindt R, Sujakhu NM, Hart R, Guo W, et al. 2014. Separation of the bioclimatic spaces of Himalayan tree rhododendron species predicted by ensemble suitability models. Glob. Ecol. Conserv. 1:2−12

    doi: 10.1016/j.gecco.2014.07.001

    CrossRef   Google Scholar

    [58]

    Kindt R. 2018. Ensemble species distribution modelling with transformed suitability values. Environ. Model. Softw. 100:136−45

    doi: 10.1016/j.envsoft.2017.11.009

    CrossRef   Google Scholar

    [59]

    Rogerson PA. 2001. Statistical methods for geography. pp. 320. London: Sage Publications.

    [60]

    Ramirez-Villegas J, Jarvis A, Läderach P. 2013. Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agric. For. Meteorol. 170:67−78

    doi: 10.1016/j.agrformet.2011.09.005

    CrossRef   Google Scholar

  • Cite this article

    Ranjitkar S, Bu D, Sujakhu NM, Marius G, Robinson TP, et al. 2021. Mapping tree species distribution in support of China's integrated tree-livestock-crop system. Circular Agricultural Systems 1: 2 doi: 10.48130/CAS-2021-0002
    Ranjitkar S, Bu D, Sujakhu NM, Marius G, Robinson TP, et al. 2021. Mapping tree species distribution in support of China's integrated tree-livestock-crop system. Circular Agricultural Systems 1: 2 doi: 10.48130/CAS-2021-0002

Figures(5)  /  Tables(3)

Article Metrics

Article views(12981) PDF downloads(730)

ARTICLE   Open Access    

Mapping tree species distribution in support of China's integrated tree-livestock-crop system

Circular Agricultural Systems  1 Article number: 2  (2021)  |  Cite this article

Abstract: The primary challenge of the contemporary world is to meet accelerating requirements for food. Limited land, competition between crop and livestock farming and climate change are major challenges. Agroforestry offer a form of sustainable agriculture through the direct provision of food by raising farmers’ incomes and through various ecosystem services. The first essential step in adopting agroforestry is the selection of appropriate tree species that fit local climates. In this paper, we mapped 20 fodder trees and important crops in China using the multi-model ensemble and Ecocrop modelling approach. Relying on the intersectional concept of set theory, the fuzzy logic technique was applied to identify regions where candidate trees could be grown with appropriate crops and livestock. The resulting models provide important insights into the climatic suitability of trees and crops and offer knowledge critical to the proper integration of trees with crops and livestock at specific locations. The results offer support for developing appropriate strategies regarding potential land-use within agroforestry systems in order to maximize ecosystem services and the benefits of sustainable agriculture. Model outputs could easily convert into conventional maps with clearly defined boundaries for site-specific planning for tree-crop-livestock integration. The next step for actualizing an integrated system is to investigate specifically what these different species may contribute to the existing farming systems, quantify the benefits and estimate any possible trade-offs.

    • Humankind is experiencing unprecedented population growth. An additional 1.5 billion people are estimated to join the current 7.6 billion over the next 30 years[1]. The primary challenge of the contemporary world is to meet accelerating requirements for food, energy, water and basic health. Demand for agricultural products is growing and is expected to increase by about 70% by 2050[2]. There have been considerable improvements in agricultural productivity as reflected by the relatively unchanged total global land area under cultivation since 1991, during which time production has increased and intensified[3]. However, interlinked threats related to food security, increasing pressure on natural resources and climate change have become ever more apparent, and this is attracting the interest of scientists and policymakers in conceptualizing sustainable agricultural practices[4].

      Mixed farming systems that integrate crops and livestock on the same farm are one of the most ancient agricultural practices[5,6]. Such a system in which a tree-integrated with livestock and crops together is known as agrosilvipasture and is one of very old agroforestry practice. These systems occur in nearly all agro-ecological zones under a variety of climatic and soil conditions. Such systems are the mainstay of smallholder production in developing countries and are crucial for food security, accounting for the greatest share of production of staple crops, including 41% of maize, 86% of rice, 66% of sorghum, and 74% of millet production[6]. These systems also produce the bulk of livestock products in the developing world, account for 90% of the milk and 80% of the meat[7] as well as employ millions of people in farms and across value chains.

