Peanut Growth and Development

Growth Requirements.

Peanut is a highly nutritious and high-valued crop worldwide; but achieving competitive yield and quality is often challenged by biotic and abiotic stresses as well as relatively high production requirements compared with other crops. To successfully address these challenges, it is important to understand peanut physiology and its essential requirements for growth and development.

Soil requirements:

It is generally accepted that peanut grows and produces best on light-textured soils with a pH between 6.0 and 6.5 (USDA-ARS, 2005; Putnam et al., 2014). Texture is an important soil physical property affecting the movement of water, nutrients, heat, and air with important effects on root development, crop growth and ultimately yield (Juma, 1993; Wu et al., 2011a). Because peanut fruits develop in the ground, soil texture is even more critical for peanut compared with other crops (Adams et al., 1993; Jackson et al., 2011). Root dry matter accumulation and distribution were greater in sandy and loam soils than clayey soils; and the largest kernels along with increased pod number resulted from peanut grown in sandy or loam than clay soil (Jia et al., 2013). However, enhanced crop establishment, more uniform stands, and improved seedling growth were observed on clayey soil and not sandy or silty soils (Zerbato et al., 2017). Zhao et al. (2015) compared peanut growth and yield on three soil textures at pH between 5.9 and 6.1, i.e. sandy soil with particle distribution of 9% <0.001 mm and 65 % 1-0.05 mm; loam with particle distribution of 19% <0.001 and 23% 1-0.05 mm; and clay with particle distribution of 45% <0.001 and 9% 1-0.05 mm. They observed significant effects of soil texture on the dry matter accumulation of stems, leaves and pods; number of flowers per plant; and dynamic changes in peg number through the growing season. For example, dry matter accumulation of stems and leaves was greatest at 47 days after beginning flower on all soil types; but on sandy and loam soils, dry matter accumulation was approximately 15% over that on clay. Similarly, pod weight was 19% less on clay at 70 days from beginning flower, probably because pod number was consistently less during 27 to 57 days after beginning flower on clay. Finally, on clay soil the number of flowers was consistently lower than on sandy and loam soils (Zhao et al., 2015). This and similar studies (Zheng et al., 2012) clearly show that understanding the relationship between peanut physiology and soil texture is essential for increasing yield and providing insights on moving towards reduced tillage practices in peanut production. Indeed, reducing the tillage to lower production costs is a tempting option currently practiced by many peanut growers in the USA. In addition to the apparent economic advantage, reduced tillage positively impacts water runoff and infiltration, and soil physical properties (Rowland et al., 2007). The main concerns with reduced tillage, however, are soil compaction, persistence of stubble from the previous crop, and inadequate soil bed-formation that could reverse the benefits of soil properties and decrease peanut yield (Jordan et al., 2003). For example, Jackson et al. (2011) showed greater yield when planting on raised vs. flat beds, depending on whether the soil was coarse or fine textured. Ripping and bedding along with conventional till of fine-textured soils reduced compaction and digging losses, and increased yield. Others (Rowland et al., 2007) were concerned with the inability of peanut gynophores to penetrate cover crop residue and produce pegs. They demonstrated that the combination of strip tillage and subsurface drip irrigation produced half the number of pods per plant compared with conventional tillage. Finally, sandy and coarse-textured soils may be preferred in peanut production. Still, they have less water holding capacity, and the water deficit can easily develop in absence of sufficient rainfall or irrigation. Indeed, more research is needed to elucidate how soil physical properties affect peanut productivity to develop more sustainable production systems in the future.

Water requirements

To grow and produce profitable yields, peanut needs a reliable water supply throughout the growing season. Nevertheless, 60% of global production occurs in semi-arid tropics of Asia and Africa, where these conditions are hardly met (Mortley et al., 2000). Even though considered resilient to water deficit, peanuts have relatively large water requirements, and yield and quality may be affected by erratic rainfall amounts and distribution (Singh et al., 1999; Halilou et al., 2015). Globally, only 16.2% of peanut acreage is irrigated, while the percentage of irrigated area is 49, 31, and 20% for cotton, wheat, and corn globally (Portmann et al., 2010). In the USA, 35% of peanut acreage is irrigated, with differences among growing regions, i.e., in Texas 95% of peanut is irrigated. In comparison, the Virginia-Carolina (VC) region only has 15% irrigation availability (USDA/NASS, 2019).

In a semi-humid environment, data from the Peanut Variety and Quality Evaluation (PVQE) replicated trials in Virginia and North Carolina showed that peanuts produced 6399 kg ha^-1 pod yield under 574 mm rainfall and irrigation and yield decreased 8 kg ha^-1 for each mm water below 574 mm (Fig. 1). This dataset is based on 30 Virginia market type genotypes (yields are mean of the 30 genotypes) grown under seven year-location environments in 2009 and 2010 (Balota, 2010; Balota, 2011). Not just yield, but a good grade requires adequate amounts of water for maximum quality and economic value. For example, Balota and Phipps (2013) compared the yield and grade of nine Virginia and runner peanut cultivars grown under rainfed conditions at the Tidewater Agricultural Research and Extension Center (TAREC), Suffolk, VA, in 2009 and 2010. In 2009, rainfall in Jun, Jul, and Aug totaled 388 mm while in 2010 only 14 mm occurred during the same time frame. Under this condition, pod yield was reduced by 26 % and economic value by 33 %. The reduction of the economic value was related to the reduction of sound mature kernels (SMK), extra-large kernels (ELK) and total sound mature kernels (TSMK), and increases of damaged kernels (DK) in a dry versus a wet year in the absence of irrigation. Similar decreases of SMK and TSMK were observed on runner peanut in Georgia when water was withheld for 3 weeks (Dang et al., 2013).

Figure 1. Dependence of peanut yield on the amount of water received from planting to maturity. Each data point represents an average of 30 Virginia market-type varieties tested in replicated trials of the Peanut Variety and Quality Evaluation (PVQE) test in Virginia and North Carolina at four locations in 2009 and three locations in 2010.

In Texas, in a semi-arid environment, Rowland et al. (2012a) showed similar water requirements for runner peanut, indicating that less than 550 mm of total rainfall and irrigation reduced yield and crop value significantly. In this study, the highest yields and values occurred when more than 600 mm of water was supplied to the crop; however, the authors also suggested that less than 600 mm can achieve similar economic value if irrigation is cut to 50 or 75% from the full norm at early growth stages, followed by full irrigation during late growth stages. This was called “primed acclimation” and was proposed as a new irrigation strategy in semi-arid regions conducive to acceptable peanut yields with less water (Vincent et al., 2020). Primed acclimation is based on translational stress memory, i.e. changes in gene expression and function over repeated stress events, and priming, i.e. a stress event may improve the physiological function under subsequent stress cycles, two relatively new concepts in plant biology (Bruce et al., 2007; Balmer et al., 2015; Hu et al., 2015; Racette et al., 2020). Based on plant physiological adaptations to water limitation that allowed accelerated root and flower production, and improved root to shoot partitioning, water use, and photosynthetic characteristics, primed acclimation was shown to work in peanut and other crops (Rowland et al., 2012a; Rivas et al., 2013; Fleta-Soriano et al., 2015; Racette et al., 2019; Vincent et al., 2019).

The temporal distribution of water is important in peanut production. Even though water is needed mainly during the flowering, pegging, and seed growth stages (Rowland et al., 2012a), good stands also need a reliable continuous water supply. Depending on the growth stage and evaporative demand, peanut requires from 25 mm of water weekly in the early stages to 75 mm at seed filling stage, which is difficult to achieve in the absence of irrigation (Putnam et al., 2014). For example, historical data in Virginia shows that 25 mm of rainfall per week is available during peanut early growth stages. However, 75 mm per week from the beginning seed to physiological maturity is rarely received. The weekly rainfall variation is also greater for later than for early peanut growth stages because of summer versus late spring rainfall patterns. The demand for irrigation and timing can be based on simulations with daily monitored weather data using the Cropping System Model (CSM)-CROPGRO-Peanut. CSM-CROPGRO is a decision support system for agrotechnology transfer (DSSAT) and peanut is one of more than 40 crops that can be modeled using this system (Tsuji et al., 1994; Jones et al., 2003; Hoogenboom et al., 2023).

In the coming era of the “water crisis”, it is estimated that increased global food production, including peanuts, will continue to come from rainfed agriculture in developed and developing countries alike; resilience to water scarcity will have to come from more efficient water use by the new cultivars and innovative production systems that exploit plant adaptive physiological mechanisms to water deficit (Rosegrant et al., 2002; FAO, 2012; Vincent et al., 2019).

