Assessing the Effect of Irrigation Water Management Strategies on Napier Productivity—A Review

Ivan Ntege^*, Nicholas Kiggundu, Joshua Wanyama, Prossie Nakawuka

Department of Agricultural and Bio-systems Engineering, Makerere University, Kampala, Uganda.

DOI: 10.4236/as.2021.1212092                

Abstract

Napier, a fast growing and perennial grass has a dry matter (DM) yield potential of 78 tons/ha/yr. However, under water availability constraints Napier’s yield potential reduces to 62 tons/ha/yr. In an effort to attain Napier’s yield potential, irrigation management strategies have been integrated into its production to provide the highest productivity. This review assesses the effect of irrigation water management strategies on Napier productivity and also looks at future perspectives. Application of these strategies i.e., precision irrigation, deficit irrigation, and application of biophysical models, can increase Napier’s yield potential to 112 tons/ha/yr. Review findings revealed that there is a need to close the knowledge gap on response of Napier productivity to different irrigation water management strategies. The future perspective explores the potential of the FAO AquaCrop model in provision of pre-season decision-making on irrigation strategies due to its relatively low cost and simplifications required in parameterization.

Keywords

Napier, Climate Change, Irrigation, Deficit irrigation, FAO AquaCrop

1. Introduction

Napier grass possesses many desirable characteristics, including high yield per unit area, tolerance to intermittent drought and high water use efficiency [1], making it a forage of choice. Napier grows across a wide range of agro-ecologies and soil conditions, well adopted up to 2100 m a.s.L, and it establishes best in areas where the average annual precipitation is between 750 and 2500 mm, although it withstands minor dry spells. Reference [2] noted that subtropical pastures are being used extensively to fulfil the fodder needs of the animals under intensive farming system and a large percentage are under irrigation due to their high production. Frequent, erratic and prolonged droughts have caused seasonality of Napier production. Farmers are often faced with having yield reductions and failure to meet agro-pastoral demands in terms of quality and quantity [3]. To avoid risks of yield penalty, irrigation has been integrated into Napier production to provide the highest productivity [4] [5]. However, there is an information meager on response of Napier productivity to different irrigation water management strategies [6].

This paper recognizes the inadequate information to assess the response of Napier yields to different irrigation water management strategies and it aims at addressing those gaps. Subsequent sections of the paper explicitly present a detailed review on Napier production in Uganda and discuss future prospects.

2. Napier Production

2.1. Background of Napier Grass

Napier grass (Pennisetum purpureum Schumach.) also well-known as elephant or Uganda grass, is a fast-growing perennial C4 grass native to Sub-Saharan Africa that is extensively grown across the tropical regions. Napier seeds are considered inappropriate for propagation as they produce weak seedlings which are also highly heterozygous since it is open pollinated [7]. Therefore, the grass is commonly distributed by vegetative cuttings as the prevailing practice. A study conducted by [3] observed that in the Lake Victoria crescent and Eastern Highlands Agro Ecological Zones (AEZ) of Uganda among smallholder farmers showed that the most prominent forage species used for feeding livestock is Napier at 31.8% under intensive farming system/Agro-Pastoral. Based on Napier Morphology, there are two major categories of cultivars, the normal or tall (up to 4 - 7 m) cultivar (i.e., “Bana” and “French Cameroon”) and the dwarf or semi-dwarf (<2 m) cultivar (i.e., “Mott”). Commonly grown varieties include: 1) Bana grass, usually leafy and with few silica hairs, which cause irritation during handling, 2) Clone 13, very resistant to white mould disease and a high yielder but its thin stems make it difficult to establish, 3) French Cameroon, is a high yielder, established easily from canes, 4) Kakamega 1 and 2, both are high yielders though Kakamega 1 has a higher growth rate than Kakamega 2 and 5) Pakistan hybrid, which does well in dry areas [8]. Normal Napier varieties have been reported by [9] to produce twice as much yield as the dwarf ones.

