Application of Crop Modeling in Multi-Cropping Systems for Maximize Production and Build Resilient Ecosystem Services

Abstract

One of the main challenges in the transition to more sustainable agriculture is designing and selecting agricultural systems that are stable and perturbation resistant. Crop diversification is now recognized as a decisive part of sustainable agroecological development. It is one of the crucial agroecological practices that prove ecosystem services such as nutrient cycling, biological N fixation, pest and disease regulation, erosion control, climate regulation, soil fertility maintenance, biodiversity conservation, and carbon sequestration. To maximize these desired outcomes, understanding, designing, and optimizing, the adoption of crop diversification is crucial for the sustainability of food production under low-input practices. One approach to building sustainable food security and optimal management systems for limited resources is through the application of crop simulation models in multi-cropping systems. Indeed, some models can be used to simulate intercropping systems such as DSSAT, APSIM, ALMANAC, STICS, and FASSET. Thus, the application of such powerful models provides an option to redesign crop mixtures in appropriate sowing proportion and sowing date to tackle the enormous challenges facing agricultural development. In this regard, this review intended to assess existing suitable model to simulate multiple cropping systems and its role in building resilient crop production and ecosystem services without damaging the environment. It also highlights the key role of crop diversity as an ecosystem service provider to guarantee plant productivity in emerging systems of sustainable agriculture.

1. Introduction

One of the current emerging challenges in agricultural sectors is to ensure increasing food demand for the growing world population and built resilient (agro) ecosystem services. Intensive agriculture is often proposed as a solution to feed the growing global population [1] with greater inputs of agrochemicals, water, and others [2], regardless of the environmental consequences [3, 4, 5]. However, this in turn can cause another environmental issue (s) such as a reduction in soil quality, loss of biodiversity, disease, and pest resistance, reduction in water quality, and high dependency on fossil fuel-based energy, and/or contribute to greenhouse gas emissions [2, 3, 6, 7, 8, 9]. These consequences led to a search for new pathways leading to promoting new strategies that can sustain agriculture production without jeopardizing human health and ecosystem services [2, 10].

One possible approach would be a diversification cropping system that is a key factor in developing a more sustainable agroecological system [1]. A key agroecological principle in agricultural production systems is diversification in time (rotation) and space (intercropping, agroforestry systems, biofertilizers, and cover crops) via mechanisms of competition, facilitation, complementarity, and compensation [11], which lies in how much diversified the agro-production system is and the positive interaction among the diverse components of the system [12, 13]. Multispecies cropping systems can significantly reduce fertilizer overuse problems thereby minimizing the environmental impacts of agriculture while maintaining high food production [14]. It proved ecosystem services such as nutrient cycling, biological N fixation, pest and disease regulation, erosion control, climate regulation, maintenance of soil fertility, biodiversity conservation, and carbon sequestration [15, 16]. The agroecological practice of intercropping, meaning the simultaneous cultivation of two or more species in the same field [4] and specific sequences [12, 17], has recently gained renewed interest as a means of ecological intensification [11].

According to Maezieux et al.’s [6] review of agroecosystems, biodiversity may (i) contribute to constant biomass production and reduce the risk of crop failure in unpredictable environments, (ii) restore disturbed ecosystem services, such as water and nutrient cycling, and (iii) reduce risks of pests, and diseases through enhanced biological control or direct control of the pest. To maximize these desired outcomes understanding, designing, and optimizing the adoption of crop diversification the system is crucial for the sustainability of food production under low input practices. Thus, the application of crop modeling is a powerful tool that provides an option to redesign crop mixtures, sowing proportion, plant arrangement, and sowing date and to tackle the enormous challenges facing agricultural development [18]. It is a useful tool in capturing the interactions among climatic conditions, soil types, and nutrient dynamics in cereal-based farming systems and generates knowledge for aiding agricultural developments that would otherwise be impossible through field experimentation [18, 19].