Germination requirements

Temperature affects peanut germination, growth, and development, with cardinal temperatures being different for each physiological stage. To germinate, peanut needs a base temperature (T_b), i.e. minimum temperature triggering germination, of 13.3 C; optimum temperature (T_o), i.e. optimum temperature for maximum rate and percent of germination of 25.5 C; and maximum temperature (T_m), i.e. germination ceases at this temperature, of 43 C (Cox and Martin, 1974; Caroca et al., 2016; Grey et al., 2016). Vegetative growth is best at average temperatures ranging from 25 to 30 C. For reproductive growth, somewhat lower temperatures, in the range of 20 to 25 C, are optimum; temperatures over 35 C have a detrimental effect on pollen fertility and pollination and, consequently, yield (Ketring, 1984; Prasad et al., 2003). However, temperature requirements and thresholds vary somewhat with genotype and environment. For example, in Chile, a study including four peanut genotypes showed that T_b for germination ranged from 11.3 to 12.5 C; T_o from 30.5 to 33.4 C, and T_m from 40.8 to 44.9 C (Caroca et al., 2016). In Florida, USA, T_b for the rates of emergence and development to V₂ stage were 11.7 and 9.8 C, respectively, with no significant difference among the six genotypes studied. The thermal time needed to reach 50% germination ranged from 77.2 to 155.2 Cd, and to reach V₂ stage, from 144.6 to 218.9 Cd with significant effects of soil temperature and genotype (Prasad et al., 2006). On a thermal gradient table, the germination pattern was non-linear, but the optimum range was similar to field conditions and varied from 22 to 30 C (Grey et al., 2011). However, these differences are likely related to the definition of germination among these researchers. Grey et al. (2016) found that the thermal time to reach max rate for germination and 80% germination, as indicators of seed vigor, were dependent upon genotype, i.e. Georgia Green had greater vigor than Florida-07 across experiments. Germination depended on the year in which seed was produced, at least compared among six years, 2006 to 2012. Seed size was important, and larger seeded cultivars had less seed vigor than smaller-seeded cultivars. In addition to genetic and pre-harvest conditions, post-harvest factors including seed age, i.e. the number of years seed was in storage, contribute to reduced germination and seed vigor, as well as subsequent plant growth. Among the post-harvest factors, seed moisture content, pest and mechanical damage, and ambient storage temperature and relative humidity are the most important (Morton et al., 2008; Isleib et al., 2014).

Factors Affecting Seed Quality, Germination, and Early Growth

Germination begins with water absorption, imbibition, and activation of enzymes needed to degrade seed compounds used for hypocotyl elongation, radicle extension, and cotyledonary leaf opening (Gonnety et al., 2007; Wang et al., 2018). This period is characterized by intense uptake of oxygen and production of carbon dioxide (Prasad et al., 1969; Raymond et al., 1985). Often, “natural anaerobiosis” can develop when the need for oxygen exceeds the supply even under adequate aeration, resulting in accumulation of ethanol, lactate, acid phosphatase enzymes, and cytokinin levels that reach maximum at 5 days after germination initiation (Leblova et al., 1974; Swamy and Sandhyarani, 1986; Gonnety et al., 2007).

On the first day of germination, the most notable change is water content increases up to 65% of the seed weight (Li et al., 2014). Sugars and fatty acids in cotyledons provide the initial energy needed to begin synthesis of DNA, RNA, and new proteins and enzymes ( et al., 2013). Hydrolysis of storage proteins generates free amino acids such as methionine and proline with maximum production at approximately 8 days after imbibition (Isola and Franzoni, 2000). Enzyme and mitochondrial activity are also maximal at approximately 8 days after imbibition (Cherry, 1963) when storage lipids are converted into sugars, which are transported to the growing embryo (Castelfranco et al., 1969). During germination, the cotyledons can lose 60% of their total dry weight and 70% of the proteins which are transferred as amino acids to the forming embryo (Ruiz et al., 2013). Biotic and abiotic factors such as water, temperature, soil mineral and microbial composition, and post-harvest storage conditions can affect germination (Ruiz et al., 2013).

Water availability plays a major role in germination, with larger seeded peanuts needing more water for imbibition than the smaller seeded cultivars (Pallas et al., 1977). Depending on soil water availability, temperature can affect germination differently, for example, hot and dry soils may extend dormancy (Alpert, 2005; Brevedan et al., 2013). Excessive soil moisture, on the other hand, decreases germination rate because of oxygen deprivation (Brevedan et al., 2013).

Seeds drier than 1% may experience loss of viability from altered plasma membrane and electrolyte leakage (Retamal et al., 1990; Walters and Engels, 1998). For overly dry seeds, desiccation signals the production of regulatory molecules inducing dormancy as a survival mechanism (Buitink, 2007). However, once germination begins, the ability of seed to survive desiccation through dormancy is lost and the radicle does not have desiccation tolerance (Hong and Ellis, 1992; Silva et al., 2017).

Offspring of stress-exposed parents can become more tolerant to a particular stress through transgenerational stress memory (Iwasaki and Paszkowski, 2014). In this way, seeds of drought-stressed peanut parents belonging to hypogaea and fastigiata subspecies germinated better under water deficit than seeds from non-stressed parents (Racette et al., 2019). However, stress memory did not apply to peanut seeds previously grown in saline soils. Specifically, saline-grown seed resulted in germination decrease to 57% from 82%. A decrease in germination velocity from 70% to 33% was observed for saline-grown seed vs non-saline seed (Meena and Yadav, 2018).

Increased salinity of soils and irrigation water are becoming major concerns for global crop production (Meena et al., 2016) with negative effects on seed germination, emergence, and early plant growth and survival (Demir and Mavi, 2008; Yuan et al., 2010; Chakraborty et al., 2019). Salt binds water molecules, reducing water availability to the plants, negatively affecting growth. While mechanisms of cellular and subcellular protection can be triggered by increased salt concentrations of soil water, i.e. accumulation of osmotic protectants, salinity applied to the parent plant has negative effects on peanut seed quality (Isola and Franzoni, 2000; Girija et al., 2002; Lim et al., 2012; Meena et al., 2016).

The temperature of the soil at planting is a dominant factor for the onset and rate of seed germination. In standard tests, germination rate increases linearly with temperature from T_b to T_o and levels off or decreases after T_m is reached (Roberts, 1988). For example, in field trials, peanut emergence rate was 50 to 70% at 14 C and increased to 85% when temperature was 25 C, then leveled off or decreased as soil temperature increased to 32 C (Prasad et al., 2006). The temperature effect is related to the enzymatic activity needed to produce carbohydrates from fatty acids via β-oxidation in the germinating seed (Bagley et al., 1963; Ketring, 1979). It is also related to the regulation of genes encoding membrane and organelle protection under cold and dehydration from freeze (Luster, 1985; Tang et al., 2011). Regions of the USA peanut belt like Virginia, Carolinas, Florida, Georgia, and Western Texas, and other global regions such as Pakistan, Northern India and Indonesia are prone to sub-optimal temperatures for peanut germination and emergence (Prasad et al., 2006).

Mineral and microbial composition of seed and soil affect successful germination. Peanut germination is dependent upon calcium (Ca) and boron (B) content in the seed (Harris and Gilman, 1957; Adams and Hartzog, 1979; Guerke, 2005). Optimum germination of runner peanut, i.e. 95% germination, occurred when seed Ca concentration exceeded 600 mg kg^-1 (Howe et al., 2012), but this can vary from a minimum of 450 mg kg^-1, which has been recommended for runner and Virginia-type peanut (Spears, 2000; Spears, 2002), to over 700 mg kg^-1 (Guerke, 2005). Ca is diffused into the soil solution and then into the pods during pod and seed maturation (Beasley et al., 1989). When seed B was less than 13 mg kg^-1, B deficiency developed causing decay of the cotyledons and seed discoloration and malformation between the two cotyledons called “hollow heart” (Harris and Gilman, 1957; Netsangtip et al., 1987). Because sandy soils are often deficient in Ca and B, the application of gypsum (CaSO₄) and elemental B are essential in peanut production for quality seed. Current production guides in Virginia, Carolinas, and the SE peanut region recommend application of 300 kg Ca ha^-1 when soil Ca content is less than 670 kg ha^-1 for Virginia and 450 kg ha^-1 for runner types. Recommendations for B require application of 0.30 to 0.60 kg B ha^-1 when soil test indicates less than 0.2 kg ha^-1 available B, even though relatively new research failed to identify a need for B under current production systems (Benton, 2016). There is increasing evidence that zinc (Zn) is becoming deficient in soils worldwide (Aboyeji et al., 2019). Like Ca and B, Zn is essential for the seed development of many crops, including peanuts (Aboyeji et al., 2019; Rahman et al., 2019).

Specific soil pathogens and residual chemical compounds from previous crops can reduce germination. For example, peanut germination was reduced when sown after sorghum while sorghum roots decomposed (Sène et al., 2000). To prevent infection and improve germination, seed is usually treated with commercial fungicides. Alternatively, naturally produced phytoalexins and polyphenolic antioxidants by germinating seed, or induced by seed microbial colonization with Rhizopus oligoporus, showed a positive effect on peanut germination (Bell, 1984; Cevallos-Casals and Cisnerose-Zevallos, 2010; Sobolev et al., 2010; Wu et al., 2011b; Ahuja et al., 2012; Gan et al., 2017).

