Introduction
Global agricultural producers are seeking strategies to meet rising food demand driven by population growth. Historically, this demand has been met through intensification practices, including application of synthetic fertilizers and pesticides, mechanization, and expanded labor capacity (Farias et al., Reference Farias, Dubeux, Savian, Duarte, Martins, Tiecher, Alves, de Faccio Carvalho and Bremm2020).
While effective in boosting yields, intensification practices often compromise soil quality (Gonçalves et al., Reference Gonçalves, Silva and de Camargo2025) and raise concerns about the long‑term environmental sustainability and economic viability of food production (Sekaran, Kumar and Gonzalez-Hernandez, Reference Sekaran, Kumar and Gonzalez-Hernandez2021; Kumar et al., Reference Kumar, Anders, Bauer, Blair, Cain, Dere, Druhan, Filley, Giannopoulos, Goodwell, Grimley, Karwan, Keefer, Kim, Marini, Muste, Papanicolaou, Rhoads, Rodriguez, Roque-Malo, Schaeffer, Stumpf, Ward, Welp, Wilson, Yan and Zhou2023). In response, producers are seeking approaches that ensure profitable production without degrading natural resources (He et al., Reference He, Lu, Cowie, Zhao, Liu, Yi, Shi, Zhang, Qiu, Shi, Huete, Siddique, Yu and Li2025; Kumari et al., Reference Kumari, Kumar, Vekariya, Ghetiya, Potluri, Satpathy, Basa and Zuorro2025; LTAR, 2025). Sustainable intensification aims to increase production while minimizing ecological harm and dependence on external inputs (Tilman et al., Reference Tilman, Balzer, Hill and Befort2011; Garnett et al., Reference Garnett, Appleby, Balmford, Bateman, Benton, Bloomer, Burlingame, Dawkins, Dolan, Fraser, Herrero, Hoffmann, Smith, Thornton, Toulmin, Vermeulen and Godfray2013; Petersen and Snapp, Reference Petersen and Snapp2015; NAS, 2021; Malone et al., Reference Malone, O’Brien, Herbstritt, Emmett, Karlen, Kaspar, Kohler, Radke, Lence, Wu and Richard2022). As Hendrickson et al. (Reference Hendrickson, Hanson, Tanaka and Sassenrath2008) emphasized, the sustainability of agroecosystems hinges on efficient resource use that supports profitable production of food, feed, and fiber without compromising environmental quality (Soussana and Lemaire, Reference Soussana and Lemaire2014; Pun and Baral, Reference Pun and Baral2024).
Integrated crop-livestock systems (ICLS) operationalize sustainable intensification by creating closed nutrient and biomass loops between crops and livestock (Walters et al., Reference Walters, Archer, Sassenrath, Hendrickson, Hanson, Halloran, Vadas and Alarcon2016; Walia et al., Reference Walia, Dhawan, Dhawan, Ravisankar, Peshin and Dhawan2019; Moojen et al., Reference Moojen, Ryschawy, Wulfhorst, Archer, Carvalho and Hendrickson2024; Gonçalves et al., Reference Gonçalves, Silva and de Camargo2025; Kumar et al., Reference Kumar, Jain, Nautiyal, Juyal, Difar, Fetrat, Archangel, Krishnan and Dejey2025; Zhuo et al., Reference Zhuo, Hu, Fu, Ji, Yao, Li, Shen and Zhu2025). ICLS leverage crop–livestock complementarities: livestock consume forage and crop residues, convert them into animal products, and return essential nutrients to soils through urine and dung during grazing—maintaining soil fertility (Burkart et al., Reference Burkart, James, Liebman and Herndl2005; Garrett et al., Reference Garrett, Niles, Gil, Gaudin, Chaplin-Kramer, Assmanng, Assmannh, Brewere, de FaccioCarvalhoi, Cortnera, Dynesj, Garbachk, Kebreabl, Muellerm, Petersone, Reisn, Snowj and Valentim2017; Sartor et al., Reference Sartor, Sandini, Adami, Novakowiski and Ruthes2018; Walia et al., Reference Walia, Dhawan, Dhawan, Ravisankar, Peshin and Dhawan2019; Farias et al., Reference Farias, Dubeux, Savian, Duarte, Martins, Tiecher, Alves, de Faccio Carvalho and Bremm2020; Peterson et al., Reference Peterson, Bell, Carvalho and Gaudin2020; Prado et al., Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025; Zhuo et al., Reference Zhuo, Hu, Fu, Ji, Yao, Li, Shen and Zhu2025).
In the U.S., crop-livestock integration occurs at two operational scales: (1) the farm scale, where integration happens on the same land base as cash crops; and (2) the regional scale, where spatially separate livestock and crop operations integrate through agreements that create production synergies (Sulc and Franzluebbers, Reference Sulc and Franzluebbers2014). Each scale presents distinct agronomic, economic, and logistical challenges and opportunities and requires strong management capacity and a commitment to continuous improvement, warranting further scientific study across ecoregions. Because the U.S. encompasses diverse ecoregions with differing weather, economic structures, and social norms, ICLS adoption varies by region, where differences in natural resources, local culture, precipitation and temperature regimes, and incentives/regulations exist (Sulc and Franzluebbers, Reference Sulc and Franzluebbers2014). These interacting factors ultimately shape the technical, economic, social, and environmental feasibility of ICLS and determine the appropriate operational scale.
In Iowa, ICLS adoption has been actively studied as a pathway toward sustainable intensification (Practical Farmers of Iowa, 2021). Practical Farmers of Iowa data indicate that cow-calf operations integrated with row‑crop farming are typically small, family‑owned enterprises—averaging less than one‑third of the U.S. average farm size (544 acres or 2.2 × 106 m2). Some integrated farms incorporate perennial forage strips, hayfields, or small grains (like wheat or oats) into their rotation. Animals graze these sections while the main corn and soybean fields mature. During the corn and soybean growing season, livestock are kept off cash-crop fields to protect the emerging crops, moving instead to permanent pastures, hayfields, perennial grasslands, and nearby feedlot facilities. Accordingly, pilot studies have focused on farm‑scale ICLS implementations, emphasizing economic returns and, more recently, the benefits of incorporating cereal rye (Secale cereale L.) cover crops into corn–soybean rotations for livestock grazing (Practical Farmers of Iowa, 2021) (Fig. 1). Once the cash crops are harvested in the fall, the fields open up again, allowing livestock to return to graze crop residue (stalks) and cereal rye cover crops.
Integrated crop-livestock system at the farm-scale unit incorporating cereal rye cover crops for grazing (Source: Practical Farmers of Iowa).
Recent pilot studies with Practical Farmers of Iowa cooperators—each managing integrated corn–soybean rotations with cattle—grazed cover crops in the fall, winter, and/or spring to evaluate the economic profitability of this practice. Economic returns from grazing cover crops were realized within 1 year in a cow-calf operation (Practical Farmers of Iowa, 2021). Planting cover crops from mid-summer to early fall provided enough forage to support grazing by late fall and early winter. Additionally, planting cereal rye in late summer or early fall supplied forage in the spring, further extending the grazing season (Soder, Reference Soder2021). Grazing cover crops also reduced reliance on hay, significantly lowering winter feed costs. Cover crops were found to increase the feedlot profit margin as a supplement feedstuff, thereby reducing the required amount of TMR (Total Mixed Ration) and contributing to a balanced diet that prevents selective feeding.