      In China, mixed-farming systems cover 83% of the total cropland and produce 74% and 87% of corn and wheat, respectively. They produce 90% of mutton and beef, and 50% of pork and poultry meat in China. About 55% of China’s agricultural population is farming in integrated crop-livestock systems[8]. Social, economic and ecological sustainability in China, to a large extent, depends on the management and continual optimization of these mixed crop-livestock production systems. Rice, corn, wheat and sorghum are important crops for China and are cultivated alongside livestock in mixed farming systems. Corn foliage contains significant amounts of nutrients, which can contribute greatly to cattle nutrition and health. Similarly, post-harvest sorghum residue sorghum an excellent source of fodder to ruminants. These mixed systems minimize risks resulting from crop failure, add value to crop residues by converting them into animal protein, cycle nutrients through manure and enhance cultivation through traction[6,8].

      Integrating trees, and in particular, nitrogen-fixating trees, such as leguminous trees, into mixed crop-livestock farms can increase the resilience of farming systems by increasing species richness[9] while providing considerable mitigation benefits[10]. Tree introduction enhanced the resilience of mixed farming systems that nurture soils, increase nutrient cycling and protect against climate shocks[11,12]; at the same time, trees could help mitigate heat-stress related problems in livestock[13]. Diversifying production also makes producers more resilient to economic shocks. Sharing the same piece of land used for crop and livestock production with useful trees is a wise land use decision to minimize competition for available land resources[14,15]. We use the term 'integration' in this paper for these systems, which maximizes land use and promote agricultural diversity as well as livestock production. In addition to diversified farming systems that allow soil and water to be better conserved, such an integrated approach also enables the production of many other ecosystem services including carbon sequestration and biodiversity conservation.

      In this system, crops, livestock and trees interact to create synergy, with recycling allowing the maximal use of available resources (Fig. 1). Aside from total environmental benefits, there are many production and economic benefits of integration. The integration provides ecosystem services such as plant pollinators and birdlife that can help reduce pesticide use and associated costs. Integrated systems increase farm value and can reduce salinity, waterlogging and erosion problems from wind and water in farmlands. Firewood provision and timber production are other benefits, along with carbon farming where such initiatives are implemented[12,14]. The system optimizes the use of all biomass and by-products. The overall environmental and production benefits of integrated livestock, crop and tree farming can provide a sustainable form of agriculture that includes circular agriculture in the context of climate change, including mitigation of the heat stress impact on livestock in the Anthropocene[13,16].

      Figure 1. 

      Outline of synergy between tree, livestock and crops in cropland.

      In this context, choosing the appropriate trees to include based on climatic suitability is an important step in developing integrated systems. Species distribution models (SDM) can provide an estimate of the potential distribution of the ‘climatic niche’ of tree species[17] based on knowledge of its existing distribution. SDM can be valuable to guide climate-based adaptation strategies involving habitat restoration, planning and conservation objectives[18-20]. Based on the set theory, intersection of climatic suitability of tree, crop and livestock can be determined. Fuzzy logic models could be useful in evaluating such intersection. Fuzzy logic refers to a group of methodologies applied in optimal site selection or suitability modelling using a multi-criteria overlay analysis[21]. It is a model of choice in the comparison of species distributions, management, conservation and land use planning[22-24]. In this context, the study aimed to: (i) generate suitability maps for trees and crops based on the theory of niche modelling; (ii) determine possible mixing of trees with crops and livestock using fuzzy logic concept; and (iii) identify suitable sites for appropriate agroforestry systems. We emphasize the importance of maintaining and enhancing locally developed agroforestry systems that have been shown to bolster resilience in mountain ecosystems and livelihoods.

    • For modelling purposes, we selected 20 native/naturalized tree species ethnobotanically important as fodder resources and commonly used in afforestation or in social forestry in China. A review of the literature (ESM 1) revealed that most of these species were multipurpose, fast growing and beneficial for the soil through various combinations of N-fixation, C-sequestration, soil stabilization and erosion prevention (Table 1). Many of the selected trees were already used in agroforestry systems as shelterbelts, windbreaks, or for improving crop and livestock production through alley cropping. In addition to adding fodder value for livestock, selected trees were or could be used as: edible (12), timber (12), medicinal (10), fiber (10), bioenergy feedstock (11) and industrial value (5) (Table 1).