Finally, post-harvest storage conditions can affect seed quality (Coffelt et al., 1979). For example, excess rainfall close to harvest can induce sprouting. Sprouted seeds have low nutritional quality and are rejected by food processors; such seed cannot be stored for planting the following year (Vishwakarma et al., 2016). Seed moisture, and storage temperature and humidity can alter germination of peanut (Steele, 1983). Usually, farmer-stock peanuts are dried to 8 to 10% seed moisture for short time storage, e.g. six months, with no significant effect on seed quality and fungal contamination (Smith et al., 1989). In this way, seed can be stored up to one year at ambient temperature of 13 C and 55 to 70% relative humidity and maintain their germination and vigor potential (Butts et al., 2017; Weaver et al., 2021). Reducing seed moisture below 6% appears to allow for higher storage temperature and humidity without effecting seed vigor (HongYan et al., 1997; Sastry et al., 2007; Shinde et al., 2012).

Physiological Growth Stages

As a day-neutral plant, peanut growth and development are not affected by day length; however, peanut needs approximately 2200 Cd growing degree-days (GDD) or approximately 90 to 150 days after planting (DAP) to mature. Boote (1982) developed growth stage descriptions for peanut based on vegetative (V) and reproductive (R) observable events. Except for VE and V0, which were based on the appearance of the cotyledonary leaves aboveground, V stages were numbered from V-1 to V-(N) based on the developed nodes on the main axis and unfolding of the tetrafoliolate leaflets from the first to N^th tetrafoliolate. Eight R stages were identified, with R1 being beginning bloom, i.e. one open flower at any node on the plant; R2 beginning peg, i.e. one visible gynophore on the plant; R3 beginning pod, i.e. one peg swollen belowground; R4 full pod, i.e. one fully-expanded pod; R5 beginning seed, i.e. one seed with visible growth of cotyledon when cross-sectioned; R6 full seed, i.e. one pod with entire cavity filled by seed when fresh; R7 beginning maturity, i.e. one pod showing blotching of inner pericarp or testa; R8 harvest maturity, i.e. 75% of all developed pods have testa or pericarp coloration; and R9 over-mature pod, i.e. one undamaged pod showing orange-tan coloration of the testa or natural peg deterioration (Table 1; Fig. 2) (Boote, 1982). To reach each growth stage, specific thermal time or GDD is needed, and this is dependent upon the peanut type, cultivar, and environment. Runner type peanuts reach R8 in over 160 d after planting and spanish-type in approximately 140 to 145 d in West Texas; whereas in South Texas, spanish-type matured in only 110 to 135 d (Smith and Simpson, 1995; Lopez et al., 2003; Burrow et al., 2014b). The new runner ‘Tamrun OL12’ and spanish ‘Schubert’ peanuts developed in Texas reach R8 two weeks sooner than older cultivars (Burrow et al., 2014a, b); the new Virginia type cultivars developed for the VC reach physiological maturity in approximately 1800 Cd in comparison with old cultivars at 2500 Cd (Caliskan et al., 2008; Balota and Phipps, 2013; Jordan et al., 2018). Developing early maturing cultivars has become an important goal for breeding programs in the USA, especially in drought-prone environments like Texas, Oklahoma and New Mexico because of limited ground water availability, and in SE and VC regions because late season disease pressure and excessive moisture may reduce yield of late maturing cultivars. In Virginia, runner and Virginia type cultivars required on average 400 Cd or 43 d from planting to reach stage R1₅₀, i.e. 50 percent of the plant population has at least one open flower; and 200 to 300 Cd or 7 to 10 days from R1₅₀, to R2, beginning peg (Balota and Phipps, 2013). This is within the ranges described for runners in Georgia and Florida (Prasad et al., 2001; Rowland et al., 2007). To reach R7, runner and Virginia cultivars grown in Virginia needed 954 Cd and 900 Cd, respectively, from R1. On average, both peanut types, 897 Cd was needed to reach R7 in a humid year and 956 Cd in a dry year (Balota and Phipps, 2013). Similarly, in Georgia, the duration of developmental phases and GDD requirements of ‘Georgia Green’ (Branch et al., 1996) runner peanut were longer under water deficit (Suleiman et al., 2013). Because peanut growth and development is dependent upon cultivar and environment, and direct measurements are time consuming and laborious, simulation models to predict peanut growth, development, and yield have been developed. CSM-CROPGRO-Peanut within DSSAT is one of them (Boote et al., 1989; Suriharn et al., 2008, 2011; Putto et al., 2013). Others, i.e. APSIM (Keating et al., 2003) and CropSyst (CROP System) (Stockle et al., 1994), and Simple Simulation Model (SSM) (Noorhosseini et al., 2018) have also been used for peanut and provided adequate levels of accuracy.

Figure 2. Peanut flower, pegs, and pods at different developmental stages: R1, R2 (2nd & 3rd), R3 (4th & 5th), and R5 (8th & 9th). R7 and R8 are the10th & 11th pods in the image, assuming internal coloration.

Table 1

Peanut growth stage description.

Determining when harvest maturity (R8) is reached is essential for peanut producers. The most frequently used method is the Williams and Drexler (1981) method using “maturity profile boards” (Fig. 3). This method is based on the temporal change of pod mesocarp color as seeds fully develop from R6 to R7 to R8, i.e. mesocarp color changes from white, denoting immature pods, to yellow, orange, brown and black, with brown and black indicating fully mature pods. In this method, the proportion of these colors is determined in a sample of approximately 150-200 pods freshly collected from the field. To reveal the mesocarp, hull exocarp is removed by high pressure washing or sand blasting with glass beads under high pressure washing. Pods are then placed on profile board and grouped by color. Even though different profile boards exist for different peanut types, they all allow projection with sufficient confidence of the optimal harvest maturity based on the proportion of pods within each color group (Fig. 3).

Figure 3. Maturity profile boards for runner (top) and Virginia (bottom) market type peanut used to determine harvest maturity (R8) by the Williams and Drexler (1981) method. Drs. Marshall Lamb, USDA-ARS, and David Jordan, North Carolina State University, are kindly acknowledged for providing the photos for these charts.

Even though hull color is largely accepted by growers of the USA peanut belt, this method has been challenged as subjective, i.e. color assessment and grouping depend on the ability of the operator, and time consuming. Therefore, predictive models have also been developed, along with automated digital analysis of pod mesocarp color to improve the accuracy of the hull method (Colvin et al., 2014; Bindlish et al., 2017). Several methods were developed in the 60’s and 70’s; for some of these, descriptions are available in Sanders et al. (1982). Most models currently used are based on thermal time and cardinal temperatures (T_b and T_m) necessary to achieve R8. Modern approaches using canopy spectral reflectance and peg strength are also underway to offer even more dependable solutions (Carley et al., 2008; Rowland et al., 2008; Sorensen et al., 2015; Monsef et al., 2019; Santos et al., 2021; Santos et al., 2022). Table 2 presents past as well as recently developed maturity predictive models.

Table 2

Methods, equations, and reflectance indices used to estimate peanut maturity.

Morphological Characteristics

Morphological characteristics of crops are important determinants of yield. Because the harvestable peanut crop develops underground, reliable evaluations of crop progress during vegetative growth are difficult for peanut; during early growth stages, evaluations solely rely on the aboveground morphological traits and their association with plant physiology and yield. Even though time consuming and difficult to directly measure, traits such as biomass amount and color, plant height, lateral branching length and number, leaf area, and leaf area index (LAI), are associated with peanut environmental resilience and breeding for these traits has been shown to be superior to breeding for yield alone (Nigam et al., 2005). Nonetheless, remote sensing tools that are now available can provide dynamic and accurate phenotyping of the morphological characteristics of a canopy and, depending on the application, with single plant and leaf detail (Jones and Vaughan, 2010; Tester and Langridge, 2010).

Plant architecture is an important morphological characteristic of crops. Plant height, leaf mass, LAI, i.e. leaf area distribution per unit land area, and leaf orientation relative to solar zenith angle are directly related to the light capture for photosynthesis, growth, and production of all crops including peanut (Radford, 1967; Mane et al., 2017; Zurweller et al., 2018a). Therefore, they are important determinants of yield.

For peanut, in addition to light harvest, plant architecture derived from the growth angle of the lateral shoots or so called “growth habit” is important because the peanut flowers are produced aboveground, but the fruits develop underground at the end of specialized fruiting organs called gynophores or pegs. The gynophores appear after pollination above ground and, after entering in the ground, their tips start to swell to produce the pods and then the seeds. Under drought, hot and dry soil may prevent gynophores from penetrating into the ground and forming pods (Fig. 4). In addition, high soil temperature can scorch the gynophore when the tip touches the soil, restricting seed development.

Figure 4. Elongated gynophores (pegs) on a lateral branch with prostrate growth. In this picture, ‘Titan’ peanut has developed numerous pegs but their entry to the soil is delayed because of dry conditions.

Four distinct growth habits have been identified for domesticated peanut (Fig.5 a, b, c, d) (Coffelt, 1974; Pittman, 1995). A prostrate growth habit is defined by lateral branches that spread on the soil surface while the main stem is easy to see but relatively short (Fig. 5a). A spreading growth habit is when lateral branches are only partially on the ground because their tips are curved upward; the main stem is easy to see, yet relatively short (Fig. 5b). A bunch growth habit is defined as semi-erect branches curved upwards, beginning at the base; the main stem is relatively taller than for the prostrate and spreading growth habits (Fig. 5c). Finally, erect growth habit peanuts have branches that grow straight up from the base and are generally 45º or less from the vertical; with relatively tall main stem (Fig. 5d). Growth habit differences among botanical types of cultivated peanut are apparent beginning at early growth stages (Fig. 6). For example, a study in Virginia in 2019 showed small differences among peanut types for the length of lateral branching, but differences were great for the main stem height (Sarkar, unpublished). In this study, Valencia and Spanish genotypes were taller than Virginia and runner types from 30 to 75 DAP; and at 75 DAP height, lateral branching, and LAI plateaued or started to decrease for all types, while biomass increased vigorously (Fig. 7).