Incorporating cereal rye into a traditional corn–soybean rotation—which accounts for 80% of Iowa’s total cash-crop acreage—can also provide additional grazing land beyond existing pasture. This additional acreage creates favorable conditions for increasing livestock stocking rates statewide, measured as grazing acreage per cow-calf (Scaglia et al., Reference Scaglia, Swecker, Fontenot, Fiske, Fike, Abaye, Clapham and Hall2008). According to the 2022 USDA Census of Agriculture, pasture acreage devoted solely to forage for grazing in Iowa has remained nearly unchanged over the past decade at about 1.92 million acres, representing only 6% of all available acreage. Of this, alfalfa production accounts for approximately 95% of Iowa’s forage acreage (NASS, USDA, 2023). Therefore, any increase in forage acreage through cover crop grazing can be a significant added benefit (Poffenbarger et al., Reference Poffenbarger, Artz, Dahlke, Edwards, Hanna, Russell, Sellers and Liebman2017).
Cereal rye cover crops also confer multiple environmental benefits when integrated into corn–soybean rotations. In neighboring Illinois, for example, winter grazing of cover crops increased corn yields by 6%, improved soil aggregate stability by a factor of two or more, and enhanced soil organic carbon compared to continuous corn systems without grazing (Maughan et al., Reference Maughan, Flores, Anghinoni, Bollero, Fernández and Tracy2009). Additionally, integrating cover crops with crop rotation in ICLS was a key driver in reducing weed biomass and lowering reliance on herbicides compared to conventional corn–soybean rotations (Tracy and Davis, Reference Tracy and Davis2009).
Despite these advantages, adoption of ICLS—with or without cover crops—faces challenges too. Excessive grazing can degrade environmental quality through soil compaction and erosion (Liebig et al., Reference Liebig, Tanaka, Kronberg, Scholljegerdes and Karn2011; Delandmeter et al., Reference Delandmeter, de Faccio Carvalho, Bremm, Santos Cargnelutti, Bindelle and Dumont2024; Lou et al., Reference Lou, Mo, Shao, Liu, Chong, Yu, Zheng and Qiu2025). Mitigation is possible: compaction risks decline when grazing is limited to dry or frozen periods, and regions of the U.S. Midwest—including Iowa—with frequent freeze–thaw cycles may experience comparatively lower compaction impacts (Liebig et al., Reference Liebig, Tanaka, Kronberg, Scholljegerdes and Karn2011). Adoption of ICLS can also bring challenges with additional labor or capital requirements and increased management intensity. Realizing the full benefits and some of the unintended consequences of ICLS, therefore, requires effective management, long‑term monitoring, and continued research (Moojen et al., Reference Moojen, Ryschawy, Wulfhorst, Archer, Carvalho and Hendrickson2024; Mubvumba, DeLaune and Gentry, Reference Mubvumba, DeLaune and Gentry2024; Gonçalves et al., Reference Gonçalves, Silva and de Camargo2025; Prado et al., Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025).
In Iowa and throughout the U.S. Midwest, further evaluation is needed to understand how projected weather stressors influence the long-term sustainability of ICLS—especially for projected precipitation variability and rising air temperature (Lobell and Field, Reference Lobell and Field2007; Seo, Reference Seo2010; Hatfield, Reference Hatfield2016; Mujeyi, Mudhara and Mutenje, Reference Mujeyi, Mudhara and Mutenje2021). Because precipitation is more variable than temperature, its effects are harder to quantify within a long horizon (e.g., 100 years) (Izaurralde et al., Reference Izaurralde, Thomson, Morgan, Fay, Polley and Hatfield2011; Gustafson et al., Reference Gustafson, Hayes, Janssen, Lobell, Long, Nelson, Pakrasi, Raven, Robertson, Robertson and Wuebbles2016). By contrast, many ecophysiological processes and livestock responses scale predictably with air temperature, making temperature‑driven impacts more tractable to evaluate over long horizons (Vermeulen, Campbell and Ingram, Reference Vermeulen, Campbell and Ingram2012; Ghaneeizad et al., Reference Ghaneeizad, Papanicolaou, Abban, Wilson, Giannopoulos and Lambert2018). Long-term sustainability is defined by the combined contributions of environmental benefits, economic returns, and social factors (i.e., labor and technical commitment).
An increase in temperature can have both positive and negative implications for crops and livestock. Corn yields, regionally, are anticipated to decline by ~8.3% for every 1 °C rise in average growing-season temperature (Lobell and Field, Reference Lobell and Field2007). The projected 8.3% yield loss per 1 °C comes from modeled temperature–yield relationships showing corn’s physiological limits. Excess heat during flowering disrupts pollination and kernel set, causing pollen sterility, fewer kernels, and faster senescence, which reduce grain fill and yield (Lobell and Field, Reference Lobell and Field2007; Schlenker and Roberts, Reference Schlenker and Roberts2009; Mishra and Cherkauer, Reference Mishra and Cherkauer2010). In the rainfed Midwest, high summer temperatures and increased vapor pressure deficit add further stress (Hatfield et al., Reference Hatfield, Takle, Grotjahn, Holden, Izaurralde, Mader and Liverman2014; Ummenhofer et al., Reference Ummenhofer, Sen Gupta, England, Taschetto, Briggs and Raupach2015; Lee and Durmaz, Reference Lee and Durmaz2016; Hatfield and Dold, Reference Hatfield and Dold2018; Tigchelaar et al., Reference Tigchelaar, Battisti, Naylor and Ray2018). Soybean yield in the Midwest is projected to decline less steeply because soybeans are more heat‑tolerant, with a broader optimal temperature range and a longer vegetative stage that can adjust to warming (Hatfield et al., Reference Hatfield, Boote, Kimball, Ziska, Izaurralde, Ort, Thomson and Wolfe2011; Jin et al., Reference Jin, Armstrong and Tang2019). High temperatures may still reduce yield through increased water stress (Sun et al., Reference Sun, Huang, Ren, Li, Xu, Xiong and Huang2026). Projected weather changes combined with elevated CO₂ generally increase soybean production by 13–17%, partially offsetting yield losses under both Representative Concentration Pathways (RCPs) 4.5 (less extreme warming) and 8.5 (more extreme warming), though CO₂ fertilization alone is unlikely to counter high‑temperature stress in most crops (Kimball, Reference Kimball2016).
Rising temperatures do promote increased erosion and degrade soil organic carbon (SOC), thereby affecting soil quality. Warmer weather leads to more intense rainfall and longer dry spells (Trenberth, Reference Trenberth2005). Heavy storms detach and transport topsoil via the collective action of raindrops and runoff, especially on bare or degraded land (Abaci and Papanicolaou, Reference Abaci and Papanicolaou2009; Wacha et al., Reference Wacha, Papanicolaou, Giannopoulos, Abban, Wilson, Zhou, Hatfield, Filley and Hou2018; Papanicolaou et al., Reference Papanicolaou, Abban, Dermisis, Giannopoulos, Flanagan, Frankenberger and Wacha2018). Extended dry periods promote soil crusting and reduce infiltration, increasing runoff water depth and erosion risk (Papanicolaou et al., Reference Papanicolaou, Elhakeem, Wilson, Burras, West and Lin2015a, Reference Papanicolaou, Wacha, Abban, Wilson, Hatfield, Stanier and Filley2015b; Elhakeem et al., Reference Elhakeem, Papanicolaou, Wilson, Chang, Burras, Abban, Wysocki and Wills2018). Higher temperatures also accelerate microbial activity, speeding organic-matter decomposition and releasing CO₂ (Papanicolaou et al., Reference Papanicolaou, Elhakeem, Wilson, Burras, West and Lin2015a, Reference Papanicolaou, Wacha, Abban, Wilson, Hatfield, Stanier and Filley2015b; Hou et al., Reference Hou, Berry, Singh, Hughes, Tong and Papanicolaou2018).
