Centre of origin of different tropical forage legumes.
\r\n\tMethadone maintenance treatment (MMT) has become the main pharmacological option for the treatment of opioid dependence. Methadone remains the gold standard in the substitution treatment, which is a harm reduction intervention, because the patient does not become abstinent, but there are a series of positive changes. Currently, the surveillance of methadone substitution treatment is considered an ongoing challenge, given the need for the individualization and the increasing of the therapy efficiency. Methadone has been also studied as an analgesic for the management of cancer pain and other chronic pain conditions.
\r\n\r\n\tThe complexity of methadone pharmacology, the high inter-individual variability in methadone pharmacokinetics, the risk of opioid diversion, the overdose and other adverse events pose many challenges to clinicians.
\r\n\tThe aim of the proposed book is to update and summarize the scientific knowledge on the opioid dependence, including the mechanism of opioid dependence, the misuse of prescription opioids and the substitution therapy of opioid dependence.
Providing a quantitative insight into light-induced electronic structure reorganization of complex chromophores remains a challenging task that has attracted a substantial attention from theoretical communities in the past few years [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]. Indeed, a potential knowledge related to the ability of a chromophore to undergo a charge transfer caused by photon absorption or emission [16, 17] is of seminal importance for designing novel dyes with highly competitive optoelectronic properties [18, 19, 20, 21]. Most often, such quantitative probing of the charge transfer locality is accompanied by a qualitative study of the rearrangement of the electronic distribution in the molecule, and the aim of this contribution is to demonstrate how in certain cases different topological paradigms are formally connected, with the junction point being the definition of the molecular exciton wave function.
\nThe outcome of the computation of the molecular electronic excited states using a quantum calculation method is, in addition to the transition energy, a series of mathematical objects allowing one to analyze the transition topology. If the reference ground state wave function is, in a given basis (called the canonical basis), written as a Slater determinant, any excited state written based on this ground state wave function is called a single-reference excited state. From this single reference and in a given canonical basis, some methods express excited states as a linear combination of singly excited Slater determinants, which means that the excited state wave function is written as a pondered sum of Slater determinants constructed from the ground state reference, in which one occupied spinorbital (vide infra) is replaced by a virtual one. This type of excited state construction is often referred to as a configuration interaction (CI) solely involving singly excited Slater determinants. In our case, the reference ground state wave function can be a Hartree-Fock or a Kohn-Sham Slater determinant, and the excited states calculation methods we deal with in this paper are called configuration interaction singles (CIS), time-dependent Hartree-Fock (TDHF), random-phase approximation (RPA), Tamm-Dancoff approximation (TDA), or time-dependent density functional theory (TDDFT). For more details about the machinery of these methods, see Refs. [22, 23, 24, 25]. While in the case of CIS and TDA, the determination of the exciton wave function is very straightforward, for the other methods, it has been subject to the so-called assignment problem which consisted in providing a CI structure to the TDDFT excited state (since the central RPA/TDHF and TDDFT equations have the same structure, the assignment problem is transferable to these methods also) [26, 27].
\nBased on the outcome of the excited states calculation, one can select an electronic transition of interest and inspect the different hole/particle contributions from the occupied/virtual canonical subspaces for having an insight into the light-induced charge displacement topology. However, in some occurrences, such analyses are quite cumbersome because many of these contributions can be significant while bearing a divergent physical meaning. For the purpose of providing a straightforward picture of the electronic transition topology, multiple tools were developed. Among them, one can cite the detachment/attachment strategy [3, 4, 25, 28, 29, 30, 31], which delivers a one-electron charge density function for the hole and for the particle that are generated by photon absorption. This strategy is based on the diagonalization of the so-called difference density matrix (the difference between the excited and ground state density matrices) and a sorting of the resulting “transition occupation numbers” based on their sign. The result of this analysis is a simple identification of the photogenerated depletion and increment zones of charge density. Quantitative insights are then reachable through the manipulation of the detachment/attachment density functions and the definition of quantum metrics [3, 4, 5]. On the other hand, one can consider the projection of the exciton wave function in the canonical basis through the so-called transition density matrix [13, 25, 29, 30, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43], which singular value decomposition [44] provides the most compact spinorbital representation of the electronic transition. The great advantage of this method is that in most of the cases it condensates the physics of an electronic transition into one couple of hole/particle wave functions.
\nThis chapter first recalls some useful concepts related to the reduced density matrix formalism and its relation to the notion of electron density and density matrix in a canonical space. The detachment/attachment density matrix construction is then exposed in details and is used for quantifying the charge transfer locality through several quantum descriptors. Afterward, the notion of density matrix is extended to electronic transitions through the concept of transition density matrix. The information contained in this particular matrix is shown to be extractable and is discussed in details by introducing the so-called natural transition orbitals. The detachment/attachment and natural transition orbitals formalisms are then compared, and we demonstrate that the difference density matrix is constructed from the direct sum of two matrix products involving only the transition density matrix, that is, the molecular exciton wave function projected into the canonical space (Lemma III.1). It follows that the natural transition orbitals are nothing but the eigenvectors of the detachment/attachment density matrices (Theorem III.1), which is a major conclusion in this contribution since the two formalisms are often introduced as being distinct and belonging to two separate paradigms. This conclusion is finally used for showing that the quantum indices designed for quantifying the charge transfer range and magnitude can be equivalently derived from the detachment/attachment and natural transition orbitals paradigms (Corollary III.1).
\nAll the derivations are performed in the canonical space in the main text, but the important concepts and conclusions are also written in the basis of atomic functions in Appendix B. The calculations performed for this book chapter were done using the G09 software suite [45].
\nSince this chapter will be mostly dealing with quantum state density matrices, the first paragraph of this section consists in a short reminder about the one-particle reduced density matrices corresponding to single-determinant wave functions.
\nWe consider an N–electron system, with the N electrons being distributed in L spinorbitals (N occupied, L − N virtual). In this contribution we will write any ground state wave function ψ0 as an arrangement of the occupied spinorbitals into a single Slater determinant. The density matrix kernel representing the corresponding ground electronic state writes
\nwhere x is a four-dimensional variable containing the spatial (r) and the spin-projection (σ) coordinates. The density matrix kernel reduces to the electron density function when \n
The (γ0)rs terms appearing in Eq. (1) are the elements of the one-particle reduced density matrix expressed in the canonical space of spinorbitals {φ} and can be isolated by integrating the product of \n
Note that generally speaking the r × s density matrix element in a given spinorbitals space {φ} for a given quantum state ∣ψ〉 writes
\nwhere conventionally r and s indices range from 1 to L. In Eq. (4) we introduced the annihilation and creation operators from the second quantization.
\nOne known strategy for formally assigning the depletion and increment zones of charge density appearing upon light absorption is the so-called detachment/attachment formalism. This approach consists in separating the contributions related to light-induced charge removal and accumulation by diagonalizing the one-particle difference density matrix γΔ ∈ \n
This density matrix can be projected into the Euclidean space in order to directly visualize the negative and positive contributions to the light-induced charge displacement:
\nNote that since no fraction of charge has been gained or lost during the electronic transition, the integral of this difference density over all the space is equal to zero:
\nHowever, visualizing this difference density does not provide a straightforward picture of the transition. The interpretation of the transition in terms of charge density depletion and increment can be made more compact by diagonalizing the difference density matrix:
\nwhere m is a diagonal matrix and M is unitary. Similar to the eigenvalues of a quantum state density matrix, the eigenvalues of γΔ, contained in m, can be regarded as the occupation numbers of the transition in the canonical space. Those can be negative or positive, corresponding, respectively, to charge removal or accumulation. These eigenvalues can therefore be sorted with respect to their sign:
\nwhere k+ (respectively, k−) is a diagonal matrix storing the positive (absolute value of negative) eigenvalues of the difference density matrix. These two diagonal matrices can be separately backtransformed to provide the so-called detachment (d) and attachment (a) density matrices and the corresponding charge densities:
\nThese detachment/attachment densities (nd(r) and na(r)) are then nothing but the hole and particle densities we were seeking. These densities are reproduced in Figure 1 for two paradigmatic cases of electronic transitions: one local transition and one long-range charge transfer. In the next paragraph, we will see how the locality of a charge transfer can be quantified using the detachment/attachment charge densities.