Figure 5. Main types of growth habit of cultivated peanut (from Pittman, 1995). Prostrate (a), spreading (b), bunch (c), and erect (d).

Figure 6. Early season differences for the main stem height and spread of lateral branching for the main types of peanuts grown at the Tidewater AREC in Suffolk, VA. Each type is an average of 2 genotypes and 3 replications (adapted from Sarkar, unpublished).

Figure 7. Development of the leaf area index and above ground biomass for the major peanut types up to 75 days after planting at the Tidewater AREC in Suffolk, VA (adapted from Sarkar, unpublished).

Since 1950, the USA peanut yield has increased approximately 46 kg ha^-1 yr^-1 (USDA-NASS, 2019). This increase is attributed to greater yield potential of newer cultivars with higher seed weight per seed, reproductive efficiency and disease resistance, and improved cultural practices (Coffelt et al., 1989; Isleib et al., 2001; Haro et al., 2013). Nonetheless, in both North and South America, part of the yield increase has also been attributed to shifting the growth habit of the new cultivars from erect growth of the fastigiata types to prostrate growth of the hypogaea descendants (Haro et al., 2013). This shift was associated with increased LAI, aboveground biomass accumulation, interception of photosynthetic active radiation, and harvest index (Haro et al., 2017). The harvest index increase seems to have come from increased weight per seed and number, increased partitioning intensity to pods, and more efficient partitioning of thermal time towards greater seed-filling duration (Duncan et al., 1978; Haro et al., 2015).

Recognized as an important agronomic trait contributing to yield, peanut growth habit has been widely studied to find the “ideotype” of peanut, i.e. peanut with optimum plant architecture that has comprehensive agronomic traits known to positively influence photosynthesis, growth, and yield. In addition, crop architecture has been considered as an important trait to breed for to improve mechanical cultivation, disease resistance and drought tolerance of peanut (Donald, 1968; Butzler et al., 1998). Numerous studies identified that early biomass accumulation and LAI are important characteristics related to peanut tolerance to water deficit stress (Kiniry et al., 2005; Nigam and Aruna, 2007; Arunyanark et al., 2008; Zurweller et al., 2018a; Zurweller et al., 2018b). In these studies, water deficit stress reduced biomass and LAI more for drought susceptible than for drought tolerant cultivars; LAI was positively related to pod yield. The length of main axis and cotyledonary branches was reduced, and taller plants wilted faster than shorter peanuts under soil moisture deficit (Fig. 8) (Reddy et al., 2003; Sarkar et al., 2022). Drought also reduced plant density, light interception by plants, and dry matter accumulation (Pandey et al., 1984; Bell et al., 1993; Collino et al., 2001).

Figure 8. The relationship between peanut height and wilting at the full pod stage (R4) in response to high temperature and soil drying in a sub humid environment. Each point represents an average of three replications for 104 genotypes of the Mini-core peanut collection. Wilting was visually scored on a scale from 0 to 5, i.e. 0 = no visual wilting; 2 = upper branches showing rolled leaves; 3 = upper and mid branches showing rolled leaves; 4 = upper and middle canopy becoming dry; and 5 = entire plant severely dry (Sarkar et al., 2022).

In addition to crop water deficit stress, peanut canopy architecture can affect disease resistance. For example, upright and less dense canopies contributed to plant resistance to soil borne diseases by creating microclimates with reduced opportunities for plant contact with infested soil (Chappell et al., 1995; Hollowell et al., 2008). Upright canopies of Spanish market type peanut cultivars were more resistant than runner and Virginia types to Sclerotinia blight (Coffelt and Porter, 1982; Chappell, et al., 1995; Goldman, et al., 1995). Studies by Blad et al. (1978) compared soybean (Glycine max (L.) Merr.) cultivars with different canopy architectures. These authors found that dense and shorter canopies had the coolest, wettest microclimate and increased severity of white mold (caused by Sclerotinia sclerotiorum (Lib.) de Bary) than cultivars with open canopies and upright growth habits which were warmer, drier, and had a low incidence of white mold. Similarly, treatments supporting or maintaining dense foliar canopies such as irrigation resulted in the highest incidences of stem rot (caused by Sclerotium rolfsii Sacc.) in peanuts (Shew and Beute, 1984).

In other crops, e.g., corn (Zea mays L.), components of plant architecture such as plant height are directly related to yield and proposed as selection tools for increased yield (Freeman et al., 2007; Yin et al., 2011). Phenotyping for plant architecture by direct measurement of its components is tedious and logistically difficult to accomplish in large breeding populations. Fortunately, biomass, LAI, plant height, and lateral branching can be estimated with sufficient accuracy using remote sensing and imaging technologies; research of this type has recently started for peanut (Yang et al., 2017; Sarkar et al., 2020). Alternatively, the development of molecular markers for marker assisted selection (MAS) may offer viable options to improve peanut architectural components contributing to improved yield, and resistance to biotic and abiotic stresses. Numerous recent studies in peanut are geared towards the development of high-density genetic maps to connect molecular markers to plant height, first lateral branching, and total branch numbers (Hake et al., 2017; Kayam et al., 2017; Li et al., 2017; Lv et al., 2018; Wang et al., 2018; Li et al., 2019).

Leaf color and specific leaf area are often associated with tolerance to water deficit stress in peanut via improved transpiration efficiency (TE) and water use efficiency (WUE), i.e. carbon assimilated, or biomass accumulated per unit of water used by or applied to the plants. For example, specific leaf area (SLA), i.e. leaf area divided by leaf weight, and leaf color or relative chlorophyll content determined non-destructively from light transmission through the leaf using a SPAD-502 color meter (Spectrum Technologies, Inc, Aurora, IL) showed good correlations with peanut yield and water use characteristics, i.e. ¹³C discrimination (Δ¹³C), TE, and WUE, across numerous environments (Nageswara Rao and Wright., 1994; Bindu Mathava et al., 2003; Rowland and Lamb, 2005; Upadhyaya, 2005; Sheshshayee et al., 2006; Arunyanark et al., 2008; Nigam and Aruna, 2008; Songsri et al., 2008; Balota et al., 2012a; Chen et al., 2013; Singh et al., 2014). However, in micro-plot experiments in India, under well-watered conditions, yield did not correlate well with TE (Vadez and Ratnakumar, 2016). In general, SPAD was positively, and SLA negatively related to yield (Janila et al., 2015), but not always. In some studies, high rather than small SLA was associated with high yield (Balota et al., 2012a; Suriharn et al., 2011). Because significant diversity in response to water deficit was found in peanut genotypes and for simplicity of measurements, SLA and SPAD were further proposed and used as surrogate traits for breeding for high yield and efficient water use in cultivated peanut (John et al., 2014; Vasanthi et al., 2015). They were also proposed for evaluation of drought tolerance of wild species (Leal-Bertioli et al., 2012), used with crop simulation models to design peanut “ideotypes” (Suriharn et al., 2011), and were further studied to identify potential molecular mechanisms underlying these morphological characteristics (Dang et al., 2013).

Root architecture is essential for the acquisition of water and nutrients, and its adaptation to specific environmental conditions is important for plant growth and development, especially under stressful conditions. For example, peanut genotypes with deeper roots produce relatively greater pod yields under water stress conditions than genotypes with more shallow roots (Rucker et al., 1995; Songsri et al., 2008; Jongrungklang et al., 2011; Jongrungklang et al., 2012). While shifting from erect to prostrate growth habit was clearly related to increased yield potential in peanut, little is known about how this shift affected root morphology; and how root morphology contributed to the current growth in yield (Coffelt et al., 1989; Seaton et al., 1992; Haro et al., 2013). Zurweller et al. (2018a) showed that fastigiata genotypes had greater total root length than hypogaea, but similar root total surface area distribution at 80 cm of soil depth. These authors concluded that more prolific root growth at depth may not necessarily result in an advantage for increased water acquisition and utilization, and higher yield of peanut. However, this conclusion may only reflect the years, location, and genotypes used in this work. Therefore, more research is needed to elucidate the role of root morphological characteristics for peanut yield and performance under environmental stress. Nonetheless, root analysis is more complicated when performed at field scale than with aboveground biomass, and current methods are imprecise, expensive and laborious. At the same time, relying on root analysis in controlled conditions is not an option for breeding programs because the physiological and morphological root adaptations are highly dependent on the environment in which plants grow (Cattivelli et al., 2008; Sinclair, 2011). Identifying new methods for field scale root analysis under conditions where plants are grown seems to be an important benchmark for peanut in order to elucidate the importance of root morphology to yield and quality. New methods using remote sensing and non-invasive techniques such as ground penetrating radar (GPR) hold promise to address this aspect. Used with success to detect and measure coarse roots of trees nondestructively (Barton and Montagu, 2004; Cox et al., 2005), GPR is now being tested for efficacy in detecting fine roots of agricultural crops (Liu et al., 2018), including peanut. For example, the peanut breeding program in Lubbock, TX, is currently working with Dr. Hayes’s group at Texas A&M University to evaluate the GPR efficacy to detect root traits (Burrow, personal communication).