Rising temperatures also impose heat stress on livestock, reducing feed intake and weight gain if appropriate shading and watering are not considered (Cheng, McCarl and Fei, Reference Cheng, McCarl and Fei2022). At the same time, higher temperatures can expand available grazing acreage and extend the grazing period. Above-ground cereal rye cover-crop biomass is projected to increase by ~27.9% per 1 °C rise (Malone et al., Reference Malone, Garbrecht, Busteed, Hatfield, Todey, Gerlitz, Fang, Sima, Radke, Ma, Qi, Wu, Jaynes and Kaspar2020). Alfalfa dry-matter yield is expected to increase by ~9.47% per 1 °C up to a cumulative warming of 1.9 °C (from a baseline monthly mean of 9.5 °C to 11.4 °C), beyond which the response moderates to ~2.82% per 1 °C (Hay and Forage Grower, 2022).
Our primary objective is to assess how temperature trajectories under different weather scenarios influence the long‑term sustainability of ICLS in Iowa, U.S.—specifically ICLS with cereal rye cover crops (ICLSwCC) and ICLS without cover crops (ICLSwoCC). We evaluate temperature‑driven impacts over long horizons to establish defensible bounds on expected outcomes and provide scenario‑specific envelopes for agronomic and livestock responses for ICLSwCC and ICLSwoCC. Three weather scenarios are considered: a baseline representing 2023 temperature conditions held constant through 2122; RCP 4.5, projecting ~1.98 °C warming by 2122; and RCP 8.5, projecting ~5.14 °C warming by 2122 (Malone et al., Reference Malone, Garbrecht, Busteed, Hatfield, Todey, Gerlitz, Fang, Sima, Radke, Ma, Qi, Wu, Jaynes and Kaspar2020; Peterson et al., Reference Peterson, Bell, Carvalho and Gaudin2020). As a secondary objective, we compare the long-term performance of ICLSwCC and ICLSwoCC with conventional cash‑cropping systems, with and without cover crops (CwCC and CwoCC), under the same scenarios.
Evaluating whether temperature increases enhance or undermine the long‑term sustainability of ICLS in Iowa requires a system‑wide perspective. Such approaches are well-suited to assess how a stressor—the rising temperature—affects individual agroecosystem components (crop production, livestock production, natural resources, economic returns, and social quality), their complementarities, and ultimately the overall system state (e.g., Queenan et al., Reference Queenan, Sobratee, Davids, Mabhaudhi, Chimonyo, Slotow, Shankar and Häsler2020; Metcalfe et al., Reference Metcalfe, Coleman, Zhang, Dixit, Saeed, Mead and Collins2026). System‑wide methods are also effective for evaluating how management adaptations may amplify or offset stressor impacts on integrated performance by disentangling interactions among key components that are otherwise difficult to capture (e.g., Turner et al., Reference Turner, Menendez, Gates, Tedeschi and Atzori2016; Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025).
In this study, we extend the contextual analysis of Walters et al. (Reference Walters, Archer, Sassenrath, Hendrickson, Hanson, Halloran, Vadas and Alarcon2016) and the recent system‑dynamics (SD) modeling of Papanicolaou et al. (Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025) to evaluate the long‑term sustainability of ICLS in Iowa under warming temperatures. Fig. 2 presents the conceptual framework guiding our system‑wide evaluation of Midwestern ICLS under the three weather scenarios described earlier. Agroecosystem impacts are assessed across crop and livestock production, environmental quality, economic returns, and social quality, and management adaptations (e.g., cover‑crop integration, increased watering and shading) are explored as levers. In our conceptual model, we do not model the feedbacks from management adaptations to weather; therefore, the arrow from the stressor box to the oval of Management Adaptation in Fig. 2 is unidirectional. We also assume that cash-crop fields are located adjacent to small feedlot facilities so transportation costs are minimal and have not been considered in our analysis (Practical Farmers of Iowa, 2021). Lastly, we do not incorporate market (price) dynamics and feedbacks in our analysis; weather and increased production intensification are considered the key stressors.
The weather-stressor system‑dynamics (SD) modeling framework for the Integrated Crop-Livestock system in Iowa.
Figure 2. Long description
A flowchart organized around a central title: The U. S. Midwest Regional Integrated Crop Livestock System.At the top, a box labeled Stressors contains two sub-boxes connected by a vertical double-headed arrow: Increased production intensification and Weather. The Weather box has a right-pointing arrow leading to the text: Increase in Temperature, Uncertainty in Rainfall Patterns.From the Stressors box, three thick gray arrows point downward.- The left arrow points to an oval labeled Agroecosystem Impact, which lists: Cash Crop production, Livestock production, Environmental quality, Economic returns, Social Quality.- The center arrow points to the central title.- The right arrow points to an oval labeled Management Adaptations, which lists: Incorporation of cover crops into the corn-soybean rotation for livestock grazing; Installation of livestock Watering system and shading.Below the central title, a thick gray arrow points down to a large box at the bottom labeled Integrated Agroecosystem Performance and Trade-offs under current 2023 and future weather 2023-2122 for the four productions schemes. This box lists four bullet points:- Cropping without cover crop C w o C C.- Cropping with cover crop C w C C.- Integrated crop-livestock systems without cover crop I C L S w o C C.- Integrated crop-livestock systems with cover crop I C L S w C C.A thin black line labeled System-wide evaluation connects the bottom box back to the side ovals and the top Stressors box, forming a continuous feedback loop.
At the top, a box labeled Stressors contains two sub-boxes connected by a vertical double-headed arrow: Increased production intensification and Weather. The Weather box includes a right-pointing arrow leading to the text Increase in Temperature and Uncertainty in Rainfall Patterns.
From the Stressors box, two large curved arrows point downward to the left and right. The left arrow points to an oval labeled Agroecosystem Impact, which lists Cash Crop production, Livestock production, Environmental quality, Economic returns, and Social Quality. The right arrow points to an oval labeled Management Adaptations, which lists Incorporation of cover crops into the corn-soybean rotation for livestock grazing and Installation of livestock Watering system and shading.
A central downward arrow from the regional system anchor points to a large bottom box titled Integrated Agroecosystem Performance and Trade-offs under current 2023 and future weather 2023-2122 for the four productions schemes. This box lists four schemes:
* Cropping without cover crop C w o C C
* Cropping with cover crop C w C C
* Integrated crop-livestock systems without cover crop I C L S w o C C
* Integrated crop-livestock systems with cover crop I C L S w C C
A label System-wide evaluation is attached to the bottom right of this box. Two L-shaped arrows on the left and right sides create a feedback loop, pointing from the bottom performance box back up to the Agroecosystem Impact and Management Adaptations ovals respectively.