\nIllustration of a local (left) and long-range (right) transition using detachment/attachment densities and the ϕS index.
One possible strategy for evaluating the magnitude of the electronic structure reorganization is to compute the spatial overlap between the hole and the particle. This is possible through the assessment of a normalized, dimensionless quantity named ϕS:
\nwhere ϑx is a normalization factor (the integral of detachment/attachment density over all the space). Obviously, a long-range charge transfer means a low hole/particle overlap and will correspond to a low value for ϕS. Conversely, a local transition will be characterized by a higher ϕS value. This is clearly illustrated in Figure 1 where the ϕS value drops from 0.77 to 0.17 when going from an electronic transition exhibiting a large hole/particle overlap to a long-range charge transfer. These two cases are used solely to illustrate the potentiality of the ϕS quantum metric to assess the locality of a charge transfer. The computation of ϕS is schematically pictured in the top of Figure 2.
\nIllustration of the complementarity between ϕS and \n\n\nφ\n˜\n\n\n.
It has also been demonstrated that ϕS can be used for performing a diagnosis on the exchange-correlation functional used for computing the transition energy within the framework of TDDFT [3].
\nAn additional quantitative strategy consists in computing the charge effectively displaced during the transition. The difference between the hole/particle and the effectively displaced charge density is illustrated in Figure 2: since there can be some overlap between the hole and the particle densities, the global outcome (the “bilan”) of the transition in terms of charge displacement is not the detachment and attachment but the negative and positive contributions to the difference density, which can be obtained by taking the difference between the attachment and detachment charge densities at every point of space. Indeed, from
\nwe can write
\nand introduce the actual displacement charge density functions
\nso the splitting operation is performed based on the sign of function entries in the three dimensions of space instead of transition occupation numbers. From this separation we can compute the normalized displaced charge:
\nObviously, splitting the transition occupation numbers and computing the detachment/attachment overlap are complementary to the integration of the negative and positive contributions to the difference density function: the ϕS descriptor provides an information related to the locality of the charge transfer, while the \n
These two complementary approaches have been associated into a final, general quantum metric of charge transfer:
\nwhich, as it was the case for ϕS and \n
Figure 3 represents the ψ projection for a series of dyes. These chromophores are constituted by an electron-donor fragment conjugated to an acceptor moiety through a molecular bridge with a variable size (i.e., a variable number of subunits).
\nIllustration of the evolution of the ψ index value for the first excited state of a series of push-pull dyes, computed at the PBE0/6-311++G(2d,p)//PBE0/6-311G(d,p) level of theory in vacuum.
We see that when the first excited state of these dyes is computed using TDDFT with the hybrid PBE0 exchange-correlation functional [46, 47] and a triple-zeta split-valence Gaussian basis set with diffuse and polarization functions on every atom [48], increasing the number of bridge subunits leads to a net decrease in the ψ projection angle. It is therefore very clear from Figure 3 that increasing the length of the bridge for this family of dyes leads to an increase of the charge transfer character of the first transition, when computed at the above-mentioned level of theory.
\nThe following paragraph details another known strategy providing a straightforward qualitative analysis of the charge transfer topology, based on another type of density matrix: the transition density matrix.
\nIn the following section we will be interested in the determination of the exciton wave function and its use for providing the most compact representation of an electronic transition. More particularly, this paragraph exposes how we can find an alternative basis to the canonical one and reduce the picture of the transition to one couple of hole/particle wave functions. The following formalism is applied to the case of quantum excited states that can be written as a linear combination of singly excited Slater determinants, constructed from the single-reference wave function (ψ0) where the spinorbital φi from the occupied canonical subspace has been replaced by the φa spinorbital belonging to the virtual canonical subspace. In these conditions, the xth excited electronic state writes
\nwhere again we introduced the annihilation \n
is a normalization factor and \n
That is, the so-called transition density matrix kernel locating the hole (r1) in the ground state and the particle (\n
Note that we conventionally set the i , j and a , b indices to match spinorbitals, respectively, belonging exclusively to the occupied and virtual canonical subspaces, while r and s indices have no restricted attribution to a given subspace. Similarly to the quantum state electron density function, one can deduce the expression of the one-particle transition density from the transition density matrix kernel:
\nwhere the δia Kronecker delta is systematically vanishing since φi and φa spinorbitals never belong to the same subspace. Here again, we will take advantage of the possibility to use finite mathematical objects such as matrices and perform a reduction of the one-particle transition density matrix size: since we know that i and a indices are restricted to occupied and virtual subspaces, we can introduce the normalized transition density matrix T (that we will call transition density matrix in the following):
\nso the connection between the two matrices is trivial:
\nwhere 0k × l refers to the zero matrix with k × l dimensions. For the sake of simplicity, we will use 0o and 0v for the occupied × occupied and virtual × virtual zero blocks and 0o × v and 0v × o for the out-diagonal blocks.
\nWe will now focus on T. This matrix contains the information related to the transition we seek, and similarly to the difference density matrix, we will extract this information by diagonalizing T. However, since T is not square but rectangular (we rarely have the same number of occupied and virtual orbitals), the diagonalization process is named singular value decomposition (SVD) [44] and takes the form
\nThe diagonal λ entries are called the singular values of T. Due to the dimensions of λ, the number of singular values is equal to the dimensions of the lowest subspace (i.e., N or L − N). Most often, the number of virtual orbitals is larger than the number of occupied orbitals. Therefore, from now on we will assume that N < L − N.
\nWhile from the diagonalization of γΔ we could build detachment/attachment densities, here we will use the left and right eigenvectors of T for rotating the occupied and virtual canonical subspaces into the so-called occupied/virtual natural transition orbital (NTO) spaces:
\nwhere i ranges from 1 to N. We have built N couples of occupied/virtual NTOs, each couple being characterized by the corresponding singular value (λ)ii. The great advantage of performing an SVD on T is that in most of the cases, only one singular value is predominant, which means that we can condensate all the physics of an electronic transition into one couple of occupied/virtual NTOs, as represented in Figure 4.
\nIllustration of the hole (top) and particle (bottom) wave functions, that is, the predominant couple of occupied (top) and virtual (bottom) NTOs for a random push-pull chromophore experiencing a photoinduced charge transfer.
We can conclude that, similarly to the usual quantum state natural orbitals which constitute the basis in which the quantum state density matrix is diagonal, the NTOs provide the most compact representation of the electronic transition and can be used to rewrite the expression of the electronic excited state and the transition density matrix kernel (the exciton wave function):
\nwhere this time the creation/annihilation operators are bearing the “o” and “v” superscripts, reminding that we are annihilating an electron in the ith occupied (o) NTO and creating one electron in the ith virtual (v) NTO. Since we know that usually one singular value is predominant, we can clearly identify the hole and particle wave functions and state, upon light absorption, from where the electron goes and where it arrives.