Stress Effect on Peanut Physiology

Water Deficit Stress

Water deficit stress is responsible for more crop loss than any other abiotic or biotic stress. In peanut, recent work is focusing on understanding physiological responses associated with plant dehydration (Borém et al., 2012). Examination of these physiological responses is often done under the context of identifying specific plant traits governing possible tolerance to “drought” conditions. In general, water deficit tolerance refers to crop traits providing adaptations of dehydration avoidance or tolerance strategies to water limitation (Blum, 1988; Pallardy, 2007), while drought refers to the severity and timing of water deficit events occurring during crop development in a given environment. Avoidance is different from tolerance; avoidance can be accomplished by deeper rooting to sustain water uptake, while tolerance is more related to survival via osmotic adjustment. An intermediate strategy can be that of reducing transpiration (whether caused by stomatal response to VPD or naturally low hydraulic conductance) to maintain relatively high turgor and less stress, while forgoing some carbon gain from photosynthesis whereby some yield may be lost.

In the United States, peanut production is primarily in the southeast (GA, FL, AL, MS), southwest (TX, OK, NM), and the Virginia-Carolina (VC) region (VA, NC, SC). The Ogallala aquifer is the primary source of irrigation for production of peanuts, cotton, corn, sorghum, and wheat in the southwestern U.S. This aquifer has been used heavily since the 1950s and, although irrigation efficiency has improved, the current rate of decline in the water table is 0.8 – 1.1 feet per year and it is estimated that the aquifer could be depleted within 30 to 40 years (Paxton, personal communication). Decline in some locales is already reducing the ability to irrigate when plant demand is the highest, during the midsummer months from flowering to mid-pod fill when air temperatures routinely exceed 40 C. For the southeastern U.S. peanut producing region, there are also issues of water scarcity compounded with high growing season temperatures. This region is not inherently thought of as having water resource issues, but because rainfall is typically erratic during the growing season and groundwater supplies are increasingly being diverted to rapidly growing major urban areas, the issues related to water quantity are reaching critical proportions. Similarly, in the VC region, precipitation amount and distribution are erratic, and, unlike the other regions, irrigation is unavailable for over 90% of the peanut growing land. In addition to the obvious implications for yield, a significant concern is that water deficit stress promotes production of the highly carcinogenic aflatoxin mycotoxins (Butts et al., 2023; Williams et al., 2004), which can be minimized by irrigation.

Recent examples abound of abiotic stress affecting U.S. peanut production. Approximately one-third of Texas peanut acreage was not harvested in 2000 and again in 2011 due to drought and heat stress. In Georgia, drought and aflatoxin contamination of the crop resulted in an inability to market large quantities of peanuts in 2002 and 2019. In Virginia, severe drought in 2010 caused a 60% decline of the state average yield. The severe drought in 2006 and 2007 caused a 40% decline in southwest peanut production. On a global scale, the effects of these abiotic stresses on peanut crops are increasingly more severe and will continue to affect directly and indirectly through sparking of new diseases and pest insect cycles as the result of climate change (Prasad et al., 2000; Ramsey, Tack & Balota, 2020; Paredes et al., 2024; Porcuna-Ferrer et al., 2024). Particularly in Africa and Asia, where irrigation water quantity and quality are limited, the effects of water stress are compounded by the effects of high temperature. In these regions, drought tolerant indigenous crops, including peanut, seem to lose their resilience in the face of climate change (Porcuna-Ferrer et al., 2024).

Droughts and high temperatures reduce not just yield but also peanut seed size and quality, with negative effects on crop revenue (Balota, 2020; Ramsey, Tack & Balota, 2020). For example, Balota (2020) showed that a relatively moderate drought reduced sound mature kernel (SMK) content by 7.5% while severe drought depressed SMK by 25%. The SMK is an important grade factor with significant contribution to the revenue, i.e. the higher the SMK the higher the revenue. Similarly, Ramsey et al. (2020) predicted an approximately 11% revenue decline for Virginia-type peanut producers with 1 C temperature increase due to both yield and seed size reduction. These authors examined the varietal improvement as an adaptation strategy to heat and dryland conditions using data from a unique multi-state project, the Peanut Variety and Quality Evaluation (PVQE), collected on Virginia-type peanut accessions for over 35 years in the Virginia-Carolina peanut region of the USA. They concluded that breeding could offset moderate climate warming, but is unlikely to sustain revenue if warming exceeds 1 C. With limited water resources to supply irrigation to peanut crops in the future, solutions must be found to support production and maintain economic sustainability. Recently, progress has been made toward identification of cultivated and diploid peanut accessions that exhibit phenotypic expression of traits associated with drought tolerance that can be used in breeding for resistance (Balota & Isleib, 2020; Bennett et al., 2022; Kumar et al., 2022; Kumar et al., 2024; Massa et al., 2024). Risk management tools have been, also, deployed along with management practices to reduce the effect of drought (Martins et al., 2023; Nboyine et al., 2023), but more research is needed to achieve sustaining yields and nutritional quality of peanuts under increasingly harsh conditions.

A decrease in gas exchange is one of the first physiological responses to drought in peanut, as evident by decreases in transpiration, stomatal conductance, and photosynthesis (Balota et al., 2024; Mohandass et al., 1989; Reddy et al., 2003; Pilon et al., 2018; Zhang et al., 2022; Soba et al., 2024). Ultimately, decreased growth and leaf area, as well as lower photosynthetic efficiency, leads to a decline in net canopy carbon assimilation, directly impacting yield (Reddy et al., 2003; Soba et al., 2024). Thus, of particular interest is the identification of the limiting factors and susceptibility of photosynthetic mechanisms to drought which reduce yield. These factors include stomatal and non-stomatal photosynthetic mechanisms that respond differently to abiotic stress depending on species and severity of the stress (Rucker et al., 1995; Pilon et al., 2018). Additionally, morphological factors such as root architecture, rooting depth, and plant acclimation capacity can contribute directly to plant water status and photosynthetic/source strength (Dang et al., 2024; Puppala et al., 2023; Rowland et al., 2012a; Rowland et al., 2012b). Pilon et al. (2018) addressed stomatal and non-stomatal limitations to carbon assimilation in peanut and showed that while declines in net CO₂ assimilation were driven by decreased stomatal conductance, the primary mechanisms contributing to low photosynthetic rates were non-stomatal (Soba et al., 2024). These non-stomatal limitations included both disruption of electron transport rates through photosystem (PS) II, RuBP regeneration, rubisco carboxylation, and diffusional limitations in the chloroplast (Pilon et al., 2018; Soba et al., 2024). Presumably both the stomatal and non-stomatal mechanisms are affected by and even damaged by low leaf water potential status.

From a plant breeding perspective, research aimed at the discovery and modeling of targeted photosynthetic traits to improve yield and stress tolerance in peanut and other crops; these traits included early biomass accumulation and leaf area index (LAI), ‘chlorophyll stability’, and net assimilation rate (A) (Balota et al., 2024; Bennett et al., 2022; Dang et al., 2024; Zhang et al., 2022; Rucker et al., 1995; Fischer et al., 1998; Kiniry et al., 2005; Nigam and Aruna, 2007; Arunyanark et al., 2008; Balota et al., 2008; Songsri et al., 2008). A comprehensive analysis of traits proposed to improve drought and heat resistance in peanut has been recently published by Puppala et al. (2023). Earlier research showed that LAI and early biomass accumulation had significant effects on peanut yield under stress conditions through improved light interception, radiation use efficiency, higher A, and extended photosynthetic duration (Collino et al., 2001; Kiniry et al., 2005; Arunyanark et al., 2008). This research also emphasized that substantial genetic variation for these traits exists in peanut (Kiniry et al., 2005; Nigam and Aruna, 2007; Arunyanark et al., 2008). In particular, A seemed to be a key trait as its cumulative rate over the growing season is the main driver of biomass and yield. For example, experimental and theoretical analyses suggested that improvement of assimilation in C₃ plants is feasible and can be achieved through increasing photosynthetic rate per unit leaf area (Zhang et al., 2022). Using carbon isotope composition and discrimination (Δ¹³C) chemistry terms, Farquhar and colleagues (1989) developed the theory linking Δ¹³C to A, transpiration efficiency (TE, the ratio of A to transpiration rate), and water use efficiency (WUE, the ratio of biomass to water used to produce that biomass). Since then, numerous studies used Δ¹³C to estimate water use of C₃ plants, including peanut, but this analysis is expensive and low throughput for regular use in breeding (Nageswara Rao and Wright, 1994; Rowland and Lamb, 2005; Zhang et al., 2022). Limited-transpiration (LT) rate at elevated vapor pressure deficit (VPD) and transpiration decrease (TD) early in the soil drying were also proposed as two physiological mechanisms by which soil water could be conserved and used by plants for a longer time in between rain events (Devi et al., 2010).

