Assessing Cowpea-Wheat Double Cropping Strategies in the Southern United States Using the DSSAT Crop Model ()
1. Introduction
Improvements in agricultural practices are needed to enhance sustainability and efficient use of soil, water, and other cropping resources with attention to profitability and increased food production. Double cropping is a system of warm season-cool season cropping that produces two harvested crops in one 12-month period. Soybean (Glycine max [L.] Merr.) and wheat (Triticum aestivum L.) double cropping in the lower Mississippi River Valley of the United States is a common example of this type of intensive agriculture grain production [1]. In this region of the southern United States, soybean-wheat double cropping has been adopted to improve the sustainability of row-crop agriculture and to eliminate or decrease N fertilizer applied to the wheat crop. Multiple studies indicated that soybean-wheat double cropping improves profitability and resource efficiency compared to monocultures [1] [2]. Besides increased profits from soybean-wheat double cropping, this planting system decreases erosion and reduces soil-water losses by runoff and evaporation [3]. Using loamy sand and fine sandy loam soils for wheat-soybeans, reference [4] showed that double-cropped soybeans had improved growth and yield compared to the fallow-soybean mono-cropping system. They attributed the increased soybean growth to wheat serving as a cover crop during the winter months and the residual nutrients from the winter wheat. In addition, reference [5] showed that wheat-soybean double cropping improved the capture and efficiency of use of rainfall and photosynthetically active radiation compared to monocropping wheat or soybeans.
Cowpea (Vigna unguiculata [L.] Walp.) is important in world agriculture as a pulse, forage, and vegetable crop in tropical, semi-tropical, and arid regions. Cowpea is generally drought and heat tolerant compared to other grain legumes but will have production losses if daytime maximum temperatures are above 40˚C [6]. The estimated world production of dry pulse cowpea was 7 million metric tons in 2011 [6]. Dry pulse cowpea production in the United States was centered in California with some Texas production between the years 2005 and 2015 and has ranged from 2000 to 6000 ha yr−1 with yields at 2200 kg ha−1 [7]. In Brazil, about 1.4 million ha of cowpea are grown annually with average grain yields of 526 kg ha−1. A cowpea-wheat double cropping system in Brazil produced 1900 kg ha−1 of cowpea grain and 5593 kg ha−1 of wheat grain with the 0 kg N ha−1 fertilizer treatment [8]. More field research information is needed to determine the potential for cowpea-wheat double cropping in other Texas regions and the humid southeastern United States to enhance soil health properties and increase net returns.
Climate is a major factor defining interannual variability in crop production. The yearly fluctuation of climate in the southern United States has been linked to a set of coupled ocean-atmosphere phenomena occurring across the tropical Pacific, collectively known as El Niño-Southern Oscillation (ENSO) [9] [10]. For the southern United States, an ENSO phase may be skillfully forecasted up to a year in advance because of the strong connection between weather patterns in this region and ENSO [11]. Thus, cowpea and wheat production in this region may potentially be benefitted from ENSO forecasts. A number of studies have been conducted on connections between ENSO and various specific crops in this region [12] - [22]. However, no study has assessed the relationships among cowpea-winter wheat double-cropping, soil type, N application rate, and ENSO for the southern United States. An understanding of such relationships could assist cowpea-winter wheat grain producers in this region in adopting alternative management strategies.
For solving real-world problems safely and efficiently by providing clear comprehensions of complex systems, the systems analysis approach is valuable [23] [24]. Since crop simulation models predict plant growth and development as influenced by crop management and the environment by using quantitative descriptions of ecophysiological processes [25], they can be valuable tools for studying various scenarios comprising a number of variables in the soil-plant-atmosphere continuum. One of the widely-tested and used suite of crop models that can be used to effectively study these scenarios is Decision Support System for Agrotechnology Transfer (DSSAT: [26] [27]).
The objectives of this study were to assess the effects of cowpea-wheat crop sequence, soil type, N application rate, ENSO, and their interactions on the grain yields of cowpea and winter wheat in southern United States using the sequence analysis tool of the DSSAT crop model.