\nMultiplying T by its own transpose and vice versa leads to two square matrices with interesting properties:
\nDue to their structure, these two new matrices share the same eigenvectors than T
\nwith, considering N < L − N, the following rules for their eigenvalues:
\nThese rules can be demonstrated by developing the product of λ with its own transpose:
\nwhere Iv is the (L − N) × (L − N) identity matrix. Due to the dimensions of λ and its diagonal structure, we can write
\nSimilarly, we have for λ†λ
\nand
\nMultiplying Eq. (28) by the left by T†O or TV leads to two new eigenvalue problems:
\nwhere Vo ∈ \n
We now have two general strategies for qualitatively studying the topology of the light-induced electronic cloud polarization, and the locality of this electronic structure reorganization can be quantified. This section is devoted to single-reference excited states calculation methods that express the electronic excited state as a linear combination of singly excited Slater determinants and brings the rigorous demonstration that in such case, the three quantum metrics we previously designed can be formally equivalently derived from the difference density matrix or the transition density matrix. This result is the corollary to a theorem stating that the occupied/virtual NTOs are nothing but the eigenvectors of the detachment/attachment density matrices.
\nIn this paragraph we elucidate the structure of the difference density matrix by developing the full expression of the excited state density matrix in the canonical space.
\nLemma III.1 The difference density matrix is the direct sum of −TT† and T†T.
\nProof. We start by writing the expression of the ground state density matrix: from Eq. (4) it follows that for an N–electron single-determinant ground state wave function,
\nIt follows that the ground state one-particle density matrix in the canonical space writes
\nIf now we rewrite the electronic excited state ∣ψx〉 from Eq. (17) using the normalized transition density matrix elements, we have
\nFrom now on we will operate a systematic index shift between matrix elements and virtual orbitals implied in the singly excited Slater determinants. Since the excited state wave function is normalized, we can write
\nand, since the trace of a matrix is an unitary invariant,
\nUsing the second quantization, we might rewrite ∣ψx〉
\nand the r × s density matrix element for the xth excited state writes
\nWe will now apply Wick’s theorem to the expression of the excited state density matrix written using our fermionic second quantization operators. According to this theorem, one can rewrite Eq. (41) as a combination of products of expectation values of couples of the second quantization operators implied in the expression of γx. Since we are working with fermionic operators, a phase is assigned to each term of this sum with the form (−1)ϱl where l corresponds to the position of the term in the sum. Note that a number is also assigned to the position of each fermionic operator both in the original expression of γx and after expanding it into a sum of terms. Figure 5 illustrates the case of γx, which can be decomposed into a sum of three nonvanishing terms. The central part of the figure shows how each term is constructed by associating a creation to an annihilation operator. Note that other operator pairings are possible, but their expectation value is vanishing due to the fact that the associated operators do not belong to the same subspace (occupied or virtual). The right part of Figure 5 shows how the label sequence of the operators has been rearranged for each term.
\nWick’s theorem applied to single-reference excited state density matrices.
Once the excited state density matrix is developed, one can write a bijection fl(x) = y between the original sequence of operators label (here 1, …, 6) and the one characterizing each term (l = 1 , 2 , 3). The ϱl value is then obtained by counting the number of pairs of projections satisfying
\nin the bijection. For example, for the first term (l = 1), the (x1 = 2, x2 = 5) pair satisfies this condition, because f1(2) = 6 > 3 = f1(5). The evaluation of the phase to be assigned to the first term (l = 1) reported in Figure 5 is fully detailed in Figure 6. The deduction of the phase for l = 2 and 3 is given in Appendix (Figures 7 and 8).
\nIllustration of the evaluation of ϱ1.
Illustration of the evaluation of ϱ2.
Illustration of the evaluation of ϱ3.
For each term in the developed expression of γx, six permutations of its factors are possible without affecting the phase, for the parity of ϱl is guided only by the primary association of creation/annihilation operators characterizing the lth term. According to what precedes, we are now able to write the r × s excited state density matrix element:
\nwith
\nwhere nr is the occupation number of spinorbital r (see Eq. (35) for more details). For ℐ2 we have
\nand for ℐ3,
\nNote that since i and j are corresponding to occupied spinorbitals, writing δjs is equivalent to writing δjsns and is not vanishing only when φs belongs to the occupied subspace. This is also the case for δri. On the other hand, since φa and φb belong to the virtual subspace, writing δsa is equivalent to writing δsa(1 − ns) and is not vanishing only when s is superior to N. Note also that writing δab when dealing with spinorbitals corresponds to δcd when working with matrix elements (see Eqs. (37) and (38)). Therefore, (γx)rs now writes
\nthat is,
\nWe see that the first and second terms belong to the occupied × occupied block, while the third term belongs to the virtual × virtual one. According to this, the excited state density matrix in the canonical space finally writes
\nSubtracting the ground state density matrix taken from Eq. (36) to γx gives γΔ
\nSince TT† and T†T have positive eigenvalues (i.e., they are positive definite), we deduce
\nTherefore, we must have that
\nwhich obviously leads to
\nThis last statement is in agreement with (12). Note that
\nIt follows that ϑx = 1.
\nThis paragraph aims at demonstrating the connection between the NTOs and detachment/attachment paradigms by using the structure of the difference density matrix.
\nTheorem III.1 NTOs are the eigenvectors of the detachment/attachment density matrices.
\nProof. We know from Lemma III.1 that
\nSince TT† and T†T are positive definite, we deduce that the only negative eigenvalues of γΔ belong to the occupied × occupied block, while the positive ones belong to the virtual × virtual block. Since we know how to obtain the eigenvalues of TT† and T†T thanks to Eq. (28), we know that the matrix M diagonalizing the difference density matrix must be the direct sum of O and V:
\nAccording to Eq. (52), we deduce that the eigenvectors of the detachment/attachment density matrices are nothing but the occupied/virtual natural transition orbitals: the Md , a matrices diagonalizing γd , a are
\nFinally, and since we demonstrated that there is a direct relationship between the NTOs and the detachment/attachment, we will use Lemma III.1 and Theorem III.1 to demonstrate that our quantitative analysis is equivalent when derived in the two paradigms when the ground state wave function is a single Slater determinant and the excited state is a normalized linear combination of singly excited Slater determinants.
\nCorollary III.1 The quantum descriptors derived from γΔ can be derived from T\'s eigenvectors and singular values.
\nProof. From Lemma III.1 and Theorem III.1, we can construct the following scheme:
\nFollowing the structure of m deduced in Theorem III.1, we simply find k±
\nBacktransformation and few manipulations lead to
\n\'The joint computation of the NTOs and detachment/attachment density matrices from a single SVD, as a preliminary to the quantum metrics assessment, can even be simplified as
\nNote finally that from Eq. (52) we see that the computation of the detachment/attachment density matrices (hence, the assessment of the topological metrics) can be performed without requiring any matrix diagonalization.
\nWe rigorously detailed the theoretical background related to two methods allowing one to straightforwardly visualize how the absorption or emission of a photon impacts the electronic distribution of any complex molecular system. Based on one of these two methods, we showed that quantitative insights can be easily reached. Subsequently, we bridged the formalism of our two qualitative strategies in the case of single-reference excited states methods solely involving singly excited Slater determinants. Finally, it was demonstrated that in these cases any of the two qualitative methods can be used as a basis for deriving equivalent quantitative results. The totality of the features exposed in this book chapter is currently coded in the Nancy-Ex 2.0 [49] software suite and will be revisited, together with new strategies, in the TÆLES software [50] to be published soon.
\nProf. Xavier Assfeld and Drs. Antonio Monari, Mariachiara Pastore, Benjamin Lasorne, Matthieu Saubanère, and Felix Plasser are gratefully acknowledged for fruitful discussions on the topic. Profs. Istvan Mayer, Anatoliy Luzanov, Martin Head-Gordon, and Andreas Dreuw are also thanked for their extremely inspiring work.
\nFigures 7 and 8 illustrate the evaluation process for the phase of terms 2 and 3 of Wick’s expansion of the excited state density matrix elements in Eq. (41).