A fundamental strategy that plants have evolved for regulating water use is altering hydraulic conductance, ultimately influencing transpiration flux. Alterations in transpiration flux can have significant impacts on crop yield because of its impact on CO₂ assimilation for photosynthesis (de Wit, 1958; Passioura, 1996; Blum, 2009). For warm-season C₃ plants like peanut, the general pattern of water use for non-stressed plants is a gradual increase in water use during morning hours peaking at mid-day and declining in the afternoon as leaf water potential decreases (Mohandass et al., 1989). Declining leaf water potential has also been closely related to declines in stomatal conductance ultimately reducing water use (Brodribb and Holbrook, 2003).

Relative humidity (RH), dew, and rain vary greatly across the peanut producing regions of the U.S. For example, RH, the ratio of actual vapor pressure to saturation vapor pressure, depends greatly on temperature. In general, the western hot-semi-arid regions of the U.S. have lower vapor pressures than the hot-humid climate of the southeastern and eastern U.S. Vapor pressure deficit (VPD), i.e. the difference between the amount of actual moisture and how much moisture air can hold, is a measure of how close air is to saturation. For a given environment, VPD is easily derived from RH and temperature, and has spatial and temporal variation through the crop growth cycle. When soil water is unlimited, because the sub-stomatal cavity of the leaf is saturated with water vapor, water diffuses through open stomata outside the leaf under high atmospheric VPD (less moisture in the air). This may result in leaf dehydration, faster soil water depletion, and on-set of water deficit stress.

Sinclair et al. (2017) and others (Turner et al., 1985; Devi et al., 2009; Shekoofa et al., 2014, 2015a, b; Pradhan et al., 2019) capitalized on the response of stomatal conductance to diurnal VPD variation and proposed that LT response to increased midday VPD could be a mechanism of dehydration avoidance. Through this mechanism, plants can conserve soil water at early growth stages for use later in the season to sustain seed filling. The predominant physiological mechanism most likely influencing the LT trait is stomatal regulation. Shekoofa et al. (2015a) documented the LT trait threshold occurring across peanut genotypes was not influenced by relative water content. This indicates that bulk leaf water status or active metabolic processes (abscisic acid transport) is not influencing the LT trait (McAdam and Brodribb, 2014; Sinclair et al., 2017). The current evidence is that LT trait could be a result of particular genotypes having relatively lower hydraulic conductivity (passive metabolic process) (Sinclair et al., 2008; Choudhary et al., 2013; Choudhary et al., 2014; Ocheltree et al., 2014). The general concept is that low hydraulic conducting genotypes under higher VPD have insufficient water flux into leaves to match water loss via transpiration, thus reducing stomatal aperture (stomatal limitation) balancing the water flux in and out of the leaf (Sinclair et al., 2017). Therefore, the presence of LT trait may be associated with resistance factors limiting leaf water transport between xylem and guard cells (Sinclair et al., 2008).

When day/night temperature was maintained at 27/21 C in a growth chamber, Devi et al. (2009) reported the LT trait was present in 9 of the 17 peanut genotypes, i.e. 9 genotypes showed a break of linearity between transpiration and VPD when VPD was greater than 2.2 kPa. Other controlled chamber studies evaluating the impact of temperature on the genotypic LT response concluded that increasing temperature could cause a lack of expression of this trait (Shekoofa et al., 2015a). When the chamber temperature was 31 C, three of the 6 genotypes expressed the LT trait, but at a temperature of 36 C the LT trait was diminished in all genotypes. The lack of response of this trait at warmer temperatures was also observed in a field study (Shekoofa et al., 2015a). The impact of temperature on the expression of this trait seems to be more influential on peanut than C₄ determinate crops of sorghum and maize (Shekoofa et al., 2013; Shekoofa et al., 2015b). Transpiration decrease (TD) in response to soil drying expressed as the fraction transpirable soil water (FTSW) was also investigated as a peanut strategy to conserve soil water at early stages for later use. Devi et al. (2009) studied 17 peanut genotypes and found large variation in the FTSW among the genotypes, i.e. FTSW ranged from 0.22 to 0.71. However, less variability of this trait has been reported for the U.S. commercial peanut cultivars (Devi et al., 2011; Shekoofa et al., 2013). Stomatal sensitivity to soil drying was observed in a field study when comparing genotypes of subspecies fastigiata and hypogaea (Zurweller et al., 2022).

Crop production loss from water deficit stress primarily occurs because of reduction in photosynthesis from stomatal closure limiting CO₂ assimilation (de Witt, 1958; Pilon et al., 2018; Tanner and Sinclair, 1983). Therefore, mechanisms that limit transpiration such as LT and TD must, to some extent, also reduce CO₂ assimilation and may result in counterproductive effects on yield. For peanut, LT and TD were documented with certainty under growth chamber conditions, but evidence of the traits’ expression in the field is limited and their direct contribution to soil water conservation and yield advantage under drought is still to be determined.

Diurnal monitoring of air and canopy temperature allows measurement of canopy temperature depression (CTD), a surrogate for stomatal conductance and transpiration of plants in the field. This technique was used with success for decades in plant breeding for drought and heat tolerance (Ehrler et al., 1978; Blum et al., 1982; Reynolds et al., 1994; Amani et al., 1996; Fischer et al., 1998; Balota et al., 2007). For field grown peanut in a sub humid environment, diurnal CTD variation was monitored with wireless infrared thermometers (Balota et al., 2012a, b). As temperature and VPD increased, transpiration rate estimated from CTD increased from 8:00 to 10:00 EST, i.e., VPD went from 0 to 1.5 kPa during this time; CTD decreased at midday, i.e. from 11:00 to14:00 when VPD went from 2 to 2.5 kPa; then CTD increased again in the afternoon until 15:00 when it started to decline, one hour after VPD began to decline (Fig. 9). A negative linear relationship between transpiration rate and VPD from 11:00 to 14:00 EST for field grown peanut was confirmed by directly measuring stomatal conductance on 8 peanut genotypes with a LI-6400 portable photosynthesis meter (LI-COR Biosciences Inc.) (Hawkins, 2014). This agrees with the classical model that stomata closes when the water content in the sub-stomatal cells declines from intensified water diffusion outside the leaf and reopens when the water content is replenished by hydraulic conductivity (Taiz et al., 2014; Brodribb and McAdam, 2011). Unlike results noted in growth chamber tests, when LT and TD to soil drying were expressed in a handful of peanut lines when temperature was 32 C and even in fewer lines when temperature was 36 C (Shekoofa et al., 2015a; Pradhan et al, 2019), in tests in the field by Balota et al. (2012b) on 30 peanut genotypes, all genotypes partially closed stomata in response to midday VPD increase, regardless of temperature and soil moisture content (Fig. 10). However, by this mechanism of LT, yield variation among genotypes from 4992 to 6758 kg ha^-1 was not explained (Balota, 2010).

Figure 9. Diurnal variation of canopy temperature depression (CTD) as an estimate of transpiration and stomatal conductance response to increased temperature and vapor pressure deficit (VPD) in a sub-humid environment. CTD was calculated as air minus canopy temperature with high values denoting cool canopy due to increased transpiration. Air temperature and VPD are the dashed lines; the continuous lines with symbols represent CTD of three peanut breeding lines in the Peanut Variety and Quality Evaluation tests in 2009 (Balota et al., 2012a, Balota et al.,2012b). Data over 10 days of continuous temperature monitoring in mid-August are averaged.

Figure 10. Response of transpiration to midday (11:00 to 14:00) VPD increase on days with different air temperatures and soil moisture content (Balota et al., 2012a, Balota et al., 2012b). Transpiration was measured with a LI-6400 portable photosynthesis meter (LI-COR Biosciences Inc.) 30 peanut genotypes grown in the field at Tidewater AREC, in Suffolk, VA. Each data point is an average of three replicates. Pre-stress represented a period with 10 d of no significant rain event at the beginning peg stage; stress period was after 20 consecutive days of no rain and peanut was at the beginning pod stage; post-stress was after 35.4 mm of combined rainfall and irrigation in 2009.

Similarly, there is little evidence that growth chamber selected peanut genotypes for LT response to increased VPD or TD with soil drying have yield advantage when grown in the field. For example, in growth chamber testing, Sinclair et al. (2018) found line N12006ol with decreased transpiration rate to soil drying in terms of FTSW, i.e. high FTSW, and speculated that TD may have caused higher yield of N12006ol in low-yield environments in the 2014 PVQE testing. Indeed, in 2014 PVQE trials, line N12006ol had an average yield of 5426 kg ha^-1 across five locations, like the popular cultivar Bailey, but its sister line, N12008olFCSmT, produced 15% more yield than Bailey and N12006ol when grown under the same environmental conditions (Balota et al., 2015). N12008olFCSmT had significantly lower FTSW than N12006ol (Sinclair et al., 2018) but was released as Bailey II in 2017 for its superior yield.