2.2. Economic Importance of Napier Grass

In Eastern African smallholder farming communities, Napier grass is reported to be one of the most grown forage crops [7]. The fact that it can be grazed directly or made into silage or hay, makes Napier a multipurpose forage crop [10] and there are also reports of using it as food for fish, for instance for feeding tilapia and grass carp in Nepal [7]. A recent report [11] from Nigeria also indicated that young shoots of Napier were used as vegetable. Besides Napier’s value as fodder, it can also be utilized as live markers for demarcation of river buffer zones, windbreak, and as a source of fuel when dried material [10]. In crop land management systems, it is used as a mulch to control soil erosion, weed infestation, and pest management as a trap plant [12]. Under pest management practice, there is application of a push-pull strategy which utilizes push plants as repellent intercrop and pull plants as attractant trap for insect pest control, particularly for the maize stem borer. Reference [7] observed that due to rising worldwide interest in plummeting consumption of fossil fuels and their related climate change impacts, there is increased promotion of large biomass plants like Napier as second or next-generation biofuel crops. Consequently, the potential exists for the use of Napier grass for phytoremediation purposes, after which the large harvest could go into processing plants for biofuel production. These wide-ranging uses of Napier grass provide an indication of the multiplicity of roles it could contribute to the decrease of poverty and nutritional insecurity.

2.3. Napier Quality Variables

2.3.1. Nutrients

Nutritional quality is strongly influenced by management practices and age at harvest but, on average, Napier grass is considered to contain different nutrients in samples taken from 10 - 15 weeks old plants as indicated in Figure 1.

Apart from genetics, nutritional qualities of forages are influenced by many factors including the climate [13], soil nutrition, season and grazing pressure, management [14] and fertilizer application. An important aspect for most forages is that cutting treatments and interval can have a significant impact on both yield and nutritional qualities [14]. Relatedly, [15] findings indicated that Napier’s CP content was observed to decrease significantly from 28.2% to 8.8% at 40-day and 80-day cutting intervals respectively. Then again, [16] noted that

Figure 1. Napier nutrient composition on dry matter basis (source: [7] ).

dry matter (DM) production increases significantly over consecutive cuttings from the first to the third. The aforementioned qualities of Napier grass make it an attractive choice for livestock production systems.

2.3.2. Yield

Napier’s potential yield mainly depends on the cultivar used which is also influenced by both the management practices employed and environment. A study conducted by [9] noted that the performance and Napier’s yield is heavily influenced by agro-ecology, climatic conditions, management practices and other edaphic factors. Relatedly, [17] findings showed that the highest Napier biomass yield was obtained at non-drought location with significant variations in cultivar performance. According to [18], the most significant factors affecting Napier’s DM production include environment, followed by interactions between genotype and environment and lastly genotype. Napier grass, with its perennial nature and fast growing characteristics, has been reported [7] with a potential to produce DM yield as shown in Table 1.

Table 1. Napier dry matter yield.

2.4. Agronomical Practices

Suitable conditions for Napier grass include well-drained medium-textured soils, soil pH from 4.5 to 8.2, precipitation from 750 to 2500 mm·yr⁻¹ with optimum temperature from 25˚C to 40˚C [22] [23]. Reference [5] noted that Napier could tolerate drought for a short spell and regenerate with rains. Areas receiving average annual rainfall of more than 1910 mm are envisaged to register a high DM yield of approximately 26.5 tons/ha/yr and cutting intervals of 7 weeks compared to 5.5 tons/ha dry matter from areas with 612 mm of average annual rainfall. Crop development does not progress under a base temperature of 15˚C and is sensitive to frost, though it can regrow from the stolons if the soil is not frozen [24]. Relatedly, [20] reported a base temperature of 10˚C for growth to progress. Reference [3] indicated that to improve forage quality, commercial forage producers need to implement better forage agro-ecological practices (38%), followed better soil testing and feed standard facilities (25%) and feed by the use of improved/new varieties (21%). Another study by [25] showed that using improved forage technologies (IFT) required lower total production costs per season, and higher average milk production per cow per season compared to the farmers using traditional technology. As such, they had significantly five times higher revenues and gross margin than farmers using traditional forage technologies. Currently practiced agro-ecological practices include crop rotation, intercropping [26], multi cropping, crop diversification and soil fertilization. Agroecological practices involved use of intercrop for Maize-Cowpeas, Maize-lablab, Sorghum-lablab, Sorghum-cowpeas, Chloris gayana-Desmodium-Siratro, Elephant grass-Desmodium intortum mixture and Elephant grass occasionally supplemented with grazing. With the exceptional of irrigation, commonly used local agronomical practices were followed in all respects. Reference [21] in China observed that the interaction of irrigation and application of 300 kg N·ha⁻¹ could possibly reach a DM yield potential of 12 ton·ha⁻¹ compared to no irrigation.