\nMost of the time, the spinorbitals themselves are expressed in a basis (often called basis of atomic orbitals, basis of atomic functions, or more simply a basis set) of K functions {ϕ}. K might be superior to L when multiple spinorbitals in the atomic space are linearly dependent. The expression of spinorbitals in the atomic space is called linear combination of atomic orbitals (LCAO), and the pondering coefficients for a given spinorbital are stored in the column of a matrix, C ∈ \n
Note that atomic orbitals are denoted by Greek letters for matrix elements. Since the spinorbitals correspond to columns in C, we can split C into two matrices, \n
The spatial overlap between two atomic functions is also stored into a matrix, S, which has the following elements:
\nAccording to the LCAO expansion, the one-particle reduced density matrix kernel from Eq. (1) can be written in the atomic space for defining the density matrix P in the atomic space
\nIn these conditions, the number of electrons is given by the trace of PS. The central object for our investigations is now P, so that in the atomic space, the difference density matrix writes
\nThe difference density matrix in the atomic space can be diagonalized
\nNote here that δ is a diagonal matrix containing the Δ eigenvalues and should not be confused with the Kronecker delta. The Δ eigenvalues can be sorted according to their sign:
\nand the resulting diagonal matrices can be separately backtransformed to provide the so-called detachment (D) and attachment (A) density matrices and the corresponding charge densities:
\nFrom the detachment and attachment charge densities, one can then compute ϕS, \n
According to the structure of γΔ derived in Lemma III.1, and the connection between density matrices in the canonical and atomic spaces (see Eq. (64)), we can write Δ using T:
\nwhich reduces to
\nThis means that from the transition density matrix one can easily reconstruct the difference density matrix in the atomic space, diagonalize it, and process until the obtention of the quantum metrics is achieved. This is the generalization of Corollary III.1 to the atomic space. We deduce from Eq. (69) that if K = L we have U = SCM.
\nNote finally that in the atomic space, occupied and virtual NTO LCAO coefficients are stored, respectively, in \n
Cultivated forage legumes and range legumes are contributing in sustainable agriculture production apart from nutritional security to the livestock population of India. Cultivated forage legumes and range legumes are also crucial for the nutritional security for mankind as they are integral component for increased availability of animal protein and product which has higher biological value than the plant proteins. The major fodder legumes crops cultivated in India are Medicago sativa, Trifolium alexandrinum, Vigna unguiculata, Mucuna pruriens, Vigna umbellate and range legumes are Stylosanthes spp., Desmanthus virgatus, Clitoria ternatea and others. Among these, Medicago sativa, Trifolium alexandrinum and Vigna unguiculata are more popular among cultivated legumes and Stylosanthes in range legumes because of easy availability of seeds of improved varieties and well developed technology to increase the forage yield and quality. To understand the current status and scope of tropical forage legumes of India for sustaining income through livestock sector, their importance in livestock production, soil health and ecosystem services and diversity among germplasms, evaluation and breeding for improved varieties are discussed in this chapter.
India has the largest livestock population in the world with more than 512 million heads. It supports 56.7% of the world’s buffaloes, 12.5% of the world’s cattle and 20.4% of the world’s small ruminants (sheep and goats) [1]. Besides, the country hosts 17% of the world human population [2]. India is also the leading milk producing country in the world but milk productivity per animal basis is very low. Deficiency in quality of fodder is one of the major reasons for the low animal productivity. Although India is very rich in varied flora and fauna but there is deficiency of quality green fodder to the tune of around 35%. The animals need proper feeding to meet their nutrient requirement to express their full genetic production potential.
In fact, the sustenance of Indian rural agricultural economy depends on crop and animal farming, the two key components of a mixed farming system. Although the contribution of agricultural sector in the Indian economy is steadily declining (from 36.4% in 1982–1983 to 14.1% in 2012–2013), it still contributes employment to over 50% of the work force [3]. The contribution of livestock sector to agriculture GDP has increased to more than 28% and is likely to increase further. In the recent past, the lifestyle of people has been changed with a marked shift in food habits towards milk, milk products and meat leading to increase in demand of livestock products. Economic scenario in animal husbandry is also changing with emergence of peri-urban livestock farming and fodder markets. This indicates the huge pressure on available land, most of which, is used for arable farming and food production.
Forages form the main stay of our animal farming to reduce the competition between human beings and animals due to increasing demand for land and other inputs. Sole feeding of green forages to dairy animals is much cheaper than feeding concentrates with crop residues and has the potential of higher level of milk production. Nearly 65% of the total expenditure of milk production in cows is attributed to the feeding of animals when both concentrates and green fodders are fed as mixed ration. When the milk production is primarily depend upon concentrate based feeding, the cost of feeding towards milk production reaches to 80%, however, in case of forage (legumes) based feeding, it is reduced to only 40% of the total expenditure [4]. Hence, any attempt towards enhancing availability of quality green fodder, and economizing the feed cost would result in better remuneration to livestock farmers/producers.
From an animal perspective, one of the largest benefits provided by legume forages is that they provide a better level of nutrition than cereal forages/grasses at a similar stage of growth, leading to greater forage intake by livestock and increased animal performance. The symbiosis between legumes and Rhizobia provides the plant with an ample supply of N and it is one of the reasons why crude protein (CP) concentrations of legumes are higher than cereals/grasses. In addition to higher concentrations of CP, forage legumes also provide a higher quality protein which may be of equal or greater importance in case of non-ruminant livestock species like equines. Legumes also contain more concentrations of digestible energy than grass/cereal forages due to the structure and development of the legume cell wall. Indeed, the cell wall of legume plants contains fewer hemicelluloses and more pectin compared to that of cereals, thus increasing their digestibility by livestock. However as the cell matures, a secondary cell wall consisting of cellulose and lignin is deposited on the interior of the primary cell wall and reduces the overall availability of the structural carbohydrates in the digestive system. In cereal forages, this phenomenon occurs in all tissues types (i.e. leaves, stems, etc.) while being primarily restricted to the vascular tissues of legume stems. The lignin of non-legumes is also more esterified to hemicelluloses and is more recalcitrant in composition (e.g. higher proportion of syringyl subunits) indicating a more suppressed degradability than in legume species.
Forage legumes is essential for providing a source of biological nitrogen fixation (BNF) for enriching soil fertility (15–40 kg fixed N/ha), reduction in land degradation, disease breaks and for mitigating climate change. Estimating biological N2 fixation of the forage and fodder legumes precisely is challenging because statistics on the areas and productivity of these legumes are highly difficult to obtain. Therefore, N2 fixation values of forage and fodder legumes will be less reliable and also estimates of %Ndfa (nitrogen derived from atmosphere) of fodder legumes in those lands. There are very few reports available on forage legumes—BNF in India. But, all works mainly focused on application of Rhizobium inoculants to fodder legumes and testing their potential for enhancing fodder production (fresh and dry weight, crude protein content, forage quality aspects, nodulation properties, etc.). Appreciable amount of atmospheric N (~60–100%) is fixed by forage legumes annually, fixing up to 380 kg N ha−1 [5]. Quantity of forage residues available for soil incorporation range from 80 to 143 kg N ha−1 and rice cultivated following forage legumes yields the same as rice with 24–50 kg fertilizer N ha−1 [6]. About 100–120 Mha of land is under fodder and forage legumes and green manure crops, with assumed average N2 fixation rates of 200 kg N/ha/year for alfalfa, 150 kg N/ha/year for clovers (Trifolium spp.), 100 kg N/ha/year for other forages and 50 kg N/ha/year for legume-grass pastures [7]. From this assumption, total nitrogen fixation by forage and fodder legumes was calculated at 12 Tg annually (average of about 110 kg N/ha/year). But fixation by legume-grass mixtures is much more variable, ranging from a just a few kilograms to more than 250 kg N ha−1.