      Table 1.  Characteristic of selected fodder trees, ethnobotanical notes, and agroforestry uses.

      SpeciesGrowth rateFodder valueSoil improvementPotential economic usesAgroforestry
      Ailanthus altissimaFastHigh especially for goat, good for silkwormC-sequestration, soil stabilizationMedicinal, timber, fuelwoodShelterbelt, potential for cultivation in heavily polluted areas and drought tolerant; known to have allelopathic effect, and therefore, proper management is necessary
      Amorpha fruticosaFastHigh, bee forageN-fixation, Erosion controlMedicinal, edible, industrial usesShelterbelt, tolerates poor sandy soils, dry soils, limey soils, acidic soils
      Arundo donaxFastHighC-sequestration, soil stabilizationBioenergy feedstock, medicinal, thatchingShelterbelt, windbreak, ability to grow in different soil types and climatic conditions
      Boehmeria clidemioidesFastHighRemediate soils with heavy metals contaminationFiber, medicinalLocal ethnobotanical value, planted in gullies
      Boehmeria niveaFastHighPrevent soil erosionImportant fiber crop in China, medicinal, ediblePlanted in gullies
      Broussonetia papyriferaFastHighIncrease phosphorus and nitrogen and improve soil moistureFood, paper making, bioenergy feedstock, fiber, medicinal, timberShelterbelt and windbreak, economic fallow crop, leading to increased crop production
      Castanea mollissimaMediumHighIncrease organic matter, nitrogen, phosphorus, and potassium contentEdible, bioenergy feedstock, produce utilizable timber every 10 yearAlley cropping, silviculture practices, good results from Castanea-tea intercropping
      Cyclobalanopsis glaucaMediumHighC-sequestration, improve soil nitrogenFuelwood, bioenergy feedstock, timberBranch and twigs are good material for mushroom culture
      Debregeasia orientalisFastHighImprove metal contaminated soilEdible, high-quality fiberLocal ethnobotanical value
      Elaeagnus angustifoliaFastMedium, good bee forageN-fixation, Erosion control or dune stabilizationEdible, industrial value, bioenergy feedstock, timberShelterbelts, windbreaks or protective plantings
      Ficus heteromorphaFastMediumStabilize soil and increase fertility of soilMedicinal, edible, paper making, pig feedShelterbelts, windbreaks
      Leucaena leucocephalaFastHigh, good bee forageN-fixation, C-sequestrationFiber, edible, timberVery good for a maize crop, alley cropping systems
      Machilus gambleiFastHigh, used for Muga silkworm (Antherea assamensis) in NE IndiaN-fixation, C-sequestrationEdible, fiber, medicinal, timber, potential for bioenergy feedstockLocal ethnobotanical value
      Morus albaFastHigh, good for silkwormErosion controlEdible, industrial value, bioenergy feedstockShade and shelter, windbreak
      Populus adenopodaFastHighIncrease in soil organic carbon, soil stabilizationTimber, fiber, bioenergy feedstockShade and shelter, windbreak
      Populus davidianaFastHighIncrease in soil organic carbon, soil stabilizationTimber, bioenergy feedstockShade and shelter, windbreak
      Populus tomentosaFastHighIncrease in soil organic carbon, soil stabilizationTimber, fiber, bioenergy feedstockShade and shelter, windbreak
      Salix babylonicaFastHigh, bee forageErosion controlMedicinal, fiber, light timber, bioenergy feedstockShade and shelter, windbreak
      Saurauia thyrsifloraFastHighErosion controlEdible, medicinalLocal ethnobotanical value, high milk production in livestock
      Ulmus pumilaFastHighErosion control, stabilizing sand dunesFiber, medicinal, edible, timberShelterbelt, windbreak, enhance crop production
      Source: literature listed in ESM 1
    • The stepwise elimination of bioclimatic variables with VIF values greater than 10 resulted in a set of ten least correlated (Pearson correlation value < 0.8) bioclimatic variables: bio2, bio3, bio8, bio13, bio14, bio15, bio21, bio22, bio25 and bio31. These variables successfully produced a distribution model for each of the selected tree species. Consensus maps showing the results from ensemble model for each fodder tree species are given in Figure 2. The models correctly predicted most of the test locations in all cases. Sub-models for all selected species weight between 0.05 and 0.29 (Table 2). The ensemble models had final AUC ranges from 0.82 to 0.98 for different species, while kappa values ranged from 0.74 to 0.97 (Table 2).