The study by Tsubo et al. [20] demonstrates the possibility of applying a crop simulation model to assess the growth and yield of cereal-legume intercropping over time and space. Carberry et al. [21] and Berghuijs [22] demonstrate the capacity of the Agricultural Production Systems Simulator (APSIM) crop model to simulate competition between species (intercropping) in an agricultural system. APSIM is also able to simulate the soil carbon, water, and nitrogen balances arising from interactions between different crops and pastures grown in rotation [23]. The experiment of Yi-tao et al. [24] suggests that the suitability DNDC model could be used to simulate yield production and N uptake in intercropping systems in the North China Plain. Similarly, Brisson et al. [25] adopt the STICS model to the intercrop model by extrapolation of a sole crop model and concluded that it is useful to evaluate various combinations of crops, including arable crops, forage, and perennial crops. Moreover, Baumann et al. [26] analyzed the competition, crop yield, and plant quality in an intercropping system using an eco-physiological model (INTERCOM). Besides, Berghuijs et al. [27] developed a novel, parameter-sparse process-based crop growth model (Minimalist Mixture Model, M3) to simulate strip intercrops, and proposed that total intercrop yield can be improved by selecting specific traits related to the phenology of both species. Pembleton et al. [28] approve the resilience of forage crops to climate change scenarios as an important component of dairy forage production in southeastern Australia using APSIM crop modeling. In this regard, this study aims to review the existing suitable models applied to simulate multiple cropping systems and their role in building resilient crop production and ecosystem service to feed dramatic population growth without damaging the environment. It also highlights the key role of crop diversity to build resilient crop production and ecosystem services to safeguard food, water, and environmental quality.

1.1 Core principles of agroecology

Agroecosystems are the most intensively managed ecosystems, capable of producing a high harvestable yield through the application of optimal agrochemical and energy management techniques [29] while maintaining the robust vitality of the soil and other environmental conditions [5]. The most commonly used definition of agroecology is an application of ecological concepts and principles for the study, design, and management of sustainable agroecosystems. The application of ecological principles in agriculture is a key part of the global response to climate instability for meeting significant increases in our food needs [30]. The core principles of agroecology are (1) planning and securing the health of the whole system by enhancing beneficial biological interactions and synergisms; (2) minimizing the use of external resources and optimizing the use of nutrients and energy on the farms; and (3) promote agro-biodiversity [31]. These agroecological principles inspire a variety of farming practices such as conservation tillage, crop rotation and fallowing, cover crops, and mulching, mixing crops in a single plot, mixed crop-livestock systems, integrated nutrient management, efficient water harvesting, agroforestry, and holistic landscape management [31]. These practices can help improve soil health and carbon sequestration, water quality and nutrient flows, and control pests and diseases, and they can make farming systems more climate resilient [15]. By reducing dependence on external inputs, agroecology can reduce producers’ vulnerability to economic risk and enhance the ecological and socioeconomic resilience community [30]. Setting up multiple cropping systems to maintain crop production while significantly reducing inputs (mineral fertilizers, water, energy, and pesticides) and providing regulation and cultural services requires much more than an understanding of species coexistence and the identification of species functions [4].

1.2 Concept of resiliency and sustainability in the agriculture system

The concepts of production, efficiency, stability, and resilience lie at the heart of natural ecosystem characterization by ecologists. Sustainability in agriculture is the practice that meets the needs of the current generation without compromising the needs of future generations [32, 33] via stable, equitable, and profitable applications of ecosystem management practices. The goal of sustainable agriculture is to maximize the net benefits that society receives from the agricultural production of food, fiber, and ecosystem services [33] by maintaining existing productivity and enhancing sustainability. This will require increased crop yields and resource use efficiency based on ecological management practices [33].

The term resilience in agroecology is defined as the greater capacity of an ecosystem to withstand and recover from various forms of stress, including herbivorous pests, diseases, droughts, and floods [3, 30, 34]. The resilience of the crop production system also refers to the largest departure from the optimal conditions that the crop production system can sustain without losing its production capacity [35]. It is constructed or emerges through the aggregation of two or more mutually reinforcing livelihood outcomes [36]. Essentially, resilience is measured in three ways: (1) the amount of change the system can undergo and still retain the same controls on function and structure; (2) the degree to which the system is capable of self-organization; and (3) the ability to build and increase the capacity for learning and adaptation [37]. This confirmed that resilience is related to the ability to ensure and guarantee system functions in the face of economic, social, environmental, and institutional disturbances through robustness, adaptability, and transformability [38]. Increasing the resilience of agricultural livelihoods is key to making sustainable development by monitoring and predicting crisis and disaster risks in the agriculture sector [30].