In India, area under fodder legumes and grasses is about 8 Mha (Sorghum bicolor—2.6 Mha, Trifolium—1.9 Mha, Medicago—1 Mha, other legume forages—1.9 Mha). Mean N uptake by Trifolium alexandrinum (240–264 kg/ha), Medicago sativa (216–264 kg/ha), Vigna unguiculata (161–181 kg/ha), Sorghum bicolor (128–160 kg/ha), BN hybrid (Pennisetum glaucum × Pennisetum purpureum) and Megathyrsus maximus (288–360 kg/ha), Avena sativa (120–144 kg/ha). Percent nitrogen derived from atmosphere (%Ndfa) is about 0.7 for legumes and 0.1 for cereals/grasses. Annual contribution of BNF by forage and fodder crops in India is about 0.61 Tg/year which is nearly 5% of world BNF of forage and fodder [8]. However, majority of values available for legume N2 fixation were based on shoots and above ground parts only. They did not include the fixed N present in roots, nodules and rhizodeposition in general. Published values for below-ground N as a percentage of the total plant N are 22–68% for the pulse and oilseed legumes, Glycine max, Vicia faba, Cicer arietinum, Vigna radiata, Lupinus albus, Pisum sativum and Cajanus cajan and 34–68% for the pasture/fodder legumes, subterranean clover, white clover and alfalfa [9, 10, 11].
In addition to BNF, many forage legumes have soil-covering growth habit similar to most grasses and deep root system which can contribute to the mitigation of many soil problems, viz., soil conservation by legume cover crops such as Stylosanthes, Crotalaria, Sesbania, Arachis and Desmodium to prevent erosion; contour-hedges with leguminous trees such as Leucaena; rehabilitation of degraded soils by legumes such as Stylosanthes spp., which are deep-rooted and adapted to infertile soils, cycle minerals from deeper soil layers resulting in soil improvement and enhanced concentration of soil organic matter through litter production [12]; the potential of legumes like Stylosanthes hamata can be exploited to ameliorate compacted soil [13]. When used as cover crop forage legumes can also control weed growth, which can be exploited as an attractive alternative to the use of herbicides. They supplement part of N fertilizer application, thus reduce nitrate leaching and eutrophication of water bodies as a consequence of surface runoff as a result of N fertilization in tropical pasture production process. Tropical forage legumes have considerable potential to increase productivity of forage-based livestock systems, while providing benefits to the environment [14]. The environmental benefits, referred as ‘ecosystem services’, comprise positive effects on: soil conservation and soil chemical, physical and biological properties; mitigation of global warming and of groundwater contamination; saving of fossil energy; and rehabilitation of degraded lands [14]. These features make tropical forage legumes particularly valuable at all levels of the system because of their interaction with plants, soil, animals and the atmosphere.
Plant genetic resources (PGR) are the basic platform for screening, improving and developing fine cultivars, and the important materials for biodiversity studies including classification, evolution and origin. Therefore, maintenance of enormous genetic diversity is mandatory for broadening the genetic base of the present and future forage improvement programmes to achieve the national goals. Extensive collection, proper evaluation, in depth study of genetic attributes and cataloging of germplasm is prerequisite for its efficient utilization. According to an estimate there are about 650 genera, 18,000 species of legumes (Leguminosae) in the world. Out of these, only about 30 legumes are used to an appreciable extent for forage production [15]. Information regarding the centre of origin of different forage crops is furnished in Table 1.
Genus | Species | Centre of origin | Distribution |
---|---|---|---|
Atylosia | scarabaeoides | India | |
Centrosema | pubescens | South America | South east Asia, Indonesia and Africa |
Clitoria | ternatea | Tropical America | Tropical and subtropical parts of the world |
Desmanthus | virgatus | Argentina | Florida, throughout the India |
Desmodium | intortum | Central and South America | Throughout the tropical areas of Africa, Australia and new world |
Macroptilium | atropurpureum | Central and South America | Australia, South east Asia, Pacific Islands |
Macroptilium | lathyroides | India | Tropical and subtropical world |
Macrotyloma | spp. | Africa and Asia | Sri Lanka |
Macrotyloma | uniflorum | India | Africa |
Stylosanthes | guianensis | Brazil | West Indies, Africa and Pacific Islands |
Stylosanthes | hamata | Islands of West Indies | Coastal regions of north and south America |
Stylosanthes | humilis | North east Brazil and Venezuela | Tropical parts of world |
Stylosanthes | scabra | Tropical America | Kenya, Brazil and Queensland |
Stylosanthes | seabrana | Brazil | |
Lablab | purpureus | Asia or Africa | India, subtropical areas of Africa, south Asia |
Cyamopsis | tetragonoloba | Africa | India (secondary centre of origin) |
Trifolium | alexandrinum | Syria | Egypt |
Medicago | sativa | Asia Minor | Near East and central Asia |
Centre of origin of different tropical forage legumes.
World-wide, 1500 gene banks are registered in the WIEWS (World Information and Early Warning System on PGR) database [16] and conserve a total of 7.1 million accessions belonging to 53,109 species, including major crops, minor or neglected crop species, as well as trees and wild plants. Out of total germplasms stored, 651,024 accessions belonging to forage crops [17]. Among the international organizations major forage germplasm repositories are International Livestock Research Institute (ILRI), Nairobi, CIAT Columbia; ICARDA Syria; CSIRO-Australia, IGER-UK, USDA-Fort Collins. Forage germplasm diversity in these organizations is part of a Consultative Group of International Agricultural Research (CGIAR) coordinated activity in plant genetic resources. The ILRI Gene bank conserves more than 18 thousand accessions of forages from over 1000 species. This is one of the most diverse collections of forage grasses, legumes and fodder tree species held in any gene bank in the world [18]. CIAT gene bank keeps 35,898 accessions of beans, for 44 species of the genus Phaseolus from 109 countries, and 23,139 forage accessions belonging to 668 different species of grasses and legumes from 72 countries, that have been introduced over the past 30 years [19]. The IITA gene bank holds the world’s largest and most diverse collection of cowpeas, with 15,122 unique samples from 88 countries, representing 70% of African cultivars and nearly half of the global diversity.
Indian sub-continent being one of the world’s mega centres of crop origin and crop plant diversity, represents a wide spectrum of eco-climate and reported diversity of 21 forage legumes genera viz., Desmodium, Lablab, Stylosanthes, Vigna, Macroptelium, Centrosema and browse plants including Leucaena, Sesbania, Albizia, Bauhinia, Cassia, Grewia, etc. (Table 2). Diversity of cultivated and range legumes were collected in form of 3261 diverse germplasm accessions through different indigenous and exotic germplasm collection programme. Collected diversity of forage legumes were evaluated and sources for different biotic and abiotic stress tolerance were identified apart from >50 cultivars in different forage legumes for different geographic regions developed. Crop wild relatives (CWR) being the reservoirs of genes for stress tolerance and quality have been utilized for genetic enhancement of forage legumes. The main centre of diversity for tropical legumes viz., Dolichos, Desmodium, Vigna and Crotalaria is peninsular India and subtropical legumes viz. Teramnus, Atylosia, Pueraria and Mucuna are mainly confined to north eastern region. Likewise, rich genetic wealth for the temperate legumes namely Medicago, Melilotus, Trifolium and Hedysarum is distributed in western Himalayan region [20]. Besides, India possesses enormous diversity of minor and under-utilized fodder species such as Agrostis alba, Desmodium parvifolium, Leptochloa fusca, Potentilla fruticosa, Rhynchosia minima and Salvadora persica [21]. The forage genetic wealth of India distributed in 15 agro-climatic zones has been summarized in Table 2.