      Figure 2. 

      Consensus mapping based on ensemble modelling. The bioclimatic suitability for each of the selected species, with the observed point distributions overlain. ‘a’ to ‘t’ are species codes for the fodder tree species as listed in Table 2.

      Table 2.  Final weights of each sub-models for ensemble forecasting, AUC, kappa and threshold for ensemble models

      GLMMAXENTMAXLIKERFRPARTENSEMBLE
      Spp codeSppWeightWeightWeightWeightWeightAUCKappamaxTPR+TNR
      aAilanthus altissima0.220.240.220.230.090.840.810.59
      bAmorpha fruticosa0.250.260.220.2600.820.740.57
      cArundo donax0.240.250.170.250.090.930.840.6
      dBoehmeria clidemioides0.220.220.150.220.190.920.850.48
      eBoehmeria nivea0.20.220.210.220.160.920.890.53
      fBroussonetia papyrifera0.230.240.10.240.190.920.820.56
      gCastanea mollissima0.210.210.20.210.160.880.830.65
      hCyclobalanopsis glauca0.20.210.20.210.180.920.860.51
      iDebregeasia orientalis0.190.210.210.210.180.950.940.46
      jElaeagnus angustifolia0.210.220.150.230.180.890.850.61
      kFicus heteromorpha0.210.210.210.210.160.910.810.55
      lLeucaena leucocephala0.240.250.170.260.080.940.930.68
      mMachilus gamblei0.210.240.230.230.080.970.970.75
      nMorus alba0.250.250.250.2400.840.850.64
      oPopulus adenopoda0.290.290.050.290.090.890.90.54
      pPopulus davidiana0.220.220.210.230.120.850.890.44
      qPopulus tomentosa0.210.240.210.260.090.890.810.6
      rSalix babylonica0.180.260.240.270.060.830.740.55
      sSaurauia thyrsiflora0.190.210.20.210.180.980.970.76
      tUlmus pumila0.220.220.20.220.130.840.860.51
      NB: sub-model with ‘0’ indicate that particular sub-model was calibrated but not used in the ensemble model.

      Bioclimatic suitability of 20 fodder tree species within China as estimated by the ensemble modelling is presented in Figure 2 (also listed in ESM 2). Species were classified based on the area of bioclimatic suitability and magnitude of suitability. Suitability of fodder trees was well represented across each of the seven crop growing areas (Fig. 2 and Fig. 3) in China. More fodder species found bioclimatic suitability in areas where grain crops, crop/pasture rotations, agro-silvopastoral systems and ponds systems dominated. Few of the selected fodder tree species showed bioclimatic suitability in areas dominated by rangeland systems.

      Figure 3. 

      Climatic suitability index of important crops in China; Millet include both pearl and foxtail millet.

      The climate suitability index of selected crops was found to be relatively low for northern and western China where low yield was reported. Higher climate suitability index was found for all the crops in southern and eastern parts of China comprised of mostly overlapping farmlands and adjoining areas (Fig. 3).

    • The fuzzy logic model output identified areas where fodder trees could be integrated with livestock and crops. According to the model, rice-based integration could be suitable in the southern, southeast and eastern parts of China. Maize, soybean and sorghum could be integrated in the southern, eastern and central parts, whereas millet, wheat and sorghum could be integrated in central and northern parts. Distinct spatial data were available for different livestock species, whereas such data were lacking for individual crops, meaning that crops could not be treated individually like livestock and fodder tree species. The potential distribution of each fodder tree was overlain with the distributions of different livestock species and crops to produce fuzzy maps (Fig. 4; for individual livestock detail maps documented in ESM 3). The range of the fuzzy maps lies between ‘0’ and ‘1’. When membership function values of all the three layers were 0 (minimum), the suitability for integration was minimum, and when the membership function values were 1 (maximum) the possibility for integration was at its greatest. Our model revealed that most of the fodder tree species were suitable for integration toward the eastern and southern parts of China. Few species found agro-ecological suitability in northern China, and few species could be incorporated into integrated systems in western China (Fig. 4). The most suitable areas fell in the humid and sub-humid regions, with few options for integration in the more arid regions.