1.3 Role of plant diversification for resilient ecosystem services

Recent agriculture has minimized diversity in favor of vulnerable monocultures and such systems show intrinsically less stability and resilience to perturbations. Diversity among and within species provides insurance or a buffer against environmental fluctuations because different species and varieties occupy different niches and respond differently to change [36]. Agroforestry, intercropping, conservation agriculture, doubled-up legume cropping, fertilizer micro-dosing, planting basins, and push-pull technology were identified as key agronomic innovations widely promoted in sub-Saharan Africa [39]. These outcomes, in turn, could lead to an increase in the resilience of rural households and communities concerning environmental, socioeconomic, and climatic stresses [36]. The impacts of agroforestry on crop yield, soil quality, and pest control are context-specific and depend on the ecological conditions, the type of tree species, and the type of crop. Because of their deep roots and year-round vegetation cover, agricultural systems with trees and shrubs are inherently more sustainable and efficient in using plant nutrients than annual systems without trees [39]. Promoting the cultivation of leguminous crops, grasses, shrubs, and trees offers multiple advantages, for example, augmenting crop and soil productivity that is adapting to climate change by increasing the resilience of agroecosystems [40].

Diversifying farming systems can provide significant ecological and economic benefits and such as food and nutritional security, income generation, and better health [35, 41]. It is perceived as a strategy to simultaneously achieve high productivity and maintain environmental sustainability [42]. It can also provide a variety of ecosystem services depending on the type (positive, neutral, or negative) and degree of interaction between biodiversity and local environmental conditions, which affect ecosystem functioning as well as the economic status of the community [4]. This kind of diversity can also provide ecosystem services, for example, regulation and control of pests and diseases, rehabilitation of fields with poor soil fertility, reduced soil erosion, sustenance of pollinator diversity, and support of below-ground biodiversity [43]. These benefits could reduce the financial, environmental, and personal health risks that usually result from a high level of (externally sourced) agricultural inputs, which is crucial for achieving global food security [36]. Generally, the main ecosystem services provided by multi-cropping are benefits for crop production (e.g., yield quality, quantity, and stability), improvement of soil biogeochemistry, improvement of biological pest control/management, and climate regulation by mitigating greenhouse gas emissions [13].

1.4 Intercropping and yield stability

Intercropping is a way to increase diversity in an agricultural ecosystem. According to the review of Bedoussac et al. [16], intercropping leads to (i) higher and more stable grain yield (0.33 versus 0.27 kg m⁻²), (ii) higher cereal protein concentration (11.1 versus 9.8%), (iii) higher and more stable gross margin (702 versus 577€ha⁻¹), and (iv) improved use of abiotic resources according to species complementarities for light interception and use of both soil mineral nitrogen and atmospheric N₂ than mean sole crops. Similarly, intercropping provides insurance against crop failure or unstable market prices for a given commodity [44]; increases food security in vulnerable production systems [45]; and is a feasible entry point to ecological intensification [46]. Thus, it offers greater financial stability than sole cropping, which makes the system particularly suitable for labor-intensive small farms. Besides, intercropping allows lower inputs through reduced fertilizer and pesticide requirements [22], thus minimizing the environmental impacts of agriculture [44, 47]. Thevathasan and Gordon [48] revealed that the tree/crop agroforestry system was four times more C sequestration potential in the fast-growing tree than that of conventional agricultural fields. Because of reduced fertilizer use and more efficient N-cycling, the tree-intercropping systems could also lead to the reduction of nitrous oxide emissions from the agricultural field [4, 5, 15, 16]. This system also increases soil organic carbon content, bird, insect, and earthworm diversity abundance and distribution, which indicates a sustainable land-management option for long term-productivity [43, 48].