2. Materials and Methods
2.1. DSSAT and Its Sequence Analysis Tool
The DSSAT crop model is a software application program comprising simulation models for many crops and tools to facilitate the effective use of the models [28]. The crop models simulate growth, development, and yield as a function of the soil-plant-atmosphere continuum. The model tools include database management programs for applications, crop data, soil, utilities, and weather. The DSSAT suite of crop models has been used for various applications at different spatial and temporal scales such as on-farm and precision management, climate variability and climate change impacts, breeding and gene-based modeling, water use, greenhouse gas emissions, and agroecosystem sustainability [28]. The inputs for the crop models include data on daily weather, soil surface and profile, and crop production-management details. The simulations are conducted primarily on a daily basis. At the end of each day, the balances of soil-plant water, nitrogen, phosphorus, and carbon, as well as development states are updated. To simulate multi-year outcomes of crop management strategies, DSSAT integrates the effects of soil, crop phenotype, weather, and management options by combining crop, soil, and weather databases with crop models and application programs.
The Sequence Analysis tool allows the user to conduct rapid inspection and analysis of results of long-term cropping sequences [29]. The model tool allows the user to calculate a series of statistics and create various graphics that examine relationships between trends and variability. The main aspect of the sequence analysis is that simulation studies are conducted across multiple cropping seasons; thus, status of soil water and nutrient is carried over from one cropping season to the subsequent one [30] [31].
2.2. Site and Data
The Texas A&M AgriLife Research & Extension Center at Overton (32.29˚N, 94.97˚W) is situated in the Pineywoods region of the southern United States. This region, where agriculture is a major economic activity, comprises eastern Texas, western Louisiana, and southern Arkansas. At the Overton Center, numerous soil fertility and cultivar trials and grazing experiments have been conducted by various scientists since 1967. In this study, Overton, Texas was used as a representative site for the Pineywoods region and the humid Southeastern United States [21] [22].
To explore the interannual climate variability effects on the yields of cowpea and wheat in the Pineywoods region, a long-term weather dataset spanning 80 years (1942-2021) was used. The historical daily data on temperature and precipitation were obtained from https://www.ncei.noaa.gov/access/search/data-search/daily-summaries (National Centers for Environmental Information) and the reports and publications from the Texas A&M AgriLife Research and Extension Center at Overton, whereas those on solar radiation were generated using a reliable irradiation model described by [32].
Darco (Grossarenic Paleudults) and Lilbert (Arenic Plinthic Paleudults) were used as representative soils for the study because they are some of the major soils used for agricultural purposes in the Pineywoods region [33] [34]. The soil data were obtained from the Gridded Soil Survey Geographic (gSSURGO) database of the USDA NRCS [35] [36]. These soils are distinct in various aspects, including texture and inherent fertility level. Compared with Lilbert, Darco had less clay and silt contents, greater saturated hydraulic conductivity, deeper A and E horizons (122 cm vs. 58 cm), less organic C and inorganic N contents, and smaller values of field capacity and wilting point (Table 1). However, the water holding capacities of both soils were about the same.
2.3. The Simulation Study Design
The DSSAT Sequence Analysis tool was used to simulate grain yields for cowpea and winter wheat in two sequences: cowpea-wheat double crop and fallow-wheat. The wheat crop that followed cowpea or fallow, hereafter, will be referred to as cwheat and fwheat, respectively. Simulations were made using two soils, Darco and Lilbert, and three N application rates only to wheat at 0, 100, and 200 kg N ha−1. Thus, a total of 12 scenarios were assessed, which comprised 2 sequences × 2 soils × 3 N rates (Table 2).
For simulations, the following management and environmental inputs were assumed. For cultivars, we used “Hyfowet” for winter wheat and “Cal #5 MG4”
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Table 1. Properties of two representative soils in the Pineywoods region of the southern US.
a. MH = master horizon, WP = wilting point, FC = field capacity, SA = saturation, WH = water holding capacity, HC = saturated hydraulic conductivity, TN = total N, OC = organic carbon.
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Table 2. The simulation study scenarios comprising two crop sequences, two soil types, and three N applications rates (kg N ha−1).
a. Double crop.
for cowpea. The Hyfowet cultivar was the highest-yielding wheat cultivar in east Texas as identified by [21]. Because the genetic coefficients for this cultivar were already estimated by [21], there was no need to further calibrate and evaluate the wheat model for this cultivar. For the Cal #5 MG4 cultivar, the default genetic coefficients for this cowpea cultivar are those in the standard DSSAT release [27] and correspond to the cultivar coefficients upon which the cowpea model was adapted (K. J. Boote, personal communication, 11 February 2022). The simulation start date was assumed to be June 20, 1942, and simulation would terminate on the harvest date of cowpea in 2021. On the simulation start day, soil moisture was assumed to be at field capacity and soil N content 25 kg ha−1. For planting dates, we assumed June 20 for cowpea and October 20 for wheat. The plant populations used were 30 plants m−2 (about 40 kg seed ha−1) for cowpea and 323 plants m−2 (about 100 kg seed ha−1) for wheat. Using the conventional tillage, dry seeds were planted on rows at 3 cm depth. Inorganic N fertilizer was given only to wheat, not cowpea.