      Figure 4. 

      Major areas suitable for integration of selected fodder trees with crops and livestock. ‘a’ to ‘t’ are species codes for the fodder species as listed in Table 2.

    • We presented a comprehensive analysis of climatically suibtable areas where a system could be adopted that potentially integrates fodder trees with crop and livestock. This is a pioneering work aimed at planning theintegration of trees with crops and livestock. The integration of trees, crops and livestock is potentially beneficial for yield and ecosystem, services such as mitigating climate change and reducing land degradation[11,12]. However, it is challenging to select trees that are both appropriate for the climate and capable of delivering benefits to farmers on agricultural lands[25,26]. Farmer’s decision to adopt tree on the agricultural land mostly based on the economic benefit and immediate return[26]. Providing farmers with the option of climatically suitable trees that have multiple benefits including economic benefits is thus extremely important, and it is essential to identify the right trees and locations that are best suited for implementation[4]. This study has identified suitable places for the integration of specific fodder trees with crops and livestock. Selecting appropriate tree species for agroforestry practices largely depends upon understanding the environmental requirements of the species under question and matching those to the prevailing conditions in a given area[25]. Evans et al.[27] reported SDMs as a promising and useful method for modeling biofuel feedstocks and other cultivated crops. Our model was successful in defining the bioclimatic boundaries for the 20 fodder tree species. Fuzzy logic has been applied for geographical modelling; however, recent research applied this method to assess climate suitability of crops (e.g. Kim et al. [28]). It also provides a variety of options for combining variables[29], which we used in intersecting tree suitability with crop and livestock distribution.

      Rudel et al.[15], in their global meta-analysis, showed a strong association between mixed crop-livestock operations and sustainable practices. The integration of fodder trees into mixed systems provides greater diversification in the farming landscape that might decrease the threat of crop failures due to climate change and increased climate variability and increase resilience to economic shocks[30,31]. When the price of inputs or outputs changes to make one commodity no longer profitable, a diversity of products can help farmers to overcome difficult economic times[16]. Research shows the economic performance of mixed farming systems is better than that of specialized farming systems in the Netherlands[32]. A similar case from Belgium was observed during a crisis of milk prices; the farmers of mixed systems fared much better than producers who had specialized only in milk production. In a linear bio-economy excreta from the intensive animal, production becomes a form of waste, polluting soil and water as well as emitting GHGs[33,34]. An integrated system, conversely, promotes a circular bio-economy in which crops and livestock interact through recycling. For example, manure is returned to the land to fertilize crops, and livestock are fed on crop residues, allowing the maximum use of available resources. On-farm GHG emissions can be reduced through the development of an integrated system that changes land-use patterns and improves farm management practices[34]. Hence, integrated systems that involve trees, crops and livestock can be more environmentally sustainable than specialized systems or simple mixed crop-livestock system[35].