3. Water Management Constraints

3.1. Water Limited Conditions

The sustainability of irrigated agriculture is mostly threatened by increasing scarcity of water. According to [23] water for forage crops remains the most critical factor in Uganda. In regions where dairy farming is predominant, Napier is grown in irrigated upland situation. Reference [27] noted that 80% of the yield occurs during the rainy season, thus the forage production is highly susceptible to seasonal water stress due to either waterlogging or drought. Reference [28] observed that Napier is expected to experience lengthy rainfall fluctuations which might prompt water stress during a growing period since it is a perennial crop. Moreover, yield potential of some cultivars reduces by 20% when grown under water-deficient conditions compared to a control environment [28]. Therefore, seasonality of fodder as a consequence of climate change is one of the major constraints in meeting agro-pastoral demands in Uganda. It is occasioned by frequent and erratic droughts some of which are prolonged. Thus, integration of irrigation management strategies in Napier production is envisaged to avoid risks of yield penalty [4] [5] [21] [29]. However, [30] noted that irrigated agriculture is still practiced in many areas of the world with complete disregard to basic principles of resource conservation and its sustainability. Henceforth, irrigation water use amidst water scarcity requires efficient utilization aiming at maximizing its productivity [31].

3.2. Water Use Inefficiency

Water Use Efficiency (WUE) has long been recognized as a key constraint on crop production and a vital target for water management [30]. Consistent with [7], successful Napier production is influenced by the ability to attenuate the trade-off between DM production and potential yield when subjected to stress conditions. Napier grass undergoes adjustments in its morphology (i.e., reduced stomatal conductance, leaf rolling, and enhanced water use efficiency) when grown under water limited conditions [28]. An immediate relation therefore exists between biomass production and water consumed through transpiration [32]. Water stress and reduced transpiration result in a reduced biomass production that normally also reduces yields. Reference [5] ’s study estimated that the minimum WUE of Napier coincides with the minimum ET as observed during fifth cut (Figure 2).

Figure 2. Mean crop evapotranspiration (ET) and water use efficiency (WUE) of Napier (Source: [5] ).

The findings are consistent with [33] that yield response to water approach linked a reduction in evapotranspiration to a proportional reduction in yield. This implies that water supply limitations will likely impose greater constraints on future crop production across diverse growing regions and crop types, increasing the need to understand and improve WUE [34]. To better explore the impacts on Napier yield of optimized WUE by manipulating crop morphology, there is need to conduct a simulation study using the crop models.

3.3. Inefficient Irrigation Scheduling

Irrigation scheduling primarily aims at attenuating wasteful losses of water (percolation beyond what is necessary for salt leaching, surface runoff and evaporation) and maximizing transpiration, which is the valuable loss of water due to its direct link with dry matter production [35]. To achieve higher irrigation efficiency, water management must be improved through optimized irrigation scheduling [29] [36]. The results are consistent with [4] ’s observations that the controlled and timely application of water through drip irrigation enhances fodder yields leading to more effective utilization and resource conservation of available water. Water management will not be reached if we have not considered the irrigation schedule and calculated the precise amounts of different crop water requirements. Consequently, water requirements from rivers, lakes and aquifers for irrigation will be under control. Therefore, irrigation scheduling under variable annual rainfall requires a good decision support tool (DST) to manage rainfall uncertainties. The yield function tends to be uncertain due to the effort in estimating the water losses to inefficient application (like evaporative losses), deep percolation and surface and subsurface runoff, particularly when variability of weather is associated.

Efficient irrigation scheduling methods such as irrigating based on a water budget i.e., crop evapotranspiration (ET) and soil water monitoring minimize over-irrigation while not affecting yields and subsequently decreasing nutrient leaching [29] [37]. The ET estimation approach involves computing the reference evapotranspiration (ETo) using meteorological data (e.g., temperature, solar radiation, relative humidity, and wind speed). Widely accepted equations for estimating ETo are the Food and Agricultural Organization of the United Nations (FAO) Penman-Monteith [38] and the American Society of Civil Engineers-Environmental and Water Resources Institute (ASCE-EWRI 2005). Crop coefficients (Kc) relate evapotranspiration from the reference crop (ETo) to evapotranspiration rates (ETa) of a crop of interest (i.e., ETa is a product of Kc and ETo) [38]. The availability of Kc values is one of the limitations of using ET-based irrigation scheduling because time and financial resources are required to develop kc values, and once developed, they remain cultivar, site, stage of crop growth, plant size, and site specific.