S. no. | Agro climatic zone/regions | Subzones/sub regions | Prominent forage genetic resources |
---|---|---|---|
1 | Western Himalayan Region | Jammu & Kashmir, Himachal Pradesh, Uttarakhand Hills | Medicago spp., Arundinella nepalensis, Chrysopogon, Dactylis glomerata, Eleusine, Echinochloa, Festuca, Zea mays, Kikui grass |
2 | Eastern Himalayan Region | Sikkim, Arunachal Pradesh, Meghalaya, Nagaland, Manipur, Tripura, Mizoram, Assam, Jalpaiguri and Cooch Bihar district of West Bengal | Rice bean, maize, range grasses, Brachiaria, broom grass and lablab bean |
3 | Lower Gangetic Plains | Basin plains, central alluvial plains, alluvial coastal plains and Rarh plains | Rice bean, guinea grass, coix and range grasses |
4 | Middle Gangetic Plains | 12 districts of eastern Uttar Pradesh and 27 districts of Bihar plains | Maize, cowpea, rice bean, Pennisetum pedicellatum and coix. |
5 | Upper Gangetic Plains | central, south-western and northern-western Uttar Pradesh | Maize, sorghum, cowpea Senji, Dichanthium, sehima and Heteropogon |
6 | Trans-Gangetic Plains | Punjab, Haryana, Delhi, Chandigarh and Sri Ganganagar district of Rajasthan | Guar, maize, bajra, berseem, lucerne, guinea grass, sorghum and cowpea |
7 | Eastern Plateau and Hills | (i) Sub region of Wainganga, Madhya Pradesh, eastern hills and Orissa inland; (ii)Orissa northern, Madhya Pradesh, eastern hills and plateau; (iii) north and eastern Chota Nagpur hills and plateau; (iv) Chota Nagpur south, West Bengal hills and plateau, and (v) Chhattisgarh and south-western Orissa hills. | Cowpea, rice bean, Pennisetum pedicellatum, guinea grass, Dichanthium spp. and Atylosia |
8 | Central Plateau Hills | 46 districts of Uttar Pradesh, Madhya Pradesh and Rajasthan | Maize, cowpea, rice bean, P. pedicellatum, Coix, Atylosia, sorghum, bajra, guar, Cenchrus, range grasses and legumes |
9 | Western Plateau and Hills | Maharashtra, parts of Madhya Pradesh and one district of Rajasthan | Maize, sorghum, Dichanthium spp. pearl millet, Dichanthium carzacosum, Vicia, cowpea, rice bean, Cenchrus, range grasses and legumes |
10 | Southern Plateau and Hills | 35 districts of Andhra Pradesh, Karnataka and Tamil Nadu | small millet, Heteropogon, Dichanthium sehima and Stylosanthes sp. |
11 | East Coast Plains and Hills | (i) Coastal Orissa (ii) North-Coastal Gujarat (iii) South-Coastal Andhra Pradesh, North-Coastal Tamil Nadu (v) Thanjavur and (vi) South Coastal Tamil Nadu. | cowpea, rice bean, guinea grass, coix, small millet, sorghum, Heteropogon, Dichanthium and Stylosanthes sp. |
12 | West Coast Plains and Hills | Western coast of Tamil Nadu, Kerala, Karnataka, Maharashtra and Goa | Congo, signal grass, Paspalum, panicum, Digitaria, Brachiaria, Iseilemalaxum, Isilemia and Vicia |
13 | Gujarat Plains and Hills | 19 districts of Gujarat | Lucerne, sorghum, small millet, pearl millet, chioori, range grasses and legumes |
14 | Western Dry Region | Nine districts of Rajasthan | Guar, moth, cowpea, sorghum, pearl millet and Cenchrus spp. |
15 | Island Region | Territories of the Andaman and Nicobar Islands and Lakshadweep |
List of prominent forage genetic resources distributed in 15 agro climatic zones of India.
Adopted from Singh et al. [77].
The National Bureau of Plant Genetic Resources (NBPGR) is the nodal agency for characterization, evaluation, maintenance, conservation, documentation and distribution of germplasm resources in India. Currently a total of 4594 accessions of different forage crops including cereal forages (1167), grasses (11,160, range legumes (1443), forage millets (781) and others [85] are being maintained at long term storage (LTS) module of National Gene Bank at NBPGR, New Delhi [22]. Indian Grassland and Fodder Research Institute (IGFRI) is a unique R&D organization in South Asia for sustainable agriculture through quality forage production for improved animal productivity. IGFRI being the National Active Germplasm Sites (NAGS) on forages works with its three regional stations and All India Coordinated Research Project (AICRP) on forage crops with 18 coordinated centres. At present IGFRI maintains more than 8000 accessions of 19 major forage crops including cereal forages, forage legumes, grasses and fodder tree at midterm storage [23].
Tropical forage legumes breeding programmes are associated with certain unique problems. Most of the tropical pasture legumes still possess traits of wild plants that include seed shattering, small seed size, seed dormancy, relatively slow germination rates, etc. In most of the cases we have very little knowledge about the basic biology of the species. Some of the problems include overlapping of vegetative and reproductive growth phases, uneven pod setting, non-synchronous maturity and seed shattering in forage legumes [24]. Inherent heterozygosity as most forage species are cross pollinated. Self-incompatibility limits the extent to which they may be inbred; small floral parts make artificial hybridization tedious; poor seed producers; or produce seed with low viability as well as inherently low seedling vigor and competitive ability. Many forage species produce weak seedlings and stands are not easily established. Strains may perform differently with different systems of grazing management. Persistence of perennial tropical forage legumes is not as a single trait, but rather as a complex of traits dependent on various factors, such as diseases, insects, abiotic stresses, or management stress. Fertility barriers of one sort or another are very common in tropical forage legume breeding viz., berseem [25], owing to the wild nature of the species and inadequate knowledge of inter- or intra-specific variation.
The genus Trifolium from the tribe Trifolieae of the family Leguminosae (Fabaceae) is important for its agricultural value. A few of the 237 species of this large genus have actually been cultivated [26], out of which 25 species are important as cultivated and pasture crops [27]. Egyptian clover or berseem (T. alexandrinum 2n = 16) is commonly cultivated as winter annual in the tropical and subtropical regions. Berseem is popular due to its multicut [4, 5, 6, 7, 8] nature, providing fodder for a long duration (November to May), very high quantum of green fodder (85 t/ha) and better quality of fodder (20% crude protein), high digestibility (up to 65%) and palatability. Berseem was introduced in India from Egypt in 1904, and has been established as one of the best Rabi (winter season) fodder crop in entire North West Zone, Hill Zone and part of Central and Eastern Zone of the country, occupying more than two million hectare [28].
Berseem being an introduced crop in India, the most important drawback in genetic improvement has been the lack of genetic variability [29, 30]. Variability in the existing gene pool has been induced through mutation, polyploidization and inter-specific hybridization. High biomass production potential along with extended growth period and resistance to biotic stresses specially root rot and stem rot have been the main target traits that were to be improved genetically. Different genetic improvement programmes carried out in various research institutes/universities by utilizing breeding approaches like selection, polyploidy and mutation resulted in the development of >15 varieties for different berseem growing regions of India. Inter-specific hybridization have been used to improve resistance to biotic and abiotic stresses and extended length of the vegetative period because genes for wide scale adaptability are widely distributed in several wild species of Trifolium (Table 3). Interspecific hybrids of berseem with Trifolium apertum [31], T. constantinopolitanum [32], T. resupinatum [33] and T. vesiculosum [34] were successfully developed and progenies of interspecific hybrids showed introgression of various desirable traits, including late flowering and resistance to root rot and stem rot diseases.