The framework is used to evaluate integrated agroecosystem performance for Iowa’s integrated systems with and without cover crops (ICLSwCC, ICLSwoCC) and to compare them with the performance of cropping-only systems with and without cover crops (CwCC and CwoCC) for current and future weather. In its previous configuration, the model was developed, calibrated, and validated to quantify how changes in tillage practices (no-tillage, strip tillage, and conventional tillage) influence three performance indicators—Economic Return (ER), Environmental Quality (EQ), and Social Quality (SQ)—while holding weather constant. Here, we incorporate weather factors into the aforementioned five integrated submodels, namely, Cash‑Crop Production, Livestock Production, Economic, EQ, and SQ, to capture long‑horizon Agroecosystem Impacts under future temperature trajectories.
Corresponding revisions to each submodel enable ecophysiological processes and livestock responses to scale predictably with air temperature. The analysis is Iowa‑focused, and treats increased temperature as the primary stressor; other stressors (e.g., precipitation changes) are acknowledged but not modeled.
Unlike the prior study, here we evaluate the state of the whole agroecosystem using integrated performance metrics and explicit trade‑off analysis, iterating between system‑wide evaluation, impacts, and adaptations (bi‑directional links in Fig. 2). To enable relative comparisons across production systems in future studies, we rank absolute values of EQ, ER, and SQ to construct normalized sector indices—the Environmental Sustainability Index (EnSI), Economic Sustainability Index (EcSI), and Social Sustainability Index (SSI).
Materials and methods
Production systems
The ‘phases’ of the integrated production systems (ICLSwoCC and ICLSwCC) and cropping‑only systems (CwoCC, CwCC) are described in Fig. 3. In our model, a ‘phase’ represents a land‑use category, not a calendar year or growing season.
Diagrammatic overview of the four agricultural production systems analyzed in this study.
Figure 3. Long description
The diagram consists of four vertical panels labeled I C L S w C C, I C L S w o C C, C w C C, and C w o C C. Each panel is divided into three horizontal rows labeled Phase 1, Phase 2, and Phase 3, and three vertical columns labeled Spring, Summer, and Fall.In all four panels, Phase 1 Summer is highlighted in yellow and contains icons for Corn and Soybean.Panel 1, I C L S w C C. Phase 2 has green-shaded Spring and Fall columns containing icons for Cover crop and Animal grazing. Phase 3 is shaded gray across all seasons and contains icons for Animal grazing and Alfalfa.Panel 2, I C L S w o C C. Phase 2 has green-shaded Spring and Fall columns containing only the Animal grazing icon. Phase 3 is shaded gray across all seasons and contains icons for Animal grazing and Alfalfa.Panel 3, C w C C. Phase 2 has green-shaded Spring and Fall columns containing only the Cover crop icon. Phase 3 is empty.Panel 4, C w o C C. Phase 2 has green-shaded Spring and Fall columns but contains no icons. Phase 3 is empty.A legend at the bottom identifies the icons. An ear of corn represents Corn. A pod represents Soybean. A leafy sprout represents Alfalfa. A stalk of grain represents Cover crop. A cow silhouette represents Animal grazing.
Panel 1, I C L S w C C. Phase 1 shows Corn and Soybean in Summer. Phase 2 shows Cover crop and Animal grazing in Spring and Fall. Phase 3 shows Animal grazing and Alfalfa across all three seasons.
Panel 2, I C L S w o C C. Phase 1 shows Corn and Soybean in Summer. Phase 2 shows Animal grazing in Spring and Fall, but no Cover crop. Phase 3 shows Animal grazing and Alfalfa across all three seasons.
Panel 3, C w C C. Phase 1 shows Corn and Soybean in Summer. Phase 2 shows Cover crop in Spring and Fall, but no Animal grazing. Phase 3 is empty.
Panel 4, C w o C C. Phase 1 shows Corn and Soybean in Summer. Phase 2 and Phase 3 are empty, though Phase 2 has green shading in Spring and Fall columns without icons.
The predominant cropping system in Iowa involves a two-phase crop rotation where the land is either in (1) corn or (2) soybean production. In our model, this practice is reflected in the cropping-only systems (CwoCC and CwCC). These systems assume that all farmland in Iowa is allocated to either corn production or soybean production in a given year, and the model assigns a land-use category (shown in Fig. 3 as a ‘phase’) to each acre. Our model allocated 13,289,506 acres of corn and 9,822,678 acres of soybean, reflecting the long-term 57.5:42.5 percent land cover ratio observed in Iowa during 1992–2017 (NASS, USDA, 2023). Synthetic phosphorus (P) fertilizer is applied in both phases, whereas synthetic nitrogen (N) fertilizer is applied only in the corn phase (see Table A1 in Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025).
Contrastingly, the ICLS systems (ICLSwoCC and ICLSwCC) reflect a three-phase crop rotation where the land is assigned one of three land-use categories: (1) corn, (2) soybean, or (3) alfalfa (i.e., forage) production. Land allocation in the ICLS systems follows the average land-use patterns in Iowa from 1992 to 2017 (NASS, USDA, 2023). Cereal rye, Iowa’s dominant cover crop, is used in the ICLSwCC system (Fig. 3). It is interseeded into corn and soybean fields prior to harvest via either high-clearance tractors or drones to maximize fall growth. In the ICLSwCC system, the cover crop provides grazing opportunities during both fall and spring, while animals are managed in the land assigned to alfalfa production for the majority of the year. Synthetic N and P fertilizers are applied in both phases to support grazing (see Table A1 in Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025). If our model assumed no forage land and relied entirely on feedlot confinement, phases 1–2 would be sufficient for the ICLS. However, our model explicitly incorporates forage/pastureland, making phase 3 essential for accurately simulating ICLS in Iowa.
Although roughly one-quarter of Iowa’s row‑crop acreage is managed with no‑till (CTIC, 2023), we adopt no‑till uniformly across the four production systems for parsimony and comparability. Our prior SD analyses indicate that the choice of production system (cash-crop versus integrated) exerts a greater influence on the three sustainability drivers than tillage intensity (Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025). Among the indicators, ER is most sensitive to adopting tillage relative to no‑till, while effects on EQ and SQ are comparatively small (Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025).
The SD model with five key components
The SD model framework, in Fig. 2, is implemented with the Vensim® DSS (decision support system) platform, release 10.3 (Ventana Systems Inc., 2025), a widely recognized and user-friendly SD software package used for industrial and environmental applications (Aboah et al., Reference Aboah, Wilson, Bicknell and Setsoafia2024). Vensim DSS is a visual platform that supports object-oriented features in C++ to link the different interactive components of the system and provides a quantitative description of stock-flow functions across different agroecosystem components. DSS here links the effects of management decisions to production and Integrative Sustainability for integrated and cropping-only systems.
The Vensim platform facilitates the use of several mathematical, statistical, and visual tools for evaluating the model’s ability to reproduce the agroecosystem’s interactions (Turner et al., Reference Turner, Menendez, Gates, Tedeschi and Atzori2016). In this study, we use these tools to incorporate weather factors into the crop and livestock production, EQ, economic returns, and SQ models, enabling a more comprehensive representation of crop-livestock interconnections under future weather conditions. Following the integration of weather variables (details provided in Section ‘Long-term weather scenarios’), dimensional consistency for all equations within each component is verified through Vensim’s syntax checks.