Diversity at all levels, from genetics to the ecosystem, enhances the ability to crop systems to overcome and adapt to forthcoming changes [49]. It reduces inter-specific competition by enhancing complementarity or facilitation processes thereby improving the exploitation of resources, which in turn reflected in the increase in plant production corresponding to greater efficiency of the agroecosystem as a whole [50]. Along with food safety, biodiversity supports healthy and nutrient-rich diets, enhances the efficiency of agroecosystems, and boosts resilience to changing environmental conditions, climate risks, and socioeconomic challenges [51, 52, 53]. Sunflower intercropping with alfalfa proved the most appropriate and stable yields than sole cropping [49]. Similarly, intercropping may contribute to the mitigation of climate change, for example, by reducing the need for fossil-based N fertilizer, mechanical weed control, and the associated N₂O and CO₂ emissions [11]. Legumes used in intercropping and doubled-up legume technology reduce reliance on nitrogen fertilizer and pesticide inputs then lower the GHG emission [39]. Generally, compared to intensive agriculture, intercropping optimizes ecosystem services such as yield stability, utilizes resources efficiently, suppresses pests and diseases, mitigates climate change, controls soil pollution, and increases on-farm biodiversity intercropping through reducing the use of agro-chemicals [5, 54, 55].

2. Crop simulation models (CSM) in multiple cropping systems

2.1 Modeling interspecific competition

Crop simulation models (CSMs) as decision support tools for intercrop/multi-crop systems and future directions for modeling multi-crop systems [56] were developed to simulate soil–plant-atmosphere interactions by considering environmental variables, genotype-specific traits, and their response to the environment using daily through mathematical equations [58] to make research, the management, or teaching more effective [59]. They are useful tools to examine the feasibility of agricultural management systems and can be used to examine the effect of trees within cropping systems [60].

Models dealing with interspecific competition get more and more important as the postulation for sustainable agricultural production has become a global political issue [60]. To model intercropping in terms of neighboring effects in the context of field boundary cultivation, Knörzer et al. [61] developed and integrated a new model approach into the DSSAT model. Different models are considered for modeling interspecific competition in different ways, for example, DSSAT [60, 62], DNDC [24, 55]; ALMANAC [64, 65], APSIM [55, 58], ERIN, FASSET, GAPS, GROWIT, INTERCOM, KMS, NTRM-MSC, SIRASCA, SODCOM, SOYWEED, STICS [66], VCROPS, and WATER-COMP [61]. One main strength of these models is that they consider the effects of several abiotic stresses (e.g., water, N, and temperature) and their interactions on crop performance providing a quantitative estimate at a relevant scale (e.g., yield ha − 1). Similarly, models such as Model Soil, Water, Atmosphere and Plant (SWAP2 × 1D), World Food Studies (WOFOST) [67], CROPSYST (Cropping Systems Simulation Model [57, 68], Daisy, Environmental Policy Integrated Climate (EPIC) and Agricultural Policy/Environmental eXtender (APEX), Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) [64, 65], WaNulCAS, and Hi-sAFe are capable of simulating growth and the yield of the crop with response to their environment and management of multiple cropping systems [19, 56, 61]. Among the numerous crop growth models, the most widely used models are the DSSAT and APSIM models. They are potentially relevant for addressing the performance of crop mixtures compared to that of sole crops under a variety of environmental conditions, such as drought or nutrient limitations [61].

Since early studies, two dominant crop simulation model types are mechanistic and empirical [56]. Mechanistic (eco-physiological) or process-based crop models (PBCMs) simulate the growth, development, and performance of crop plants by modeling their underlying physiological processes, and the coordination and integration of these processes at the whole-plant and canopy scales based on “focal plant–neighbor plant” interactions [69, 70]. The physiological processes incorporated into PBCMs can include photosynthesis, transpiration, respiration, organ development, and assimilate transport. For process-oriented models, the turbid layer medium analogy (where the canopy structure is described by statistical distributions.) has proven to be the most useful [61]. The majority of CSMs use the mechanistic approach to model crop systems. On the contrary, empirical (descriptive) models are direct descriptions of observed data used to estimate final yield and are generally expressed as regression equations with one or a few factors. They are useful for making predictions within the range of data used to parameterize them but are not suitable for extrapolation. Such formal description at the logical level may perfectly reflect the properties of a real system in the “entry-exit” terms within a relatively narrow class and a limited range of affecting factors but is almost not associated with the essence of physical, chemical, and biological effects in the soil–plant-atmosphere system example Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model that can only supported by experimental data [69].