Species | Chromosome number (2n) | Desirable characters | References |
---|---|---|---|
T. alexandrinum ecotype Mescavi | 2n = 16 | Annual, multicut, highly productive, crude protein, high digestibility and palatability, basal branching | [31] |
T. alexandrinum ecotype Fahli | 2n = 16 | Annual, single cut, self-compatible, stem branching | [78] |
T. alexandrinum ecotype Saidi | 2n = 16 | Annual, 2–3 cut, stem and basal branching | [78] |
T. berytheum | 2n = 16 | Biotic resistance | [79] |
T. salmoneum | 2n = 16 | Biotic resistance | [79] |
T. apertum | 2n = 16 | Annual, profuse basal branching, late flowering, resistance against root rot and stem rot, high protein content | [31, 79] |
T. meironense | 2n = 16 | Biotic resistance | [31] |
T. resupinatum | 2n = 16 | Root rot and stem rot resistance, soil alkalinity tolerance | [33, 80] |
T. constantinopolitanum | 2n = 16 | Profuse basal branching, resistance against root rot and stem rot | [32] |
T. vesiculosum | 2n = 16 | Lateness, disease resistance | [25] |
Desirable characters in berseem ecotypes and wild Trifolium species.
A major breakthrough in berseem breeding in India was achieved through induction of polyploidy. The work on polyploidization of berseem genome was started with the aim to induce greater leaf and stem size [35, 36]. Autotetraploid induced by using colchicine treatment, and selection at tetraploid level resulted in the development of first polyploid variety ‘Pusa Giant’ with more fodder production and good regeneration capacity, uniform and higher yield throughout the season than diploid varieties released for general cultivation in India [37]. Another big achievement in polyploidy breeding was achieved at IGFRI, Jhansi by developing an autotetraploid variety namely ‘Bundel Berseem-3’ through colchiploidy followed by recurrent single plant selection followed with mass selection [28]. Major success in Berseem breeding was achieved by induction of longer duration mutant in Mescavi variety through gamma ray treatment which resulted in ‘BL-22’ a variety released in 1988 for temperate and north west zone; and ‘BL-180’ released in 2006 for cultivation in north-west zone of India [28]. Protocol for in vitro plant regeneration from meristematic tissue and the establishment of regenerable callus culture have been developed in Berseem and related species viz., Trifolium glomeratum, T. apertum, T. resupinatum [38, 39, 40]. Embryo rescue technique has been effectively utilized to overcome the problems of post fertilization barriers in interspecific crosses of berseem with Trifolium apertum, T. constantinopolitanum, T. resupinatum and T. vesiculosum [31, 32, 33, 34]. Recently, SSR based markers were developed for large scale utilization programme in Berseem [30]. Few studies on genetic diversity in Berseem and related Trifolium species were reported by using isozymes [29] and molecular markers [41].
The genus Stylosanthes comprises approximately 40 species, distributed in the tropical [42], subtropical and temperate regions areas of America, Africa, and Southeast Asia. It can be grouped into two subgeneric sections, Stylosanthes and Stylosanthes. Most species are diploid (2n = 20) but polyploid species (2n = 40 and 2n = 60) also exist. Six species, namely Stylosanthes scabra, S. seabrana, S. hamata, S. guianensis, S. humilis and S. viscosa, are predominantly used as fodder legume in humid to semi-arid tropics of India (Table 4). These are very popular and have been widely adapted due to their ability to restore soil fertility, improve soil physical properties, and provide permanent vegetation cover as well as to provide nutritious fodder. The most specific problems associated with Stylosanthes are the limited variations of available germplasm and the susceptibility to anthracnose disease caused by the fungus Colletotrichum gloeosporioides. In the past, mainly five species of Stylosanthes (S. hamata, S. scabra, S. humilis, S. viscosa and S. guianensis) have been introduced primarily from Australia and evaluated at different sites in India [43, 44, 45]. This was in addition to the native perennial S. fruticosa Alston, which is widely distributed throughout the southern peninsular regions [46].
Species | Chromosome | Specific features |
---|---|---|
S. scabra | 2n = 4x = 40 | Adapted in low rainfall areas (325 mm rainfall), suitable for semi-arid areas of Maharashtra, Andhra Pradesh, Karnataka and Tamil Nadu, S. seabrana and S. viscosa are known progenitor of S. scabra |
S. hamata | 2n = 2x = 20 2n = 4x = 40 | Diploid S. hamata and S. humilis are the two progenitors of this species (Curtis et al., 1995), highly palatable, grazing tolerant |
S. viscosa | 2n = 2x = 20 | Early emergence and highly stickiness of the leaves and stems, drought tolerant, grows on poor soils, some resistance to anthracnose, acaricidal properties |
S. humilis | 2n = 2x = 20 | Tolerance for salinity, susceptible to anthracnose, hairs on stems and leaves are some of the important features helpful in identifying the species |
S. guianensis | 2n = 2x = 20 | Suitable for humid and higher rainfall regions, adapted to acid infertile soils, tolerant of Al and Mn |
S. fruticosa | 2n = 4x = 40 | Allotetraploid, drought tolerant |
Important Stylosanthes spp. with specific features.
Testing and evaluation of wide germplasms carried out at IGFRI on acid and saline soil which contribute major part of the soils of India, indicated better adaptation of S. hamata and S. seabrana lines over other species in salinity. The potential of S. seabrana for tropical and subtropical regions of the country with clay and heavy soils, cool winters and distinct wet-dry seasonal conditions directed the use of this species in developing new breeding approach. The one could be based on the finding that it is the second progenitor of S. scabra which in turn elucidated the evolution of one of the most important Stylosanthes species, S. scabra may lead to important impacts on the efforts of improving S. scabra [47]. It may be possible to artificially synthesize S. scabra using pre-selected S. viscosa and S. seabrana accessions [48]. These artificial S. scabra genotypes could be used directly or more likely, be used in breeding programs. By doing so the genetic variation existing in the two diploid progenitor species would become available in improving the allotetraploid S. scabra. So far developed map and linked markers with anthracnose resistance also provide the opportunity to use them after converting them in sequence tagged sites (STS) or sequence characterized amplified region (SCAR) and then using them in direct breeding programs.
Genus Medicago is one of the oldest forage legume comprising 60 perennial and 35 annual species, distributed mainly around the Mediterranean basin, cultivated throughout the world in diverse environments ranging both temperate and tropical environments [49]. It is generally agreed that the basic chromosome number for the genus Medicago are x = 7 and x = 8. Its ploidy varies from diploid (2n = 16) to polyploid (2n = 32, 48, 64). Perennial species are mainly tetraploids (2n = 4x = 32) and allogamous, however diploid (2n = 2x = 16) and hexaploid (2n = 6x = 48) cytotypes have also been reported [50]. Medicago sativa (alfalfa or lucerne) is widely cultivated as the most important forage legume in the temperate areas of the world. Lucerne is native to South West Asia as indicated by occurrence of wild types in the Cancasus and in mountainous region of Afghanistan, Iran. M. sativa complex, comprises of several members at the same ploidy level e.g., M. falcata, M. media and M. glutinosa, which freely intercross, without any hybrid sterility in the F1 or later generations [51]. In India, it is grown in Maharashtra, Gujarat, Andhra Pradesh, Karnataka, Tamil Nadu, Haryana, Madhya Pradesh, Rajasthan, Punjab. The major breeding objectives in the crop include vigorous tall growing plants, better branching, quick regeneration, and balance between seed and forage yield and persistence.
Genetic resources for alfalfa improvement are limited and restricted to the M. sativa complex but tolerant sources for biotic and abiotic constraints are lacking in the complex [52]. The annual and perennial species of the genus Medicago are the reservoir of several useful agronomic traits, including disease and insect resistance and potential salt and drought tolerance having direct implication in cultivated alfalfa improvement (Table 5). Most of the lucerne cultivars grown in the country and worldwide are susceptible to many diseases and insect pests and the most serious constraint is the alfalfa weevil (Hypera postica Gyll.) [53]. Resistance to weevil has been reported in several annual species such as M. scutellata, M. prostrata, M. turbinata and M. intertexta [54, 55, 56, 57]. Genes conferring resistance to aphid have been identified in M. rugosa, M. scutellata and M. littoralis [58]. Similarly, three woody species viz. M. arborea, M. strasseri and M. citrine of the section Dendrotelis have been reported as excellent sources for incorporating drought and salt tolerance in M. sativa [59, 60, 61]. However, due to post fertilization barrier, interspecific hybridization is difficult, so we may need to use biotechnological tools like ovule-embryo culture and electroporation.