The SD model is applied at the Iowa State scale unit, with outputs compared to data aggregated across all 99 counties. Although county‑level heterogeneity and local weather could warrant finer spatial simulations, we treat Iowa as a single unit to prioritize production‑system effects and reduce computational burden. In the analysis, we consider farm-scale operation to be the dominant operational structure of ICLS in Iowa, and thus cow-calf integration occurs on the same land base as cash crops—via grazing crop residues, pastures, or cereal rye cover crops throughout the whole state. The enhanced model for weather underwent a three-step evaluation process using data for the State of Iowa, following the methodological steps described in our previous work (Papanicolaou et al., Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025). These steps are briefly described below.
In the first step, the model is calibrated for corn and soybean yields using data from 2002 to 2012. The second step involved validation, which is conducted for four agricultural production systems—cropping alone and ICLS, each with and without CC—during the 2013–2022 period. Finally, in the third step, sensitivity testing is performed on the most complex production system (ICLSwCC) over the 2023–2122 period. Sensitivity testing includes changes in available grazing acreage and the number of grazing days. Overall, the enhanced weather SD model demonstrates robustness and effectively captures the interconnections between crops and livestocks, as well as the long‑horizon responses of each of the five components under future temperature trajectories. Further details on the general methodology for SD model calibration, validation, and sensitivity analysis, can be found in Papanicolaou et al. (Reference Papanicolaou, Basnet, O’Brien, Wacha, Malone and Archer2025), where the key input variables and visualizations of the stock-flow functions for each of the five submodels—Cash‑Crop Production, Livestock Production, Economic, EQ, and Social Quality—along with the corresponding data sources are provided (see Figs. A1–A4 and Table A1). Forage production for the 100-years simulation period is estimated with a random normal distribution based on the long-term (1992–2017) mean, and standard deviation of alfalfa yields in Iowa (NASS, USDA, 2023). The current number of beef cows in Iowa, which is 925,000 (Iowa Beef, 2024), is considered the initial herd size in the SD model at the onset of the model simulation period (starting in 2023).
As noted earlier, the five components of the SD model have been enhanced to account for the effects of projected temperature increases on EQ, ER, and SQ and are described below.
Cash-crop production
The cash‑crop component focuses on the two dominant commodities in the U.S. Midwest—corn and soybeans. Its primary outputs are the crop yield ([ActualYield]) and the grazeable residue biomass ([ResGraz]). [ActualYield] is shaped by management practices, soil quality, and prevailing weather conditions, with baseline estimates drawn from long‑term regional trends (NASS, USDA, 2023). Temperature adjustments follow established response functions: corn yields decline with warming (Lobell and Field, Reference Lobell and Field2007), while soybean yields are considered largely temperature‑insensitive (Hatfield et al., Reference Hatfield, Boote, Kimball, Ziska, Izaurralde, Ort, Thomson and Wolfe2011). Irrigation may play an important role in crop yields (Lambert et al., Reference Lambert, English, Clark, Lambert, Menard, Hellwinckel, Smith and Papanicolaou2021), especially when considering the change in temperature; however, it is not considered here. Such consideration could be important if, in the model, all 99 counties were treated as separate units.
Livestock production
The livestock production component assumes cow-calf pairs and models both herd size and diet dynamics. [HerdSize] is determined by the annual numbers of [weaners] and [culls]. [Culls] are determined based on the upper and lower bounds of [HerdSize], which are governed by the required [FeedReq] and the available [AvailFeed]. The livestock diet includes grazing on corn residue (stover), cover crops in corn and soybean fields, forage crops such as alfalfa, and supplemental feed like hay stored for later use. Heat stress effects on feed intake are incorporated through [FeedReq] and [minFeedUse], while mitigation via watering and shading is reflected in the economic model under [AnProdCost], using fixed‑cost estimates from Iowa State University’s Ag Decision Maker. The positive effects of temperature increase on [AvailFeed] are accounted for using above-ground cereal rye cover-crop biomass and alfalfa dry-matter yield projections with temperature increase for RCP 4.5 and RCP 8.5 (Malone et al., Reference Malone, Garbrecht, Busteed, Hatfield, Todey, Gerlitz, Fang, Sima, Radke, Ma, Qi, Wu, Jaynes and Kaspar2020; Hay and Forage Grower, 2022). Forage, corn biomass, and cover-crop biomass are used to estimate [OrgMatFact], which is the total SOC in the environmental-quality model.
Environmental quality
The environmental-quality component is defined by four key drivers that capture soil–water–weather interactions (Carvajal et al., Reference Carvajal, Quiroz, Alaniz, Vergara, Valenzuela-Aguayo and Hidalgo-Corrotea2025): the Soil Conditioning Index ([SCI]), soil organic carbon ([SOC]) stocks (Papanicolaou et al., Reference Papanicolaou, Wilson, Abaci, Elhakeem and Skopec2009; Lewandowski and Cates, Reference Lewandowski and Cates2022; Peng et al., Reference Peng, Rieke, Chahal, Norris, Janovicek, Mitchell, Roozeboom, Hayden, Strock, Machado, Sykes, Deen, Tavarez, Gamble, Scow, Brainard, Millar, Johnson, Schindelbeck, Kurtz, van Es, Kumar and Eerd2023; Lou et al., Reference Lou, Mo, Shao, Liu, Chong, Yu, Zheng and Qiu2025; Nakajima et al., Reference Nakajima, Mizumoto, Akiyama, Gavilan and Lal2025), synthetic-fertilizer application rates (N and P), and manure management. The SCI framework (NRCS , 2003) evaluates soil dynamics by considering organic-matter availability, tillage‑induced disturbance, and erosion (Elhakeem and Papanicolaou, Reference Elhakeem and Papanicolaou2009; Franzluebbers, Causarano and Norfleet, Reference Franzluebbers, Causarano and Norfleet2011; Papanicolaou et al., Reference Papanicolaou, Elhakeem, Wilson, Burras, West and Lin2015a, Reference Papanicolaou, Wacha, Abban, Wilson, Hatfield, Stanier and Filley2015b). Soil organic matter is assumed to contain 58% carbon (Pribyl, Reference Pribyl2010). Biomass from cover crops is combined with cash‑crop residue to compute the cropland organic-matter factor ([OrgMatFact]). Forage biomass contributes an additional factor ([OrgMatFactFor]), which is added to [OrgMatFact] to estimate total SOC. The variables [Nindex] and [Pindex] represent percentage reductions in synthetic-fertilizer use compared with cropping-only systems without cover crops, signaling improvements in soil quality and/or manure utilization; higher values indicate better environmental performance. [ManureMgmt] equals 1 when no surplus manure remains in [ManureStock] after crop application and declines as excess stock accumulates.