Of the total amount of N applied to wheat, 50% was applied at the planting time and the remainder on February 15 of the following year. For organic amendments, the cowpea stover residue of 2125 kg DM ha−1, with the N content of 1.5%, was incorporated into the soil on the planting date of wheat, and the wheat residue of 500 kg DM ha−1, with the N content of 1%, was incorporated in the soil on the planting date of cowpea. For soil organic matter, Century was used as the method, with the five years’ field history of “Cultivated, good management, initial default SOM” [37].
2.4. ENSO Classification
For ENSO analyses, the yields of cowpea, cwheat, and fwheat that were simulated for each of the 80 years (1942-2021) were assigned to an ENSO phase – El Niño, La Niña, or Neutral – as categorized by the Japan Meteorological Agency (JMA) index [38] [39] [40] [41]. For ENSO characterization, the JMA index was chosen because it best selects the known ENSO events [41]. Accordingly, the total numbers of El Niño, La Niña, and Neutral years analyzed were 18, 20, and 42, respectively. The JMA index is a 5-month moving average of the sea surface temperature anomalies over the tropical Pacific (4˚S - 4˚N, 150˚W - 90˚W). The ENSO year of October through the following September is categorized as El Niño, La Niña, or Neutral if the index values are ≥0.5˚C, ≤−0.5˚C, or between −0.5˚C and 0.5˚C, respectively, for 6 consecutive months, including October, November, and December [39] [41].
2.5. Data Analyses
For cowpea, cwheat, and fwheat each significance tests were carried out to assess grain yield differences across soil types as influenced by N rate and ENSO interactions, across N rates as influenced by soil type and ENSO interactions, and across ENSO phases as influenced by soil type and N rate interactions. The tests were done using a nonparametric alternative to the two-sample t-test, known as the pairwise Wilcoxon rank sum test [42]. The reason for using the Wilcoxon test was that the assumption of normality was not met for each analysis of variance (ANOVA) test. For statistical analyses, the R software environment (R version 4.1.1) was used (https://www.r-project.org/).
3. Results and Discussion
The grain yields of cwheat were greater than those of fwheat on both Darco and Lilbert soils at all the three N rates considered (0, 100, and 200 kg ha−1) and under the three ENSO phases—El Niño, La Niña, and Neutral (Table 3). These results were likely because cwheat received more nutrients than did fwheat mainly through two processes: the symbiotic N fixation of cowpea (about 117 kg N ha−1 season−1) and N transfer from the cowpea crop residue applied (about 32 kg N ha−1 season−1 from the residue of 2125 kg DM).
3.1. Soil Type Effects on Cowpea and Wheat Yields
3.1.1. Influence of N Rate
Under all ENSO phases, the soil type effect on fwheat yields was significant at all N rates considered (0, 100, and 200 kg ha−1) and that on cwheat yields was significant only at 0 kg N ha−1 (Table 3). In all these cropping cases, the yields of both cwheat and fwheat on the Lilbert soil were greater than those on the Darco
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Table 3. The soil type effects on the grain yields of cowpea and wheat as influenced by N rate x ENSO phase interactions over 80 years long-term weather at Overton, TX.
†,cwheat = wheat preceded by cowpea, fwheat = wheat preceded by fallow, ‡Means followed by the same letter between soils (vertically) within a N rate-ENSO-crop combination are not significantly different at α = 0.1.