2.2 How modeling build resilience multi-cropping system?

2.2.1 Model can provide an opportunity to assess the suitability and sustainability of cropping systems under projected climate change

Cereal-legume intercropping has a substantial impact on enhancing higher yield, yield stability, and food security community [71]. The yield stability of intercropping systems is important in developing cropping systems that produce economic yields in response to variations in the environment due to years and locations [72]. Changing climate adversely affects agricultural productivity and creates food insecurity. Crop growth models are modern and efficient tools that have been extensively used in mounting the climate change impacts and developing adaptation packages for sustainable crop production in changing climate [73]. They suggested that the negative effect of future climate change on maize production systems can be minimized or overcome by modifying the sowing dates and fertilizer (fertigation) and developing heat and drought-tolerant hybrids. A study by Msongaleli et al. [63] approves the applicability of DSSAT and APSIM crop simulation models as tools for assessing possible impacts of climate change on sorghum under projected climate scenarios. Similarly, Chimonyo et al. [74] applied the APSIM model and recommend that changing plant populations and sequential in maize landrace with Bambara groundnut intercropped system increase yield and WUE under projected climate change. This allows for the identification of short-, medium-, and long-term strategies to aid in mitigating the impacts of climate change on productivity and WUE. They stated that crop diversity could enhance crop productivity, stability, and thus food security, through efficient water utilization. Shili [75] test the impact of a living cover crop on the agronomic and environmental performance of the system for different climatic and technical scenarios using the STICS crop model adapted. They found that, in most climatic scenarios, the emergence of the fescue crop during the late tillering phase of the wheat crop gave the best compromise between wheat yield overall nitrogen accumulation and radiation interception. Furthermore, APSIM could adequately simulate expert knowledge, that is, expected yields, and of important crops with adequately simulated competitive effects in maize-bean intercropping systems [76].

2.2.2 Explore water management, GHG regulation, and adaptation strategies

The increase in the concentration of greenhouse gases (GHGs) in the atmosphere has led to an elevated concern and urgency to adopt measures for carbon (C) sequestration to mitigate climate change. Carbon sequestration plays a major role in mitigating climate change by converting atmospheric carbon into long-lived wood biomass and soil carbon pool [77]. Agriculture is the second-largest contributor to GHG emissions in terms of CO₂ eq. contributing about 31% of Africa’s total emissions [39]. Most practices can lower emission intensities (e.g., intercropping, conservation, agroforestry) and biomass (e.g., agroforestry) by reducing direct and indirect soil N₂O emissions and by increasing the amount of carbon stored in the soil [39]. Integrating strip intercropping, conservation tillage, as well as straw mulching, significantly boosts crop yields, improves resources use efficiency, and alleviates food security in arid areas while lowering the carbon emissions from farming [78].

Mathematical models have the promising potential to explore solutions to water management problems. Archontoulis et al. [79] test and calibrate APSIM for the crop, soil water, soil N, surface organic matter, manure, and soil temperature and prove to be a reliable model that can be used as a research and decision tool for the agricultural system. Fung et al. [55] stated that DNDC model able to assess the environmental values of intercropping in terms of alleviating air pollution and safeguarding a sustainable food supply. They show that maize-soybean intercropping systems have the potential to save 42% of fertilizer application, compared with their monoculture counterparts, while producing high yields, improving both fertilizer-use and land-use efficiencies. Crop environment resource synthesis (CERES) in DSSAT and world food studies (WOFOST) in SWAP were used to simulate the growth, development, and soil water balance based on field experiment data [80]. Similarly, Hernández-Ochoa et al. [19] identified and tested the robustness of some models (e.g., APSIM, DSSAT, EPIC, STICS, and WOFOST) for the regulation of GHGs by simulating N₂O emissions and mitigation of climate change. Banerjee et al. [62] assess the impact of projected climate on the growth and yield of rice-lentil-groundnut cropping sequence using DSSAT and suggested that rice benefited from preceding groundnut and residue, hence, could sustain the yield in a long term.