Economics
The economics component evaluates the wealth lost or gained over time within a production system, based on expenses and income from both crops and livestock. Crop production expenses include seed and chemical costs [SeedChemCost], land and labor costs [LandLaborCost], machinery expenses [TillageCost], and fertilizer costs for nitrogen and phosphorus [CostFert]. Grazing input costs associated with fencing and water treatment for livestock management are incorporated through [AnProdCost] and are estimated using the Iowa State University Ag Decision Maker, Livestock Enterprise Budgets for Iowa, by considering in this study barbed-wire fencing and assuming 10 gallons of water consumption per head per day that is supplied through a pumping system with aeration treatment and underground-pipe system. Default initial costs are considered to be $1.50/ft for pipeline and installation length for 1.5 miles and $3,500 for a solar-powered submersible pump. Initial costs for the trough and storage tank are $5,000 and $2,100, respectively. Maintenance costs are 1% of the total initial cost. However, the above management costs can vary based on the type of fencing and watering system used, as well as the codes set by the local department of agriculture and land stewardship (Wilson and Clark, Reference Wilson and Clark2002). Additionally, the production costs for CC and forage for livestock are accounted for within this component. Sources of income encompass profits from the sale of crops (cash crops and CC) [CropsToIncome] and the sale of cull animals [LivestockToIncome]. The [NetIncome] is modeled as the net profit per acre across the entire farmland. In the net return calculations, crop and livestock prices are modeled as random variables drawn from a historical distribution in Iowa. As noted earlier, irrigation costs are not considered in the economic model; thus, the historical crop yield for non-irrigated land is used to parameterize the yield in the cash‑crop production model. The additional weight gain from using pumped and aerated water treatment is accounted for as 10 lbs/head/year, reflected in [TotalProtein], and the additional gain in income per head over the spring/summer and summer/fall grazing periods, reflected in [NetIncome], is considered to be double for farms with water treatment versus untreated water from ponds/dugouts and found at approximately $73/head. For a typical average cow-calf operation (~50 head), the initial costs for the water infrastructure described earlier will be offset in less than 3 years.
Social quality
The SQ component focuses on two easily quantifiable drivers: protein production and available leisure time for the producer (Ding, Bennett and Secchi, Reference Ding, Bennett and Secchi2015). Protein production is designed to address societal concerns about ensuring both the quantity and quality of food (Ding, Bennett and Secchi, Reference Ding, Bennett and Secchi2015). [TotalProtein] represents the net protein produced (i.e., from cash crops and livestock) per acre of farmland. [TotalLeisureTime] reflects the free time available to the producer and is computed as [TotHrs] minus the total time required for production activities, including cash crops, cover crops, forage, and livestock, per acre of farmland. Due to the low annual maintenance of the water-pumping and aeration treatment that is considered here, it is assumed that temperature increases will have minimal effect on [TotalLeisureTime] year-around. Although increased protein production is often associated with greater emissions, long‑term evidence demonstrates that small, family‑owned enterprises, ICLS with feedlots, where cover crop is used as a supplement feedstuff, are managed to have low to moderate grazing intensities (de Souza Filho et al. Reference de Souza Filho, Nunes, Barro, Kunrath, de Almeida, Genro, Bayer and de Faccio Carvalho2019). Improved grazing management represents a considerable mitigation opportunity for flux emission intensities in ICLS (de Souza Filho et al., Reference de Souza Filho, Nunes, Barro, Kunrath, de Almeida, Genro, Bayer and de Faccio Carvalho2019).
One could therefore posit that low to moderate grazing intensities reduce negative impacts and feedback to the environment compared to large operations, by enhancing soil-carbon sequestration and lowering emissions, especially, if rotational stocking occurs with managed grazing intensity (Moojen et al., Reference Moojen, Ryschawy, Wulfhorst, Archer, Carvalho and Hendrickson2024).
System-wide evaluation and long-term sustainability assessment
Long-term sustainability assessment under the three weather scenarios involves a three-step methodology (Kremen and Miles, Reference Kremen and Miles2012). First, eight core sustainability drivers are individually modeled within their respective components: [SCI], [SOC], [ManureMgmt], [Nindex], and [Pindex] in the EQ component; [NetIncome] in the economics component; and [TotalProtein] and [TotalLeisureTime] in the SQ component. Each driver is scaled such that higher values signify improved quality.
Next, the individual drivers within these three components are averaged using equations (1), (2), and (3) to derive the combined sustainability indicators for the corresponding sectors: Environmental Quality [EQ], Economic Return [ER], and Social Quality [SQ].
Three sustainability indicators ([EQ], [ER], and [SQ]) serve as absolute performance measures for each individual sector, with their cumulative value traditionally used to gauge the overall performance of the production system (Conway, Reference Conway1985). However, solely relying on cumulative absolute values may fall short for a robust sustainability assessment (Smith et al., Reference Smith, Case, Smith, Harwell and Summers2013). Instead, a comprehensive evaluation should include a comparison of performance across multiple sectors in relative terms before estimating integrated performance (Liebig, Varvel and Doran, Reference Liebig, Varvel and Doran2001).
The second step in the methodology involves an integrated sustainability (IS) assessment based on ranking the absolute values for each of the three sustainability sectors ([EQ], [ER], [SQ]). This method generates a normalized rank-index to compare production systems for each sector in relative terms and assigns corresponding indices: EnSI, EcSI, and SSI (Pope, Annandale and Morrison-Saunders, Reference Pope, Annandale and Morrison-Saunders2004). Production systems ranked first, second, third, and fourth receive index values of 1, 0.75, 0.5, and 0.25, respectively. The IS performance is then calculated as follows:
Herein, we define a production system as long-term sustainable if it obtains the highest possible score of IS for achieving sustainable intensification—considering the relative contributions of all three sectors—while simultaneously maintaining the highest absolute environmental-quality value (equation 1) among all production systems (Xue and Bakshi, Reference Xue and Bakshi2022).
Long-term weather scenarios
Temperature changes are modeled using future long-term weather projections from the IPCC (2014), with RCP 4.5 representing an intermediate stabilization pathway (moderate weather change) and RCP 8.5 representing the worst-case scenario (extreme weather change) (Malone et al., Reference Malone, Garbrecht, Busteed, Hatfield, Todey, Gerlitz, Fang, Sima, Radke, Ma, Qi, Wu, Jaynes and Kaspar2020; Peterson et al., Reference Peterson, Bell, Carvalho and Gaudin2020). Instead, the baseline condition considers that 2023 temperature conditions are held constant through 2122. Figure B1 illustrates the increased temperature profiles for both RCPs relative to the baseline year 2023. By 2122, total temperature increases are projected to reach 1.98 °C for RCP 4.5 and 5.14 °C for RCP 8.5.
Results and discussion
Production systems performance under baseline conditions
The performance of the four production systems under unchanged weather conditions is presented here as a reference (Fig. 4) prior to proceeding with the evaluation of their performance under the two future weather scenarios. The synthesis of the results shown in Fig. 4 is assuming no-till operations for all production systems, as noted earlier, and compares these systems by considering all 8 sustainability drivers, the 3 combined sustainability indicators (EQ, ER, and SQ), and the Integrated Sustainability (IS). This analysis underlines two key points for the baseline weather scenario: first, CC and ICLS work synergistically to improve EQ and ER, whereas CC in cropping-only systems slightly reduces performance. Second, the different production systems involve meaningful trade-offs, particularly in ER and SQ. Although the SQ value is lower, the greater EQ and ER values result in the ICLSwCC system achieving the highest IS score. A more in-depth assessment is provided below.
Individual, combined, and Integrated Sustainability Indicators evaluation among the four agricultural production systems under baseline conditions (no change in weather) and for no-till management.