soil. The greater yields produced on Lilbert, compared with Darco, were likely due to the following reasons. Lilbert was a heavier soil, containing larger proportions of clay and silt (Table 1); thus, it was less prone to percolation losses. Lilbert was more productive because its inherent inorganic N and organic C contents were greater. The eluviation zone of Lilbert, which consisted of the master horizons of A and E, was much shallower (58 cm) than that of Darco (122 cm). A shallower zone of eluviation, which contained more nutrients for plant growth, represented a smaller volume where small colloidal-sized materials had been removed through the movement of water [43]. Compared with the cwheat yields, the fwheat yields were associated with less fertile soils due to the absence of N fixed by cowpea and N transferred from cowpea residue. Because of lower soil fertility levels, the fwheat crops were more responsive to the applied N than were the cwheat crops. Thus, the fwheat yields on Lilbert were greater at all N rates. Since the productivity levels of soils associated with cwheat crops were already higher than associated with fwheat crops, additional amounts of N contributed less. Thus, the cwheat crops started plateauing at about 100 kg N ha−1. Regardless of the ENSO phase, soil type effects on the yields of cwheat and fwheat decreased with an increase in N rate. For instance, at the N rates of 0, 100, and 200 kg ha−1, respectively, the cwheat yields were 20%, 4%, and 1% greater and the fwheat yields were 148%, 40%, and 8% greater on Lilbert than those on Darco. The decreasing soil type effect with an increase in N rate was likely because the inherent fertility level of Lilbert was higher than that of Darco. Thus, the yield difference between the two soils was very large at low N level. With an increase in N rate, however, the yield difference became less because additional amounts of N contributed less due to decrease in N efficiency.
3.1.2. Influence of ENSO
At all N rates, cowpea yields were impacted by soil type only under the La Niña phase of ENSO. Irrespective of N rate, the soil type effect on cowpea yields was greater during La Niña. Under this phase, the cowpea yields on Lilbert were 25% greater than those on Darco, whereas the greater yields on Lilbert were only 12% during the El Niño phase (Table 3). Regardless of soil type and N rate, cowpea yields under La Niña were the least of all ENSO phases. This was likely because La Niña received the smallest amounts of precipitation during the growing season (June-October) (313 mm under La Niña vs. 362 mm under El Niño), the establishment phase (June-July), the peak summertime (August), and the flowering and pod formation stages (mid-Sept to mid-Oct), the critical stages of water requirement for cowpeas [44] (Figure 1). This scenario was confirmed by the amount of water transpired by the cowpea crops that was the least under this phase (197 mm vs. 220 mm under El Niño). Because the impact of weather on crop yields was stronger on a less productive soil as higher soil fertility could mask the weather effect, the La Niña impact on Darco, a less productive soil than Lilbert (Darco: 0.95% total N, 0.10% organic C vs. Lilbert: 3.91% total N, 0.39%
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Figure 1. Daily average precipitation distribution in different months during the year under the three ENSO phases (El Niño, La Niña, and Neutral) at Overton, Texas.
organic C), was greater than that on Lilbert. This phenomenon led to further decrease in cowpea yields on Darco relative to Lilbert under La Niña. The greater yields of cowpea on Lilbert relative to Darco during La Niña were also likely due to the textural differences of these soils. The larger amount of water that the Lilbert soil, which was heavier than Darco, was able to conserve in a drier year of La Niña played more important role in producing cowpea yields than that conserved in a wetter year of El Niño. For cwheat and fwheat yields, the soil type effect was not influenced by ENSO because rainfall was not restrictive during this period of wheat growth.
3.2. The N Rate Effects on Cowpea and Wheat Yields
The yields of cwheat and fwheat were both impacted by the N application rate, regardless of soil type or ENSO phase (Table 4). On both soils under all ENSO phases, the yields of both cwheat and fwheat increased with an increase in N rate from 0 to 100 and 200 kg ha−1, respectively. However, with an increase in N rate from 100 to 200 kg ha−1, cwheat yields did not increase. The yield responses of cwheat to N rate were lower than those of fwheat because cwheat yields were associated with a higher soil fertility level from cowpea residue (about 32 kg N ha−1 season−1) and N transfer through N fixation (about 117 kg N ha−1 season−1). Since the inherent fertility level of the soil associated with cwheat was relatively high, additional amounts of N contributed less; thus, the cwheat yields started plateauing at 100 kg N ha−1. The fwheat yields, however, did not level off at this N rate due to a lower soil fertility level.
For cowpea yields, however, no N application rate effect was observed on either soil under all ENSO phases. These results were expected because cowpea is a
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Table 4. The N application rate effects on the grain yields of cowpea and wheat as influenced by soil type x ENSO phase interactions over 80 years long-term weather at Overton, TX.
†,cwheat = wheat preceded by cowpea, fwheat = wheat preceded by fallow, ‡Means followed by the same letter across N rates (vertically) within a soil-ENSO-crop combination are not significantly different at α = 0.1.
legume, despite no N fertilizer, and wheat residues carry-over (5 kg N ha−1 season−1 from the residue of 500 kg DM) did not enhance cowpea yields. The N applications were made only for cwheat and fwheat crops. These N applications did not have any significant residual effects on the yields of cowpea which followed wheat.