      Similarly, ecological interactions between trees, crops and livestock can provide a wide range of services, including soil and water management, microclimate modification, weed control, natural fencing, carbon sequestration and nutrient cycling[36,37]. Such an integrated system has manifold benefits and, through centuries of experience, has proven to be sustainable in many Asian communities[38]. These systems intensify agrobiodiversity, which is valuable for food security, health and income[39]. Because of the high agro-biodiversity, nutritional supplements and waste recycling in integrated farming, resilience to climatic extremes is higher[15]. In addition to regular and seasonal feed source, fodder trees can provide shade to livestock, which is important for mitigating heat stress, which can potentially cause problems in reproduction and reduce milk production in cattles[13]. Most of the tree species selected for modelling in our study are useful in atmospheric nitrogen fixation, carbon sequestration, improving soil through phosphorus and potassium, stabilizing soil and reducing soil erosion. Ideal fodder tree species should meet fodder deficiencies in times of extreme climatic conditions, such as droughts. They must be fast growing, require little land, labor or capital, produce numerous by-products and, ideally, supply feed within a year after planting[40]. Most of the selected species are fast-growing and all of them are ethnobotanically important fodder trees. A review of the literature (listed in ESM 1) shows them to be valuable fodder resources, economic values (e.g. timber, medicinal) and have high agroforestry potential. Several examples of successful agroforestry using the selected species are available. For instance, Broussonetia papyrifera is successfully grown with rice in Laos[41] and Leucaena leucocephala is grown as a hedge tree with maize or millet in middle hills of Nepal[42]. Similarly, the majority of farmers grow trees species of Morus, Leucaena, Ficus, Cyclobalanopsis with cereal crops (maize, wheat, millet), lentils and vegetable as an agroforestry practice in Nepal[42]. Research show Castanea mollissima intercropping in tea plantations improves resource availability, ecosystem function and product quantity and quality[43]. Poplar (Populus spp.) based agroforestry is popular in different parts of Asia, including China[44]. In addition, most of them are important for timber, bioenergy feedstock, edible products and have medicinal purposes. There are also possibilities for marketing fodder tree biomass and its use in commercial feeds[40]. Beside fodder value, species like Arundo donax, Broussonetia papyrifera, Castanea mollissima, Cyclobalanopsis glauca, Machilus gamblei and Morus alba have potential value as bioenergy feedstocks[45,46]. The next step is to investigate specifically what these different species may contribute to existing farming systems in the area and conduct trials to quantify benefits.

      Despite the many benefits, trade-offs between different options need to be considered carefully as strategies and policies are developed. One possible trade-off includes a reduced crop yields per unit of land area used during the tree establishment and development phase of integrated farms[47]. Integrating trees in farmland is held back because of a lack of reliable tools to accurately predict yields from tree-crop mixtures[48]. These fully integrated systems are highly labor-intensive during the startup phase and are not well suited to mechanization. There are several examples that indicate proper management of trees in integrated systems can maximize landuse, ecosystem services as well as production[49]. For example, when the paper mulberry is managed properly (lopping and trimming), rice yields maintain the same levels as before intercropping was introduced[41]. The findings from other places indicate that in addition to ecological services, selected trees could provide off-farm income and opportunities to bolster income. However, conducting trials and systematic data collection of integrated systems would allow closer examination of the socio-economic and ecological factors as well as ecosystem services.

    • Results from our work provide an initial step in the planning and implementation of tree-crop-livestock systems, which can bring many environmental benefits to more specialized production practices[50,51]. A logical next step is the investigation of these results through appropriate field implementation. The results presented here provide landscape-level indications of where it is expected that particular tree fodder species will thrive in integrated tree-crop-livestock systems. As an example, we have listed the candidate species for tree-livestock-crop system integration in Honghe County of Yunnan Province, where the authors are currently implementing agroforestry projects. The results indicate that 13 of the 20 species analyzed could be integrated into various sites of Southeast or Northwest Honghe. These species include Ailanthus altissima, Arundo donax, Boehmeria clidemioides, Boehmeria nivea, Broussonetia papyrifera, Castanea mollissima, Cyclobalanopsis glauca, Debregeasia orientalis, Ficus heteromorpha, Leucaena leucocephala, Machilus gamblei, Morus alba and Populus davidiana.

    • We considered the whole of China for the modelling work. Agriculture is a vital sector in China, providing livelihoods to several hundred million people. Rice, corn, wheat, sorghum and soybeans are among the major crop produced in China. Mixed crop-livestock production systems are commonly practiced, and five broad production systems are recognized[8]. These systems are separated based on agro-ecology, dividing the country into rangeland, grain crops, crop/pasture rotations, agro-silvopastoral systems and pond systems, overlain with the seven important crops mentioned above (Fig. 5).

      Figure 5. 