Application of agroecosystem modeling can also dynamically simulate a diverse set of regulating and provisioning ecological services such as regulation of greenhouse gas emissions, water quality, and soil erosion [19, 80]. Simulation of water quality by simulating soil N retention (via N leaching dynamics) is possible for most models’ examples are CropSyst, DNDC, STICS, EPIC, and APSIM [19]. SWAP2 × 1D and WOFOST can simulate the water balance components and crop growth [67]. Likewise, the STICS growth model simulates crop growth and development, as well as water and N balance to improve understanding of interspecific interactions and explore best options of strategies management [75, 81]. This model also paves a possible way to recycle mineral nitrogen efficiently in multiple cropping systems without any effect on water balance and environmental conditions [75]. Moreover, Araya et al. [82] evaluate the impacts of cropping systems and water management on the yield performance of selected dominant cropping systems in the highlands of Africa using DSSAT modeling and highlight the significance of integrating diverse cropping systems (that include legumes) and water management practices (tied ridges and irrigation) for agroecological intensification. Thus, it helps to control the balance between competition and facilitation then improving the agronomic practice for resilient ecosystem service provision in a holistic manner.

2.2.3 For carefully designing and selecting the best adaptive practice

Cereal-legume combinations are known to facilitate the efficient utilization of nutrients by creating a congenial environment [83]. Plant models able to infer plant–plant interactions can be helpful for the identification of major interaction traits and the definition of ideotypes adapted to a targeted intercropping system [84]. The crop simulation model dealt with competition for light and can be used to assess risk for intercrop productivity over time and space in semi-arid regions [20]. Agricultural system models are important tools for understanding complex system interactions to achieve multiple productivities and environmental goals [79]. Models are used extensively for understanding the behavior of the crops in specific environments, and optimization of planting dates, fertilizer application, and crop choice. Multi-cropping systems have potential advantages in productivity, stability of outputs, resilience to disruption, and ecological sustainability [6]. Multispecies systems can also provide other services, linked to the quality of the environment: Trees and cover crops can provide shade and shelter for animals and humans. Although frequent, the advantages and benefits of multispecies systems must not be over-generalized: Not all crops are beneficial in mixtures, since they do not systematically generate ecological and/or economic benefits, and may involve more complex or higher inputs of labor [6]. So, using crop modeling, it is possible to develop an innovative planting design, management practice, and crop varieties for mixed-species plantation [22] through ecological, agricultural, and genetic concepts and approaches [4, 56]. These varieties can modify about criteria of agronomy needs and holistic environmental issues, which lead to higher yields and quality than the corresponding pure crop. Baumann et al. [26] determine ranges of plant densities that enable the intercropping system to meet the current quality standards of the component crops.

Using APSIM modeling, Nelson et al. [85] support the suitability of intercropping to achieve high-yield production or reduce risk under drought and an opportunity to diversify food production. Similarly, APSIM is also used to develop best management practices for improved yield and WUE of sorghum-cowpea intercropping system [86]. DSSAT and APSIM models have been already employed as promising tools to discover likely options for better nitrogen management and water-saving techniques, thereby bringing nitrogen- and water-efficient best management practices to different cropping systems in semi-arid tropics [87]. The results study by Gautam et al. [88] concluded that diversification of rice fallows with the inclusion of short-duration pulses/oilseeds is one of the options to achieve higher profitability, system productivity, and sustainability in the long run. Hoffman [42] proves the usefulness of APSIM model applications for the design of suitable cropping systems in addressing various dimensions of sustainability. They suggest intercropping is a promising option for cropping system diversification.