3.2.1. Influence of Soil Type
Regardless of the ENSO phase, the N rate effect on both cwheat and fwheat yields was greater on Darco, relative to Lilbert. For instance, cwheat yields on Darco and Lilbert increased by about 28% and 12%, respectively, when N rate was increased from 0 to 100 kg ha−1. Likewise, fwheat yields at the N rate of 100 kg ha−1 on Darco and Lilbert, respectively, were 199% and 69% greater than those associated with 0 N rates. Reference [22] exhibited that the response of biomass production to N rate was influenced and controlled by soil water holding capacity and inherent fertility level.
As the water holding capacities of Darco and Lilbert were about the same (0.09; Table 1), the greater N rate effect on Darco was due to its lower inherent fertility level. Because the plant production conditions on Darco were more N-limiting compared with that on Lilbert, there was a greater yield response to external N application on Darco. The greater N response was also due to its lower clay and silt contents. The lighter texture of Darco provided better environment for soil aeration and root development. The yields associated with Lilbert were greater than those associated with Darco because the inherent fertility level of the former was higher than that of the latter.
3.2.2. Influence of ENSO
Irrespective of soil type, N rate effect was greater under an El Niño phase for both cwheat and fwheat yields. For instance, cwheat yields at the N rate of 100 kg ha−1 under El Niño and La Niña phases, respectively, were 27% and 18% greater than those associated with 0 N applications. Similarly, fwheat yields under El Niño and La Niña phases increased by about 151% and 121%, respectively, when N rate was increased from 0 to 100 kg ha−1. The greater rates of increase in cwheat yields with increases in N rate under El Niño were likely due to the following reasons. With more precipitation during the wheat growing season in El Niño years, the available soil water relative to N was more; thus, plant N uptake became less water-dependent. However, with smaller amounts of soil water during La Niña, the water-limited production conditions increased, and the increase in grain yields with a higher N rate became smaller. These results agreed with the findings that increases in potato (Solanum tuberosum L.) [45] and bermudagrass [Cynodon dactylon (L.) Pers.] [22] biomass yields with an increase in N rate were small when the amounts of water in the soil were small.
3.3. The ENSO Effects on Cowpea and Wheat Yields
The yields of cowpea were impacted by ENSO on both soils and at all N rates considered. Regardless of soil type and N rate, cowpea yields in El Niño years were greater than those in La Niña years (Table 5).
The greater yields under El Niño conditions were likely due to the greater amounts of precipitation this ENSO phase delivered during the establishment phase, the growing season, the peak summertime, and the flowering and pod formation stages (Figure 1).
Although the ENSO impacts on cwheat and fwheat yields were statistically evident only at 0 kg N ha−1 and at 0 and 100 kg N ha−1, respectively (Table 5), the general trend was that, regardless of soil type and N rate, yields of both cwheat and fwheat in La Niña years were greater than those in El Niño years (Figure 2). These results are in agreement with the findings of [19] [46] [47] and [48]. The likely reasons for the greater yields of both cwheat and fwheat under the La Niña phase, in general, were as follows. First, due to warmer conditions at
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Table 5. The effects of ENSO on cowpea and wheat grain yields as influenced by soil type x N rate interactions over 80 years long-term weather at Overton, TX.
†, cwheat = wheat preceded by cowpea, fwheat = wheat preceded by fallow, ‡Means followed by the same letter across ENSO phases (vertically) within a soil-N rate-crop combination are not significantly different at α = 0.1.
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Figure 2. Grain yields of wheat preceded by cowpea (cW) or fallow (fW) as influenced by soil type [Darco (D) vs. Lilbert (L)] and ENSO [La Niña (LA) vs. El Niño (EL)] at Overton, TX. The legend cW-L-LA, for instance, corresponds to the yields of wheat following cowpea on the Lilbert soil during the La Niña phase.
the planting time under La Niña than under the other ENSO phases, wheat had better germination. For good germination and growth of wheat, 12˚C to 25˚C is the optimum temperature range [49]. Second, during germination and early vegetative stages, no significant harmful effect of drier conditions under La Niña was likely because the total amount of water required for these stages was small (<1.5 cm·d−1) relative to the ones that occurred later in the season [50]. Third, due to warmer conditions under La Niña, more wheat tillers per area would be likely due to warmer temperatures [51], with the optimum temperature for tiller development being 13˚C [52]. Fourth, under the La Niña phase, more photosynthetic assimilates would be expected as this phase has been generally associated with clearer skies and more solar irradiation. Fifth, the La Niña phase had less likelihood of the occurrence of various insect pests and diseases due to drier and warmer conditions [17] [53]. Finally, La Niña was less likely to provide freeze injury to wheat crops, particularly during jointing to flowering [54].