      Map of eco-regions, cropland and the crop-livestock production systems in China: 1. systems based on rangeland; 2. systems based on grain crops; 3. systems based on crop/pasture rotations; 4. agro-silvopastoral systems; and 5. systems based on ponds (adapted from Hou et al.[8] and Broxton et al.[52])

    • In this paper, we included 20 tree species. These were selected based on (i) existing plantation practice and farmers' preferences; (ii) well-perceived potential of the species to address economic and ecological benefits (e.g., livelihood source, soil fertility, shade tree and combinations thereof); and (iii) availability of ground data from various ethnobotanical and agroforestry projects in which the authors were involved as well as a review of the relevant literature (e.g., Fang et al.[53]). The twenty fodder tree species were represented by a total 3,016 geo-coordinated points which were consigned to a 2 km grid for the analysis. Cropland data were extracted using the Land Use Land Cover (LULC) 2010 map of China. Crops (Table 3) were modelled based on temperature and precipitation relations. Livestock data were obtained from Robinson et al.[54] and confirmed with annual statistical information from the Chinese government. Livestock used in this study includes cattle, buffaloes, goats and sheep. We used bio-climatic variables downloaded from the CliMond archive[55] as inputs for the modelling of the 20 fodder tree species. Detailed information on these bio-climatic variables, comprising variables of temperature, precipitation, radiation and moisture indexes are listed in ESM 4.

      Table 3.  Optimum and absolute range of temperature and precipitation for selected crops.

      AbsoluteOptimum
      CropTmaxTminPmaxPminTmaxTminPmaxPmin
      Oryza sativaRice36104 0001 00030202 0001 500
      Sorghum bicolorSorghum4083 00030035271 000500
      Zea maysMaize47101 80040033181 200600
      Hordeum vulgareBarley4022 00020020151 000500
      Pennisetum glaucumPearl millet40121 7002003525900400
      Setaria italicaFoxtail millet3554 0003002616700500
      Glycine maxSoybean38101 80045033201 500600
      Triticum aestivumWheat2751 6003002315900750
      Tmax − Maximum temperature, Tmin − Minimum temperature in °C; Pmax − Maximum precipitation, Pmin − Minimum precipitation in mm
    • BiodiversityR package (Ver. 2.8−2) and dismo package in R were used to prepare bioclimatic suitability maps for the 20 fodder trees, important crops and other analysis. Five different algorithms − Maxent, Maxlike, Random Forests (RF), Generalized Linear Models (GLM) and Recursive Partitioning and Regression Trees (RPart) − were used in the tree modelling. Following Hijmans[56], we used 4-fold cross-validation, where tree occurrence and background data were partitioned into 75% calibration and 25% evaluation observations. The consensus mapping technique is based on an ensemble of several niche-modeling algorithms (sub-models). The results using each model were treated as individual sub-models (Pmod), with weights assigned based on their performance. An ensemble model (Pensemble) was then calculated using the following formula[57,58]:

      Pensemble=(wmodPmod)(wmod) (1)

      where, wmod = weighted averages of sub-models (Pmod).

      Bioclimatic variables were selectively removed based on variance inflation factor (VIF)[59] calculations where VIF > 10 were eliminated (ESM 5) to provide a minimum set of the least correlated bioclimatic variables.

      An EcoCrop model was used to identify areas suitable for selected seven important crops in China. The EcoCrop is a simple mechanistic model that use expert-based temperature and rainfall ranges[60]. The FAO-EcoCrop database (http://ecocrop.fao.org/) provided such ranges for crops and hence used in this work to determine the climatic niche of crops in China and then produces a suitability score. The model needs absolute range (at which the crop can grow) and the optimum range (at which the crop grows best) of temperature and precipitation. Table 3 presents range of absolute and optimum temperature and precipitation for selected crops. The model determines suitability index based on the conditions over the growing season at a particular place using a gridded data of temperature and rainfall. The suitability index ranges from zero (not suitable) to 100 (highly suitable). The model performs two different calculations separately, one for rainfall and the other for temperatures, and then calculates the interaction by multiplying them.

    • We evaluated the ability of sub-models to cope with spatial autocorrelation by calculating calibrated Area Under the Receiver Operator Curve (cAUC) values and comparing these with a geographical null mode[59]. Spatial sorting bias[56] was removed and carried out through several rounds of calibration and evaluation of all models (including the geographical null model), each time using three partitions for model calibration and one partition for model evaluation. Elimination of spatial sorting bias in testing data in model calibrations produced cAUC values of the different algorithms between 0.6 to 0.85 for different tree species. These values were significantly different from the null model (0.49 and 0.501 for the null model, which is equivalent to a random draw[56]; Mann-Whitney tests, p < 0.05 in all cases). Weights calculated for the sub-models were used to determine the appropriate weights (ranging between 0 and 1) for the ensemble model.