Species | Annual/perennial | Chromosome number (2n) | Distribution | Desirable traits | References |
---|---|---|---|---|---|
M. dzhawakhetica Bordz. | Perennial | 32 | Western Mediterranean region | Cold tolerance and resistance to Phoma medicaginis | [81] |
M. suffruticosa Ram. | Perennial | — | — | Resistance to Phoma medicaginis, deep taproot system and high palatability | [81] |
M. cancellata M.B. | Perennial | 48 | Russia | Resistance to Stemphyllium leaf spot | [82] |
M. prostrata Jacq. | Perennial | Resistance to alfalfa weevil and potato leafhopper | [54] | ||
M. scutellata (L.) Miller | Annual | 30 | Mediterranean Basin, Southern Ukraine | High biomass production, Resistance to alfalfa weevil and aphid | [83] |
M. turbinata (L.) All. | Annual | — | Mediterranean Basin | Resistance to alfalfa weevil | [54, 56] |
M. intertexta (L.) Miller | Annual | 16 | West Mediterranean Basin | Resistance to alfalfa weevil | [54, 57] |
M. rugosa Desr. | Annual | 30 | Mediterranean Basin | Resistance to aphid | [58] |
M. littoralis Rohde ex Lois | Annual | — | Mediterranean Basin, East Europe, Caucasus | Resistance to aphid | [58, 83] |
M. polymorpha L | Annual | 14 | Europe, North Africa, Middle East, Ukraine, Georgia, Central Asia | Plant height, high seed production potential | [83] |
M. lupulina L | Annual | Excellent species for sustainable agriculture, reported to improve soil health, reduce diseases and save moisture | |||
M. arborea Hutch. | Perennial | 32 | Mediterranean region | Woody species, ornamental value, drought and salt tolerant | [59, 61] |
M. strasseri Greuter et al. | Perennial | 32 | Crete Iceland | Woody species, drought and salt tolerance | [60] |
Medicago citrine (Font Quer) Greuter | Perennial | 48 | Balearic Islands | Highly drought and salt tolerant species within the section Dendrotelis | [84, 85] |
M. truncatula Gaertner | Annual | 16 | Mediterranean Basin, East Europe, Russia | Genes possessing broad spectrum resistance to anthracnose, stay green genes | [86] |
Annual and perennial Medicago species and their desirable characters.
Inter specific hybrids of M. sativa with some of the perennial species viz. M. cancellata, M. glomerata, M. papillosa, M. prostrata, M. rhodopea and M. saxatilis have been recovered by conventional crosses [51]. However, pollen and embryological studies demonstrated that there exist strong post fertilization barriers for recovering hybrids between M. sativa and annual species [62]. Utilizing embryo culture and fertilized pod culture techniques interspecific hybrids were obtained between M. sativa and many other annual species however, no hybrids were produced between M. sativa and weevil resistant M. scutellata [63, 64]. Bauchan and Elgin [65] reported chromosomal incompatibility and presence of two SAT chromosomes in M. scutellata as the major barriers for getting interspecific hybrids between M. sativa and M. scutellata. Utilizing protoplast fusion technique S1 plants were obtained between M. sativa and M. rugosa and it was confirmed by genomic in situ hybridization (GISH) that small portions of M. rugosa chromosomes were present in the hybrid however, it is not clear that in which chromosome the resistance genes are present [50].
A lot of molecular information has been generated across species. However, information from M. truncatula on marker-trait association is unlikely to be exploitable in lucerne, considering the large differences between annual and perennial [66]; in addition to the differences due to the ploidy level which may further contribute to the inconsistent genetic control of some morpho-physiological traits between the two species [67]. Some breeding goals such as region-specific adaptation; drought-tolerance; improvement for forage quality should be considered [68]. Attempts have been made to produce transgenic alfalfa containing fungal chitinase gene for resistance against fungal pathogens [69], tolerance to abiotic stresses such as salt and cold [70, 71], improved forage quality [72], and sulfur-containing amino acids [73], value addition by making it an edible forage vaccine [74]. In recent years the breeding strategies for Lucerne are more towards utilizing potential of polycross methods followed with phenotypic selection. It has resulted in development of a few cultivars in recent years. The future strategies should include development of cold and drought hardy lucerne with degree of persistence for pasture and meadows, increasing genetic base, high seed production, stress tolerance, diseases and pest resistance etc.
Cowpea (2n = 2x = 22, genome size = 620 Mb) also known as ‘black eye pea’ or ‘hungry-season crop’ is an annual food and forage crop mostly grown throughout the semi-arid tropics in parts of Asia, Africa, Southern Europe, Southern United States, and Central and South America (Singh 2005). It can be grown throughout the year due to its short duration and fast growing nature. It is suitable for inter, mixed and relay cropping system. Cultivated cowpea, which is in subspecies unguiculata, is divided into five cultivar groups namely Unguiculata, Sesquipedalis (yard-long-bean), Textilis, Biflora and Melanophthalmus [75]. The commonly cultivated cowpea belongs to cultivar group Unguiculata the most widespread and economically important group of the species. They are pulse and vegetable and forage types. Other cultivar group Biflora also known as ‘catjang cowpea’ mainly cultivated in South Asia (India, Sri Lanka) as a pulse or as forage for hay and silage, and as a green manure crop. In Australia and Asia cowpea is primarily a fodder crop, but is also used for green manure or as a cover crop [76]. In India, the crop is cultivated around 6.5 lakh ha with 3 lakh as fodder crop in Rajasthan, Gujarat, Maharashtra, Karnataka and Tamil Nadu [24].
Cowpea was first introduced to India 1000–1500 years ago and now Indian-subcontinent appears to be a secondary centre of diversity. In India a large numbers of varieties for vegetable, pulse and fodder purpose have been developed. The breeding objectives have focused around developing lines with terminal drought tolerance, early maturity, erect growth to fit in cropping systems and enabling improved radiation use efficiency, high harvest index and resistance to diseases. The desirable traits in forage cowpea varieties are leafiness with indeterminate growth to get green fodder for a longer period. International Institute of Tropical Agriculture (IITA) has developed several dual purpose cultivars of cowpea with high grain and biomass yields and erects habit for intercropping/mixed farming purposes. In future development of cowpea lines against various forms of root-knot nematode, cowpea aphids and Fusarium wilt, is required. Further, development of transgenic cowpea lines with resistance to major insect pests can also be a breakthrough in cowpea breeding.
Tropical forage legumes were promoted in the past with the major focus on livestock production in India. This has led to a substantial decrease in research on tropical forage legumes. In view of current climate change problems and environmental concerns, research on forage legumes should be resumed with adequate funding support at national and international levels. Newer biotic and abiotic stress tolerant varieties should be developed for the changing environmental conditions. Forage legumes have potential to contribute significantly to environment-friendly agricultural land use and sustainable livestock production in the tropics.
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Without sacrificing the quality of carefully edited and produced peer-reviewed content, Compacts are published as part of IntechOpen’s book collection but on faster schedules, typically 4-6 weeks after acceptance. With an average of 132,000 visitors per week, publishing in Compacts guarantees high visibility and international content sharing. As a fully Open Access publisher, the utilization of a CC BY NC 4.0 license means that other researchers will never have to pay permission fees and can adapt, use, and further build upon the material published in Compacts, eliminating any barriers to the further development of scientific research.
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