3.3.1. Influence of Soil Type
The effect of ENSO on cowpea yields was greater on the Darco soil at all N rates. For instance, irrespective of N rate, the cowpea yields in El Nino years, compared with La Nina years, increased by about 40% on Darco and by about 26% on Lilbert. The stronger ENSO effect on cowpea yields on Darco relative to Lilbert was likely due to fertility and textural differences of these soils. On Darco, a less fertile soil compared with Lilbert, the effect of weather was more pronounced. As a lighter soil, the less amount of water Darco conserved, relative to Lilbert, in a drier year of La Niña resulted in less yields, whereas the yield difference between the soils in a wetter year of El Niño was less. Thus, the yield difference between the two ENSO phases (El Niño yields minus La Niña yields) was larger on Darco relative to Lilbert. However, the role of soil water holding capacity—another principal factor determining the influence of soil on the ENSO impact [16], was not significant as both soils had about the same capacity for water retention. The effects of ENSO on the yields of both cwheat and fwheat were not significantly influenced by the soils considered in this study.
3.3.2. Influence of N Rate
The effect of ENSO on the yields of all crops considered decreased with an increase in N rate on both soils. For instance, irrespective of soil type, cowpea yields in El Nino years, compared with La Nina years, increased by about 37%, 33%, and 31% at the N rates of 0, 100, and 200 kg ha−1, respectively. Similarly, the yield increases in La Nina years compared with El Nino years at the N rates of 0, 100, and 200 kg ha−1, respectively, were, about 15%, 7%, and 5% for cwheat and about 38%, 20%, and 8% for fwheat. The smaller ENSO impact on crop yields at a higher N rate was due to the masking effects of soil fertility on weather effects.
When 0 N was applied or at low soil fertility, the ENSO impacts on cwheat and fwheat yields were the greatest (Table 5). With an increase in N rate, however, the ENSO impacts diminished (Figure 2). Accordingly, no ENSO impact was significant at N rates greater than or equal to 100 kg N ha−1 for cwheat, and at 200 kg N ha−1 for fwheat. The results demonstrated that weather was the driving variable for wheat production on impoverished soils, and thus the ENSO had impacts only at low soil fertility. High soil fertility levels masked the impacts of the ENSO on wheat yields. Unlike cwheat, which received more nutrients through N fixation and N transfer from cowpea residues, the ENSO impact on fwheat, which received no residue nutrients, was significant also at 100 kg N ha−1 because the soil fertility level even at this N rate was still too low to conceal the effect of ENSO.
4. Conclusions
The simulation results showed that grain yields of wheat preceded by cowpea (cwheat) were greater than those preceded by fallow (fwheat) on all soils, at all N rates, and under all ENSO phases. Yields of both cwheat and fwheat were greater on Lilbert, the more fertile soil. The soil type effects on cwheat and fwheat yields decreased with an increase in N rate. The soil type effect on cowpea yields was greater during La Niña. Cowpea yields under La Niña were the least of all ENSO phases regardless of soil type and N rate. The La Niña impact on cowpea was greater on the less fertile soil Darco. Yields of cwheat and fwheat increased with an increase in N rate from 0 to 100 and 200 kg ha−1, respectively. The yield response of cwheat to N rate was less than that of fwheat. For cwheat and fwheat yields, the N rate effects were greater on Darco and under El Niño. The grain yields of cowpea were the greatest under El Niño, and those of cwheat and fwheat were the greatest under La Niña. The effect of ENSO on cowpea yields was greater on Darco. The effect of ENSO diminished with an increase in N rate.
Wheat grain production in a double-cropping system with cowpea illustrated the biological efficiency of the legume-preceding wheat compared to the fallow-wheat system, especially under zero N fertilization. With increased costs of inputs and current costs of N at $2.45 to $3.50 per kg N, cowpea-wheat double cropping using reduced N fertilizer inputs will improve the efficiency and profitability of the production system, compared to fallow-wheat.
Acknowledgements
Partial funding for this work was provided by the Texas A&M AgriLife Research at Overton, TX.