      Kappa and AUC values were calculated for each of the ensemble models. The ensemble output consists of a consensus map that represents the agreement between sub-models. All pixels in the consensus map output were classified according to the cut-off point, based on a threshold defined by maximizing the sum of the true presence and true absence rates (maxTPR+TNR). A score above this threshold represents the suitable climatic space for the species in question[57]. All pixels with suitability scores above the cut-off point were included in the final bioclimatic suitability map for each species.

    • A fuzzy logic model was employed to identify areas suitable for tree-crop-livestock integration. Classified raster layers of fodder tree and crop distributions were overlain with raster layers of ruminant livestock and cropland in China (Fig. 5). Fuzzy overlay analysis is based on set theory in which a set generally corresponds to a class. Fuzzy overlay analysis reclassifies or transforms data values to a common scale, but the transformed values represent the probability of belonging to a specified class. Fuzzy logic system can facilitate complex approaches, such as the incorporation of biotic interaction in the modelling[28]. The combining step in fuzzy overlay analysis quantifies each location’s probability of belonging to specified sets from various input rasters[24,29]. The equation using fuzzy Gaussian function is:

      μ(x)=ef1×(xf2)2 (2)

      where, the inputs to the equation f1 and f2 are the spread and the midpoint, respectively. The midpoint is a user-defined value with a fuzzy membership of 1. The default is the midpoint of the range of values of the input raster. Spread defines the membership of the Gaussian function. It generally ranges from 0.01 to 1. Increasing the spread causes the fuzzy membership curve steeper. Fuzzy overlay analysis quantifies the possibilities of each cell or location to a specified set based on membership value[29].

      • The research was financially supported by key research project “Agroforestry Systems for restoration and bio-industry technology development (grant no: 2017YFC0505101)”, by the Agriculture Science and Technology Innovation Program (ASTIP-IAS07; CAAS-XTCX2016011-01), Research Program of the State Key Laboratory of Animal Nutrition (2004DA125184G1103), Bureau of International Cooperation Chinese Academy of Sciences (151853KYSB20160032) and CGIAR Research Program on Climate Change (FTA-FP5). We acknowledge support from Key Laboratory of Economic Plants and Biotechnology Kunming Institute of Botany, Chinese Academy of Sciences Kunming China and Prof. Yuhua Wang for providing information and data on fodder trees.

      • The authors declare that they have no conflict of interest.

      • ESM 1 List of literature cited to understand the characteristic of selected fodder trees mentioned in Table 1.
      • ESM 2 Presence absence map generated based on a threshold (see Table 2) defined by maximizing the sum of the true presence and true absence rates (maxTPR+TNR) for 20 fodder tree species as estimated by the ensemble modelling. ‘A’ to ‘T’ are tree species codes for the fodder tree species as listed in Table 2.
      • ESM 3 Fuzzy map produced for different livestock to show suitable regions for integration with tree potential distribution and cropland. ‘A’ to ‘T’ are tree species codes for the fodder tree species as listed in Table 2.
      • ESM 4 Description of Bioclim variables and the contributing variables used in their calculation.
      • ESM 5 Results of VIF analysis for selection of least correlate bioclimatic variables.
      • Copyright: © 2021 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (5)  Table (3) References (60)
  • About this article
    Cite this article
    Ranjitkar S, Bu D, Sujakhu NM, Marius G, Robinson TP, et al. 2021. Mapping tree species distribution in support of China's integrated tree-livestock-crop system. Circular Agricultural Systems 1: 2 doi: 10.48130/CAS-2021-0002
    Ranjitkar S, Bu D, Sujakhu NM, Marius G, Robinson TP, et al. 2021. Mapping tree species distribution in support of China's integrated tree-livestock-crop system. Circular Agricultural Systems 1: 2 doi: 10.48130/CAS-2021-0002

Catalog

  • About this article

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return