There is no evidence that LT and TD traits expressed early in the season can conserve water for use at later physiological growth stages of seed development under field conditions. In general, growth chamber studies not only provide conditions different than in the field and expressed phenotypes may be different in controlled conditions than in the field, but they disregard the soil-plant-atmosphere relationships, which are the driving force for crop production. For example, in the VC region, the soil is sandy and shallow, and drought stress can develop within 10 to 20 days of no rainfall. Also, at early growth stages, evaporation is more intense than later in the season when plants cover the ground. Under these conditions, field crops in Suffolk, VA, may become more drought stressed than crops in TX in mid-July (Fig. 11). However, some phenotypes can withstand severe field dehydration. The peanut genotype that remains green and turgid plants in Fig. 11, while the entire field is water-stressed, is GP-NC WS 17 (Tallury et al., 2014). This line is an exotic derived line from interspecific hybridization with Arachis cardenasii (Krapov. & W.C. Gregory) with drought and heat tolerance (Tallury et al., 2014). This genotype was screened for LT in growth chambers by Shekoofa et al. (2015a) and did not show the LT trait. However, GP-NC WS 17 showed the greatest stomatal conductance recovery after water deficit stress, greater chlorophyll fluorescence estimated as the ratio of variable to maximum fluorescence (Fv/Fm), and the least membrane injury, when compared with other genotypes in the field (Rosas-Anderson et al., 2014; Singh et al., 2014). Pilon et al. (2018) also documented that non-stomatal factors such as electron transport are important for maintaining relatively high levels of CO₂ assimilation under drought. Similarly, under field conditions when drought was imposed by coverage with rainout shelters, Zhang et al., (2022) showed that some peanut genotypes, i.e. PI 502120 and AU-NPL 17, maintained high photosynthetic and stomatal conductance rates, and produced high yields under drought. The authors called these genotypes “water spenders”, like cultivars of wheat (Triticum aestivum L.) and common bean (Phaseolus vulgaris L.) previously identified with similar responses to drought (Blum et al., 2009; Sanz-Saez et al., 2019). Other cultivars, however, developed an opposite strategy to cope with drought; they closed the stomata early during the onset of drought. These cultivars, i.e. Line-8 and AU16-28, called “water savers” showed lower stomatal conductance but similar yields with “water spenders” (Zhang et al., 2022). The authors concluded that, indeed, water savers did not have a yield advantage over water spenders under field-imposed drought.

Figure 11. Left, a corn field in Suffolk VA, on July 17, 2010, after 20 days of no rainfall. At the right, an entire peanut field (the 2010 Peanut Variety and Quality Evaluation trials) at the same location after 2 months of rainfall absence on Sep 24, 2010. The green plot to the extreme left of the peanut picture is genotype GP-NC WS 17 (Tallury et al., 2014) tested at line SPT 06-07.

Drought tolerance is a complex of morphological and physiological adaptive plant responses including escape, avoidance, and resistance (Blum, 1988). Considering that drought is usually accompanied by high temperatures, the complexity may become overwhelming and unrealistic to address with single traits identifiable through low-throughput techniques in controlled conditions. For agricultural production, adaptive responses must have a yield benefit under drought, while biotic resistance needs not to be forgotten; this makes drought tolerance even more complicated from a breeding perspective. The single trait selection approach has limited physiological applications in plant breeding and constitutes a major point of controversy among breeders and physiologists for decades. Hopefully, the next generation strategy of genomic breeding coupled with advances in phenomics, and machine learning will allow for increasingly more effective inclusion of physiology in breeding and accelerated development of peanut cultivars with improved drought tolerance. Because these strategies have the power to combine a diversity of plant responsive traits, i.e. traits associated with yield, biotic and abiotic tolerance, the mechanisms mostly contributing to increased yield under various “drought” conditions will be more likely to be successfully identified and further used to develop drought tolerant peanut cultivars.

Phenotyping Tools for Yield and Environmental Stresses

Field HTP: Platforms and Sensors

Remote sensing technologies are becoming increasingly more affordable to the end-users, breeders, and farmers. These technologies collect multiple plant characteristics in a relatively short time, repeatedly and consistently over the season with increased accuracy. Each of these technologies requires two main components. First, they require a vehicle or a platform to bring the sensors on site. Platforms are of different types, and they are complex and costly. The second part is the technology itself in the form of one or multiple sensors for data collection.

Phenotyping platforms

Several types of systems are currently available, with different levels of adaptability and upgradability, mobile or stationery, and with a drastic price range from hundreds to millions of dollars (Fig. 12) (Yuan et al., 2023). Platforms can be immobile, using single or multiple cameras and other sensors mounted on single or multiple poles to take time-lapse images (Brocks et al., 2016; Zhou et al., 2017). Others are stationary field scanner systems on a movable gantry (Virlet et al., 2017; Burnette et al., 2018). The Field Phenotyping Facility at the University of Nebraska Lincoln is one acre in size, and includes sensors and cameras mounted on an automatic cable suspended carrier system (Spidercam) attached to four giant poles in each corner of the field (Kirchgessner et al., 2017; Bai et al., 2019). Significant limitations of the stationary platforms are the high maintenance cost and the limited number of experiments that can be accommodated, e.g. use of any chemicals as treatments may result in negative effects on the subsequent experiments.

Figure 12. Different categories of potential and actual ground and aerial phenotyping platforms, along with the spectral ranges used for different remote-sensing tools. RGB cameras (VIS), multispectral and hyperspectral sensors and cameras, light detection and ranging (LiDAR) sensors, thermal sensors and cameras (TIR/LWIR), and the different categories of Radars. The ground stationary platforms correspond to the Maricopa Agricultural Center (USA) and the ETH field phenotyping platform (Switzerland). The images of the phenomobile correspond to (from left to right) a proximal remote sensing buggy, the phenomobile lite, and a phenocart. IR, infrared; LWIR, long-wave infrared; NIR, near-infrared; SWIR, short-wave infrared; TIR, thermal infrared; VIS, visible (Adapted from Araus et al., 2018b).

Alternatives to the stationary platforms are mobile, manned or unmanned, platforms. Mobile remote sensing systems include phenomobiles, aircraft, and satellites. The phenomobiles are ground-based vehicles such as unmanned robots or manned diesel-powered tractors and pushcarts (Pittman et al., 2015; Yuan et al., 2018; Antonucci et al., 2023). While phenomobiles allow freedom of use in multiple environments over stationary platforms, they are also limited by the geography of the land through which they can be driven. For example, their use can be limited for tall canopies, wide-spread ground cover crops, and uneven ground. For tractor-based platforms, a significant limitation is the area where they can be safely and time-effectively moved to collect data.

Manned and unmanned aircraft bypass the terrain limitations of the terrestrial platforms. Aerial imaging allows faster and broader coverage of a field than ground-based vehicles. Manned aircraft are well-suited for large landscapes consisting of several hundred to thousands of acres, but their high cost, flight restrictions to certain zones, and the requirement and availability of pilots make their use limited. Drones or unmanned aircraft (UAVs) with either fixed wings or multi-rotors are better options for small farms and research areas. A disadvantage of UAVs, however, is that battery capacity limits the flight mission and payload.

Satellite imagery collected from the Sentinels of the European Space Agency and Landsat from the US is used for phenotyping, mainly for large landscape areas. Satellite imagery is based on vegetation-derived reflectance at multiple wavelengths, from which numerous vegetative indices have been developed for different applications (Reed et al., 1994; Labus et al., 2002; Johnson et al., 2003). The major disadvantage of satellites is their lower spatial resolution in comparison with aircraft. Image resolution is 30 m² or more for Landsat 8, 3 m² for Planetscope, and 1 m² for SkySat, which eliminates small plot comparisons, and intra-plot and plant-level phenotyping. The passage of the satellite above the experiment area as well as the presence of clouds can also limit their use. A general disadvantage of all terrestrial and aerial HTP platforms is that they require expertise at multiple levels to use and maintain them.

Sensors used in field phenotyping

The sensors used in remote sensing are diverse. Laser and ultrasonic sensors are mainly used on ground-based vehicles to estimate plant height. These systems emit a laser or an ultrasonic pulse at a recurrent frequency, which hit the ground or a plant, then reflect back to the sensor. The longest distance measured by the emitted pulse is the bare ground and the smaller is the plant. The subtraction of both distances gives the height of the plant. In this way plant height of maize, bermudagrass, wheat, and alfalfa (Fumiki and Omasa, 2009; Ehlert et al., 2010; Selbeck et al., 2010; Pittman et al., 2015; Cazenave et al., 2019a, b) was successfully estimated.

Light Detection and Ranging (LiDAR) technology has been used for several decades in meteorological applications. More recently, LiDAR is being used to phenotype crops, including peanut, by creating 3D images, either from terrestrial or aerial platforms (Yuan et al., 2019; Sun et al., 2017; Saeed et al., 2023). LiDAR has been used to successfully estimate plant height and, in combination with ground-based platforms, estimate canopy architecture and its components, i.e. stem height, length of lateral branches, and canopy shape and density (Fig. 13).

Figure 13. Comparison between 3D reconstruction from LiDAR (top) vs photogrammetry RGB (bottom). The LiDAR can provide more details than RGB by penetrating denser canopy (left, Wallace et al., 2016) while the RGB (bottom right) is allowing some high-resolution visual data in full color as compared to the colorized LiDAR version based on intensity of reflection (top right, USGS, 2018).