Figure 4. Long description
The chart uses a color-coded legend for four scenarios: C w o C C (blue), C w C C (orange), I C L S w o C C (gray), and I C L S w C C (yellow).Panel 1: Individual Sustainability Drivers. The y-axis ranges from -0.2 to 3. The x-axis lists eight drivers grouped into three categories:- Environmental: S C I, S O C, ManureMgmt, Nindex, and Pindex. Values are generally low, mostly under 1.2.- Economic: NetIncome. This is the highest driver, with I C L S w C C reaching nearly 3.0.- Social: Protein and LeisureTime. Values range between 0.2 and 0.8.Panel 2: Combined Sustainability Indicators. The y-axis is unlabeled but shows values from 0 to 1. It contains three groups:- Environmental Quality (E Q): Values are 0.5, 0.58, 0.5, and 0.69.- Economic Return (E R): Values are 0.61, 0.56, 0.88, and 1.- Social Quality (S Q): Values are 0.68, 0.67, 0.54, and 0.4.Panel 3: Integrated Sustainability (I S). This single group shows the final aggregated scores for the four scenarios:- C w o C C: 0.63- C w C C: 0.58- I C L S w o C C: 0.54- I C L S w C C: 0.75
Panel 1: Individual Sustainability Drivers. The y-axis ranges from -0.2 to 3.
* Environmental drivers include S C I, S O C, Manure Mgmt, N index, and P index. Values are generally low (below 1.2), with Manure Mgmt showing equal values of 1.0 across all systems.
* Economic driver: Net Income shows the highest values in the chart, with I C L S w C C peaking at 3.0.
* Social drivers: Protein and Leisure Time show values between 0.2 and 0.8.
Panel 2: Combined Sustainability Indicators.
* Environmental Quality (E Q): Values range from 0.5 to 0.69, with I C L S w C C highest.
* Economic Return (E R): Values range from 0.56 to 1.0, with I C L S w C C highest.
* Social Quality (S Q): Values range from 0.4 to 0.68, with C w o C C highest and I C L S w C C lowest.
Panel 3: Integrated Sustainability (I S).
* This single grouping shows the final aggregated scores: C w o C C at 0.63, C w C C at 0.58, I C L S w o C C at 0.54, and I C L S w C C at 0.75.
Environmental quality
The ICLSwCC system achieved the highest EQ value because of high values across the four EQ drivers: SCI, SOC, N-index, and P-index. These high SCI and SOC values are supported by the inclusion of CC, while N and P indices reflect the inclusion of manure. Incorporating CC into an agroecosystem not only protects bare soil by reducing erosion rates (Burkart et al., Reference Burkart, James, Liebman and Herndl2005), but also enhances SOC levels by improving the overall carbon stock (Pandey et al., Reference Pandey, Liu, Racelis and Soti2025).
Interestingly, soil quality decreases slightly in the ICLSwoCC system due to lower SCI and SOC values compared with the CwoCC system. This reduction arises from reduced corn biomass, as part of it is consumed by grazing animals (Prado et al., Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025). These lower SCI and SOC values offset the positive contributions of the N and P indices, resulting in the same EQ values for ICLSwoCC and CwoCC. The difference between ICLSwoCC and ICLSwCC clearly shows an important synergy, where the capacity of the ICLSwCC system to maintain high EQ is attributed to its increased herd size from cereal-rye biomass production (see Table 1). The larger herd results in greater quantities and enhanced value of manure, thereby further reducing dependency on synthetic fertilizers—improving the N and P indices—but also serving as a vital source of organic matter, thereby preserving higher SCI and SOC values. Conversely, the smaller herd size in the ICLSwoCC system produces insufficient manure to fully counteract the negative effects on these sustainability drivers. Overall, our results are in line with studies showing that farms combining crops, livestock, and cover crops have lower greenhouse gas emissions (Pereyra-Goday, Jebari and Takahashi, Reference Pereyra-Goday, Jebari and Takahashi2024). They also do a better job of storing carbon in the ground. Research from the USDA Agricultural Research Service highlights that integrating these methods restores soil organic matter and protects water quality. A five-year study on a large-scale research farm near Pana, Illinois, U.S., found that incorporating winter cover crops and cool-season pastures within ICLS improved soil quality and enhanced the dynamics of SOM (Maughan et al., Reference Maughan, Flores, Anghinoni, Bollero, Fernández and Tracy2009). The use of winter cover crops resulted in an average annual increase in maize yield of 0.7 Mg/ha (a positive impact on ER) compared with continuous maize monoculture.
Livestock dynamics under baseline and future weather scenarios: Herd size determined by available grazing resources
Table 1. Long description
The table is divided into two main production systems: I C L S w o C C and I C L S w C C. Each system is evaluated under Baseline, R C P 4.5, and R C P 8.5 weather scenarios.
For I C L S w o C C:
* Baseline: Corn residue 29.47, Cover crop in corn 0, Cover crop in soybean 0, Forage 1.93, Total feed 31.40 billion lbs., Herd size 6.10 million, Supply ratio 2.42 ac/head.
* R C P 4.5: Corn residue 27.01, Cover crop in corn 0, Cover crop in soybean 0, Forage 2.11, Total feed 29.12 billion lbs., Herd size 5.79 million, Supply ratio 2.52 ac/head.
* R C P 8.5: Corn residue 23.45, Cover crop in corn 0, Cover crop in soybean 0, Forage 2.25, Total feed 25.70 billion lbs., Herd size 5.34 million, Supply ratio 2.75 ac/head.
For I C L S w C C:
* Baseline: Corn residue 29.67, Cover crop in corn 12.15, Cover crop in soybean 9.41, Forage 1.93, Total feed 53.16 billion lbs., Herd size 10.80 million, Supply ratio 2.50 ac/head.
* R C P 4.5: Corn residue 27.22, Cover crop in corn 15.51, Cover crop in soybean 11.99, Forage 2.11, Total feed 56.83 billion lbs., Herd size 11.34 million, Supply ratio 2.40 ac/head.
* R C P 8.5: Corn residue 23.68, Cover crop in corn 20.38, Cover crop in soybean 15.72, Forage 2.25, Total feed 62.03 billion lbs., Herd size 12.45 million, Supply ratio 2.24 ac/head.
Where 1 ac =4046.85 m2.
Economic return
Both integrated production systems generate significantly higher net income compared with cropping-only production systems, with ICLSwCC achieving the highest ER value (Fig. 4). Notably, the improved economic performance primarily stems from livestock production, as the net income from the corn and soybean production phases remains similar across systems. For ICLSwoCC, the increased net income results from expanding the number of beef cows to match the land’s capacity based on acreage and available feed. The current number of beef cows in Iowa, 925,000 (Iowa Beef, 2024), serves as the initial herd size in the SD model but increases over time. Ultimately, the ICLSwoCC system accommodates an average of 6.1 million beef cows, representing a 6.59-fold increase over Iowa’s current herd size. This growth is even greater when factoring in the additional forage opportunities provided by CC in the ICLSwCC system during spring and fall phases (Prado et al., Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025). These findings align with Gonçalves et al. (Reference Gonçalves, Silva and de Camargo2025), who highlight the strategic benefits of increased plant diversity in ICLS, coupled with animal grazing, for achieving substantial productivity gains. Similarly, the National Cover Crop Survey Report for 2022–2023 indicated that 76% of producers grazing CC reported a net profit increase (SARE, 2023). A report by Practical Farmers of Iowa (2021) further supports this, emphasizing the economic advantages of grazing CC in Iowa.