2.2.4 For optimization of traditional farming systems and yield gap analysis

Traditional farming systems like intercropping or mixed cropping are known to be the embryonic form of sustainable production concerning biodiversity, resource use efficiency, and yield stability [61]. As field trials are time consuming and expensive, models are the alternatives. Agroecosystem models can be used to simulate the basic effects of crop rotation on crop yields, resource use dynamics, and efficiency. Most crop modeling can simulate the performance of intercropping systems in response to the climate and soil conditions and allows the evaluation of management intervention through tillage, irrigation, or fertilization as well as choice, timing, and sequencing of crops such as APSIM [89], STICS [90], and ALMANAC for weed relay intercropping with wheat FASSET, DNDC [24], and INTERCOM [26, 61]. Crop yield simulation is an important component of yield-gap analysis and numerous studies have been published that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), the impact of climate change on future crop yields, and land-use change [91]. Modeling can allow for the verification of estimated yield gaps with on-farm data and experiments [92]. Similarly, Rizzo et al. [93] suggest double-cropped soybean cropping systems as an alternative for increasing grain production in the main agricultural region of the world after analyzing their yield gaps.

2.2.5 For improving land use and management

A landscape generator typically considers different agricultural land use systems including natural, semi-natural habitats, cropland, and landscape elements. Maize-cowpea intercropping with a temporal niche difference is a better option for sustainable crop production and maximizing land use [94]. Meixiu et al. [95] showed that intercropping could be used to obtain more yield on less land with less water by developing and application of dynamic process-based modeling taking into account the acquisition of light and water by the component species. Holzkamper et al. [96] determine the ideal configuration of grassland, farmland (without a specific crop specified), and woodlands for particular bird species in Northwest Saxony, Germany, using a spatial optimization model for land use modification tradeoffs between species habitat appropriateness and management.

APEX is being used in the USDA-NRCS CEAP Cropland National Assessment to evaluate the effectiveness of conservation practices, including the impacts of conservation practices on pesticide losses from farm fields. The optimum setup for species habitats and management was provided by smaller patches and greater diversity of land use including more forest lands and de-creased grassland and cropland. Accordingly, EPIC and APEX models are the most flexible and dynamic tools that can be used to estimate the impacts of land management, conservation practices, and/or climate on a wide range of environmental indicators, including water quantity; wind, water, or channel erosion; soil carbon sequestration; pesticide fate and movement; nutrient (nitrogen and phosphorus) cycling and losses via surface runoff (both soluble and sediment-bound phases); leaching; volatilization; and tile drainage [65]. Plotkin et al. [97] demonstrate the value and utility of APEX in agricultural fate modeling for evaluating the environmental benefits of conservation practices such as residue management and conservation tillage, as well as identifying areas where conservation practices may be required. This shows APEX model can replicate measured stream flow and sediment yields for rangeland watersheds with satisfactory performance based on well-accepted statistical criteria [98].

3. Summaries and conclusions

Multi-cropping systems and agroecological approaches can improve resource use efficiency for both nutrients and water, thereby facilitating low-input agricultural practice. It can help to develop more sustainable and resilient farming systems that combine stable yields with enhanced biodiversity and ecosystem services to feed a growing world population. To further increase sustainability, there is a need to expand the research to consider other management strategies such as the use of other traditional crop species, fertilization, rainwater harvesting, and soil conservation techniques. A key point in future modeling challenges remains the need for creating bridges between ecophysiology, population biology, and functional ecology. Indeed, some models can be used to simulate intercropping systems. These models often include competition for light, water, and N, such as DSSAT, APSIM, ALMANAC, STICS, and FASSET. Similarly, the DNDC model is also able to simulate yield and N uptake for intercropping systems under different N application rates. Thus, the model can explore soil and water management strategies, GHG regulation, and its adaptation mechanism then can provide an opportunity to assess the suitability and sustainability of cropping systems under projected climate change. Based on these modeling outs, one can design and build more sustainable crop production and resilient ecosystem service for the future generation holistically. Finally, for optimizing adoption and use of intercropping for all stockholders, further scientific development in simulation and awareness creation is urgently required. This should relate to the development of strong ethics for sustainable management of soil, water, and natural resources.

Acknowledgments

The authors are highly thankful to researchers whose findings are included directly or indirectly in preparing this manuscript.

Conflicts of interest

The authors declare no conflict of interest.

Funding

The authors received no direct funding for this research.

Data availability

All data generated are included in this article reference’s part.