Photogrammetry uses high resolution cameras mounted on either terrestrial or aerial platforms to sense reflected light in the visible red, green, and blue (RGB) bands of the electromagnetic spectrum. In peanut, seedling vigor, main stem height, wilting, and yield under drought and late leaf spot disease [caused by the fungus Northopassarola personata (Berk and Curt)] were RGB-estimated with great accuracy (Balota and Oakes, 2016, 2017; Sarkar et al., 2020; Sarkar et al., 2021a; Sarkar et al., 2021b; Kassim et al., 2022; Sie et al., 2022; Oteng-Frimpong et al., 2023; Balota et al., 2024; Chapu et al., 2022; Chapu et al., 2024).

Near infra-red and thermal multispectral sensors are used to assess crop color, temperature as result of transpiration, and biomass accumulation from reflectance in the near infra-red and other spectra. Reflectance-derived vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), LAI, and canopy temperature in peanut (Balota and Oakes, 2017; Yuan et al., 2018; Sarkar et al., 2020) and other crops (Freeman et al., 2007; Erdle et al., 2011; Gnyp et al., 2014; Pittman et al., 2015; Cazenave et al., 2019b) have been recently developed.

Phenotyping complex physiological traits

Phenotyping complex physiological processes and hidden plant organs, e.g. roots and peanut pods, is challenging. For these, HTP and remote sensing can only be partially implemented. Therefore, phenotyping mainly relies on low throughput methods (LTP). However, recently Gimode et al. (2023) used Ground Penetrating Radar (GPR) to analyze the below-ground peanut pods non-destructively.

Gas exchange phenotyping

Photosynthesis is a complex process that includes different complex reactions that, for simplification, can be divided into light reactions driven by electrons passing the different photosystems, and CO₂ fixation. Measurement of CO₂ fixation parameters uses infrared gas analyzers (IRGA). Depending on the type of photosynthetic parameter, measurements can take between one min, i.e. midday CO₂ fixation (A), to 90 min, i.e. the maximum rate of Rubisco carboxylation (V_cmax) and electron transport (J_max), and A response to internal CO₂ (Ci) (A-Ci) curves (Farquhar et al., 1980; Bernacchi et al., 2003). Nonetheless, IRGA measurements only give periodic information that may not be representative of the whole plant performance over the growing season. Farquhar et al. (1989) used carbon isotope discrimination (Δ¹³C), as it offers information on photosynthesis and WUE in a more integrated way over the life span of the plant.

WUE phenotyping through carbon isotope discrimination

In peanut, WUE has been demonstrated to be an important trait to select to improve drought stress tolerance (Vadez and Ratnakumar, 2016; Zhang et al., 2022). Water use efficiency can be calculated as the amount of biomass fixed per amount of water used by the plant. The most accurate way to calculate the amount of water used by a plant is gravimetrically, i.e. weighing potted plants every day, re-watering to pre-calculated weights to achieve desired water levels and calculating the amount water lost through transpiration (Vadez and Ratnakumar, 2016; Zhang et al., 2022). However, this method is time consuming, and few facilities can afford to perform such big scale, controlled experiments. Farquhar et al. (1989) reported a strong relationship between WUE calculated gravimetrically and Δ¹³C. This relationship has been demonstrated in several crop species such as common bean (Ehleringer et al., 1991), soybean (Dhanapal et al., 2015), wheat (Del Pozo et al., 2016), and peanut (Wright et al., 1993), allowing Δ¹³C to be used as surrogate phenotyping for WUE. In peanut, Δ¹³C is highly inheritable (Hubick et al., 1988), therefore allowing identification of the genomic regions responsible for changes in Δ¹³C and then WUE. However, the Δ¹³C method is expensive, i.e. mass spectroscopy analysis costs around $8 per sample.; Due to the cost and complexity of the peanut genome, Δ¹³C method has not yet been connected successfully to QTLs in peanut breeding; it is more successfully used in soybean and wheat (Dhanapal et al., 2015; Del Pozo et al., 2016). In soybean and wheat, GWAS and QTL mapping have been performed and genomic regions codifying Δ¹³C are now available (Dhanapal et al., 2015; Mora et al., 2015). Despite its success, Δ¹³C is far from being considered an HTP technique because of the time required for biomass sampling and preparation, and the subsequent spectroscopy analysis. Near infrared (NIR) technology is based on the unique reflectance patterns that different leaf and seed chemical compounds produce when they are exposed to light with a known light spectrum. Ferrio et al. (2001) used NIR technology to accurately predict Δ¹³C, and thus WUE. These authors predicted Δ¹³C from NIR reflectance with an accuracy ranging from 82 to 86%.

Hyperspectral reflectance to predict photosynthetic parameters.

Hyperspectral sensors capture electromagnetic radiation reflected from vegetation in the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1,300 nm), and short-wavelength infrared (SWIR, 1,400–3,000 nm) regions of the electromagnetic spectrum, which contain information about leaf physiological status including pigments, structural constituents of biomass, and water content (Curran, 1989; Penuelas and Filella, 1998). Variation of foliar reflectance at different wavelengths is specific to the variations in chemical and structural characteristics of the leaf and photosynthetic apparatus (Fig. 14a, b, c) (Serbin et al., 2012). Hyperspectral reflectance spectroscopy as an HTP tool is becoming recognized as promising in agricultural research (Serbin et al., 2012; Weber et al., 2012; Araus and Cairns, 2014; Silva-Perez et al., 2017; Yendrek et al., 2017). Hyperspectral hand-held sensors such as the Field Spec Hi-Res-4, have been used to predict photosynthetic parameters including maximum rate of rubisco carboxylation Vc_max (Fig. 14) and midday CO₂ assimilation of peanut plants (Buchaillot et al., 2022). However, this technology is low throughput as individual plants need to be measured one by one. To reach high throughput phenotyping, researchers are currently using hyperspectral cameras mounted on drones to predict these parameters with similarly accurate results predicted with the hand-held equipment (Bagherian et al., 2023). The combination of drone and hyperspectral technologies will allow breeding programs to phenotype plant photosynthetic parameters in a high throughput manner in the future.

Figure 14. (A) Example of standard A-Ci curve (grey dots), Rapid A-Ci curve (blue and red dots), and data used to calculate Vcmax and Jmax. Figure modified with permission from Stinziano et al. (2019). (B) Example of the FieldSpec HiRes4 (ASD technology) used to take hyperspectral data from the leaves. (C) Correlation test used to demonstrate the accuracy of the Partial-Least (PLSR) model and hyperspectral data to predict Vcmax for peanut and soybean (Buchaillot et al., 2022).

Phenotyping roots - the hidden resource.

Roots provide water and nutrient uptake. Different root traits enable plants to respond, adapt and tolerate different environmental stresses. For example, peanut cultivars with drought tolerance showed greater root length at deeper depths than drought-sensitive cultivars (Thangthong et al., 2017). As the root system is underground, historically, it has been difficult to phenotype (York et al., 2015; Atkinson et al., 2019). However, recently, technological improvements in image acquisition and data analysis have allowed great advancements in root and pod phenotyping, as mentioned earlier in this section (Gimode et al., 2023). Regardless of various obstacles, e.g. soil type, heterogeneity and moisture, GPR technology can accurately detect below-ground plant parts significantly contributing to the understanding of plant response mechanisms to tolerate stress (Dobreva et al., 2021; Gimode et al., 2023).

Other non-destructive field measurements rely on rhizotrons and rhizo-boxes systems with different automation capabilities and, more recently, on X-ray computed tomography and GPR (Garbout et al., 2012; Nagel et al., 2012; Rogers et al., 2016; Thangthong et al., 2016; Liu et al., 2018). For example, for peanut, Thangthong et al. (2016) developed a rhizotron that can be placed in the field to monitor root development in quadrants of 10 cm² (Fig. 15a), from the beginning of the season until harvest. Although this type of system is advanced and more representative of “normal” root development in the field, soil particles can interrupt the visibility of the roots causing missed data (Atkinson et al., 2019). Soil and soilless systems (Fig. 15b, c) used in greenhouse applications are less informative as root phenotypes can be drastically different from the phenotypes in the field (Kuijken et al., 2015).

Figure 15. Examples of different root phenotyping platforms used in field and under controlled conditions. (A) Rhizotron used in field experiments to image root profile using RGB camera (Thangthong et al., 2016). (B) Greenhouse based automated root phenotyping GROWSCREEN-Rhizo System (Nagel et al., 2012). (C) Aeroponic system used to study cassava root development in the greenhouse (Gomez-Selvaraj et al., 2019).

In summary, research achievements in peanut physiology have been numerous in recent years. These achievements are predominant in the areas of crop requirements for sustainable production, plant growth and development, mechanisms of adaptation to biotic and abiotic factors, and, more recently, the development of high-throughput tools for phenotyping. Along with the achievements in genome sequencing and genetic mapping technologies, plant physiology discoveries and advancements in HTP technologies pave the way for the implementation of new strategies in peanut breeding, i.e. molecular breeding and genomic selection. These strategies appear more robust than traditional breeding for predicting valuable traits for increased yield and adaptation to environmental conditions; and allow pyramiding these traits more effectively in future peanut cultivars to meet production requirements for the specific geographical regions in which cultivars will be grown.

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