Changes in crop morphology play a fundamental role in regulating water-use efficiency, they present a key target for improving WUE. Soil water sensors have been used to estimate soil moisture in the root zone [39]. These can be integrated with irrigation control equipment to automate irrigation scheduling at set soil water stress thresholds. Specific soil water stresses include canopy expansion, allowable depletion, stomatal closure, and canopy senescence each with a threshold depletion level. Reference [40] observed that an allowable depletion level that avoids stomatal closure should be selected if drought stress during the sensitive growth stages only has a negative effect on the Harvest Index (HI). The limitation with irrigation scheduling based on soil water content monitoring is brought about by the cost to represent the range of conditions in the field.

4. Irrigation Water Management Strategies

4.1. Precision Irrigation

Reference [41] noted that irrigation management must put into consideration characteristics such as water requirements (e.g., seasonal, average, annual and daily water use), root system development, critical stages of growth, soil characteristics, irrigation system, and available water supply. While variations in yield between cultivars can be large [17], the variation in water use under optimum irrigation is much smaller, as water use is primarily controlled by evaporative demand [38]. This is because under conditions where water is scarce, water productivity (WP) may be more important to the farmer than an emphasis on production per unit area [42]. Reference [42] examined strategies to improve the water productivity of irrigated Napier systems to remain economically viable. These included modification of irrigation strategies to reduce water use whilst maintaining WP and using cultivars that can survive and still be productive under reduced irrigation and then recover when full irrigation is restored. A similar observation by [43] pointed to the fact that the actual irrigation depths caused a quadratic response on pasture yield. In an attempt to optimize water use for irrigation, there is significant uncertainty in the anticipated yield results and, often the alternatives that anticipate higher net returns also have higher risks [30]. Conversely, precision irrigation (PI) or irrigation depths that applied water volumes close to ETo promoted considerable increases in yields. PI corresponds to the water requirement enabling the actual crop evapotranspiration to be equal to its potential evapotranspiration [44]. Therefore, integration of irrigation in Napier production is envisaged to avoid risks of yield penalty [4] [20]. Reference [43] noted that studies related to fodder yield responses to different irrigation depths are hardly in the literature. Henceforth, there is a dire need to close the knowledge gap on response of Napier productivity to different irrigation water management strategies [6].

4.2. Deficit Irrigation

The functionality of irrigation is not only to provide sufficient water for crops in order to achieve better outcome in production, as implied in conventional irrigation definition but must be also contributing to improving the features such as WUE [4], crop water productivity, and water saving potential. Deficit Irrigation (DI) refers to a variable management strategy and its effective implementation is dependent on a thorough irrigation schedule, in terms of both application amount and timing. Therefore, there exist several possibilities (e.g., growth-stage-specific DI, intermittent DI (irrigation is applied on specific days) [39], and root zone soil moisture depletion) when exploring and implementing a DI management approach. In each case, [40] observed that different water depths can be applied. Reference [21] observed that applying 0.5 × ET₀ increases Napier’s DM yield by 44% compared to production under rain-fed conditions. A validation by [45] observed that a slight decrease of yield in the earlier phenological stages could be compensated in the later stages when applying DI. It has been successfully applied in dry regions overseas to improve WUE in many horticultural and annual crops [46]. According to [40], DI is an optimization management strategy in which irrigation is only applied during water stress sensitive growth stages of a crop. An analysis by [40] also imparts that by selecting an allowable depletion level that avoids severe water stress (i.e., when the available soil water is far below the stomata closure threshold) during sensitive crop growth stages, Water Productivity (WP) and Harvest Index (HI) can be maximized. So, inevitability there is a need to optimally determine the level of depletion during the sensitive crop growth stages to avoid inducing stomatal closure. Similarly, [47] acknowledged that identifying optimal DI strategies can potentially save water without imposing yield penalties during crop growth. However, its application on forage crops has not been extensively explored in Uganda. Thus, accurate information on the response of Napier to water shortages is required, if implementation of a deficit irrigation strategy is to be successful. Therefore, undertaking studies relating water regimes of Napier that subsidize the choice of the irrigation system and its management is long-awaited [43] [45] [48]. A similar observation by [29] indicated that the relationship between the crop water stress and yield is very important in scheduling for deficit irrigation. A point in case is canopy expansion stress threshold for Napier grass which is triggered at 50% of available soil water under controlled greenhouse conditions [28]. Irrigation scheduling based on soil water content monitoring is most triggered at 40% - 50% of allowable depletion determined by constantly monitoring the soil water status and estimated as follows in Equation (1):

$\text{Depletion}\left(\%\right)=100\times \frac{1}{n}{\displaystyle {\sum }_1^n\frac{F{C}_i-{\theta }_i}{F{C}_i-W{P}^{\prime }}}$. (1)

where n is the number of sub-divisions of the effective rooting depth used in the soil moisture sampling, FC_i is the soil moisture at field capacity for ith layer, θ_i is the soil moisture in ith layer, and WP is the soil moisture at permanent wilting point.

4.3. Application of Biophysical Models

The complexity of crop responses to water deficits had often led to the utilization of empirical production functions as the most practical choice to assess crop yield response to water. Crop growth simulation models have been illustrated to be powerful tools that can provide pre-season decision-making on cropping patterns and irrigation strategies. Biophysical models have a pivotal role to play in evaluating irrigation management strategies for improving agricultural water use and exploration of new practices [49]. The FAO AquaCrop model is less costly and utilized in objective decision-making and in the selection of crops prioritized for irrigation in areas of limited water resources [50]. The model introduced relative simplifications and a small number of crop parameters to typify the crop than other models without negatively affecting its performance in terms of biomass. AquaCrop enables the user to simulate the combined positive and negative effects of drought stress on HI adjustment during yield formation and to derive the mathematically optimal level of depletion during sensitive growth stages. It has successfully been parameterized to simulate crop growth and yield as influenced by varying soil moisture environments for crops like rice [50] [51], maize [52] [53], cabbage [54], cotton [36], barley [55], sunflower, Bambara groundnut [56], and wheat [57] [58] [59]. Reference [36] illustrated the potential of AquaCrop model by developing irrigation scheduling scenarios in cotton production. The results showed that peak irrigation water productivity is obtained by application of a single irrigation at the seedling stage in a wet year, two irrigation events at the seedling and squaring stages during a normal year and three irrigation events at the seedling, squaring and flowering stages during a dry year. Reference [40] used the AquaCrop model to establish a linkage between yield response of Quinoa (Chenopodium quinoa Willd.) and varying irrigation water management strategies through development of DI schedules. The findings indicated that for a field with medium developed dry biomass production (B) at anthesis, irrigation should be applied every 5 days between 70 and 100 days after sowing (DAS) and every 4 days between 100 and 120 DAS. In eventualities of poor B development until anthesis, the irrigation frequency should be lower (every 7 days) between 70 and 90 DAS, but is similar to the former case between 90 and 120 DAS.

5. Future Perspective

A combination of field experiments and series of climate data with crop modeling is envisaged to enrich the experimental study results by developing scenarios not previously considered [60]. Scenario to be developed include 1) irrigation schedules for maximum production (e.g., in dry, normal, wet year); 2) comparison between attainable and actual yields in fields; 3) crop responses to different agronomic practices; and 4) best use of stored soil water when irrigation supply is limited. A field research experiment will be conducted in this regard to provide an opportunity to assess the effect of irrigation water management strategies on Napier productivity [24] [43] [45] [48]. Reference [49] noted that AquaCrop Model results obtained from representative fields are can be utilized to upscale field productivities to the watershed level. If upscaled to other crops and basins, the presented strategy can be a simple and illustrative decision support tool for sustainable intensification.

6. Conclusion

As demonstrated by the study, it is evident that there is inadequate information on response of Napier productivity to different irrigation water management strategies. Numerous strategies including precision irrigation, deficit irrigation and crop modelling have been assessed and implemented from the perspective of water resource conservation and its sustainability and maximizing productivity. However, in an attempt to optimize irrigation water, there is significant uncertainty in the anticipated yield results and, often the alternatives that anticipate higher net returns also have higher costs involved and risks. To reduce uncertainty and risk, biophysical models like the FAO AquaCrop that simulate irrigation performance indicate potential to aid in assisting water managers to optimize a limited supply of irrigation water and developing scenario simulations. Unlike fodder crops, most herbaceous food crops have their built-in crop parameters in the AquaCrop model, therefore it is very imperative to first parameterize the model using experimental data.

Acknowledgements

The authors are thankful to the Lecturers and colleagues at the Department of Agricultural and Biosystems Engineering, Makerere University for their instrumental efforts which made this review a success.

Conflicts of Interest

All authors declare that they have no potential competing interests.

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