Social quality
In contrast to ER and EQ, ICLSwCC ranked the lowest among the four production systems in SQ, while CwoCC ranked the highest (Fig. 4). Although the total protein produced is comparable across all four systems, the total leisure time is significantly greater for CwoCC and CwCC. The higher leisure time in the cropping-only systems can be attributed to their status as comparatively common practices in the region, having been optimized over time in terms of equipment availability and cultural practices. The inclusion of CC decreases leisure time slightly in CwCC compared with CwoCC, but this reduction is much lower than the addition of livestock to producer operations. Typical row-cropping systems have well-defined windows of intense activity, such as planting and harvest. But livestock requires intensive management year-round, which greatly increases the time invested in the integrated systems. This requires more producer time (and less leisure) or the use of additional hired labor.
Production systems performance under long-term weather variability scenarios, RCP 4.5 and RCP 8.5
Environmental quality
Transitioning from row-crop farming to integrated farming practices has a positive impact on most key drivers of environmental quality, ultimately enhancing the overall EQ of the agroecosystem under both long-term weather variability scenarios (RCP 4.5 and RCP 8.5). However, the ICLSwoCC system does not show any measurable improvement in EQ level compared with the CwoCC system. This is likely due to a slight decline in soil quality, as both SCI and SOC decrease relative to the CwoCC system, primarily because grazing reduces corn biomass. These findings emphasize the importance of further improving the integrated production system, potentially through the incorporation of cover crops, to sustain EQ levels.
The relative performance of each production system remains consistent throughout the study period (Fig. 5a). Although only extreme weather conditions (RCP 8.5) are presented here, the trends for the four environmental drivers (SCI, SOC, N-index, and P-index) remain similar under moderate conditions (RCP 4.5). Notably, the EQ in CwoCC and ICLSwoCC systems declines steadily as temperature rises, whereas the EQ in the CwCC system shows only a slight reduction, and the ICLSwCC system maintains stable EQ levels. In the ICLSwCC system under changing temperature conditions, the beneficial impact of grazing cover crops on SOC levels can be attributed to increased biomass production. This observation aligns with the findings reported by Delandmeter et al. (Reference Delandmeter, de Faccio Carvalho, Bremm, Santos Cargnelutti, Bindelle and Dumont2024). While these temporal differences do not alter the ranking of the four production systems, the EQ values are more pronounced under RCP 8.5 conditions (Fig. 6).
Dynamic progression of sustainability indicators for the four agricultural production systems from 2023 to 2122 under RCP 8.5: (A) environmental quality, (B) economic quality and (C) social quality.
Figure 5. Long description
The figure consists of three panels, A, B, and C, sharing a common X-axis representing Year from 2020 to 2120. A legend at the bottom identifies four scenarios: C O (blue), C w C C (orange), I C L S (grey), and I C L S w C C (yellow).Panel A: Environmental quality. The Y-axis ranges from 0.2 to 0.8. The yellow line (I C L S w C C) fluctuates at the top between 0.6 and 0.8 with no clear trend. The orange line (C w C C) shows a slight downward trend from 0.6 to 0.4. The blue (C O) and grey (I C L S) lines are nearly identical, showing a steady linear decrease from 0.5 in 2020 to approximately 0.25 by 2120.Panel B: Economic return. The Y-axis ranges from -0.7 to 1.4. All four lines follow a highly volatile, synchronized oscillating pattern. The yellow line consistently reaches the highest peaks (up to 1.4) and lowest troughs. The blue, orange, and grey lines are clustered together, frequently crossing the zero baseline.Panel C: Social quality. The Y-axis ranges from 0.2 to 0.7. The blue (C O) and orange (C w C C) lines remain highest, showing a very gradual decline from 0.7 to 0.6. The grey line (I C L S) maintains a steady level around 0.5. The yellow line (I C L S w C C) drops sharply from 0.6 to 0.4 before 2030 and continues a staggered decline to approximately 0.25 by 2120.
Panel A: Environmental quality. The Y-axis ranges from 0.2 to 0.8. The yellow line (I C L S w C C) fluctuates at the highest level between 0.6 and 0.8. The orange line (C w C C) shows a slight downward trend from 0.6 to 0.4. The blue (C O) and gray (I C L S) lines are nearly identical, showing a steady linear decrease from 0.5 in 2023 to below 0.3 by 2122.
Panel B: Economic return. The Y-axis ranges from -0.7 to 1.4. All four lines exhibit high volatility with frequent peaks and troughs. The yellow line generally reaches the highest peaks, while all systems frequently dip below the zero line, particularly after 2080.
Panel C: Social quality. The Y-axis ranges from 0.2 to 0.7. The blue (C O) and orange (C w C C) lines remain highest, starting near 0.7 and slightly declining to 0.6. The gray line (I C L S) remains stable around 0.5. The yellow line (I C L S w C C) shows a sharp initial drop from 0.6 to 0.4 before 2030, then continues a staggered decline to approximately 0.25 by 2122.
Sustainability analysis of the four agricultural production systems under RCP 4.5 and RCP 8.5.
Figure 6. Long description
The chart is divided into four vertical panels labeled Environmental Quality E Q, Economic Return E R, Social Quality S Q, and Integrated Sustainability I S. Each panel contains four pairs of bars representing scenarios: C w o C C, C w C C, I C L S w o C C, and I C L S w C C. Blue bars represent R C P 4.5 and orange bars represent R C P 8.5.* Environmental Quality E Q: Values range from 0.39 to 0.71. I C L S w C C has the highest scores (0.7 for R C P 4.5 and 0.71 for R C P 8.5).* Economic Return E R: Shows the highest variance. I C L S w C C peaks at 0.85 for R C P 4.5, while C w C C is lowest at 0.13 for R C P 8.5.* Social Quality S Q: Values generally decrease from left to right across scenarios, with C w o C C highest (0.67 and 0.65) and I C L S w C C lowest (0.37 and 0.33).* Integrated Sustainability I S: I C L S w C C shows the highest integrated performance with a score of 0.75 for both R C P scenarios, while other scenarios range between 0.54 and 0.63.
In the Environmental Quality panel, values range from 0.39 to 0.71, with I C L S w C C showing the highest scores.
In the Economic Return panel, R C P 4.5 consistently outperforms R C P 8.5. I C L S w C C has the highest value at 0.85 under R C P 4.5, while C w C C under R C P 8.5 is the lowest at 0.13.
In the Social Quality panel, scores are relatively stable across systems, ranging from 0.33 to 0.67, with a slight downward trend from left to right.
In the Integrated Sustainability panel, I C L S w C C achieves the highest score of 0.75 for both climate scenarios, while other systems range between 0.54 and 0.63.
A legend at the bottom indicates blue for R C P 4.5 and orange for R C P 8.5.
The environment deteriorates more noticeably under RCP 8.5 than under RCP 4.5 for both CwCC and CwoCC, including the ICLSwoCC system. However, incorporating CC into the ICLS system leads to an improved environmental outcome for the ICLSwCC system under extreme weather conditions compared to moderate ones. This highlights the importance of incorporating CC into an integrated production system to preserve EQ under future weather scenarios.
We found that EQ could be improved significantly and consistently by incorporating cover crops into both cropping-only and integrated production systems under both weather scenarios, thereby supporting our hypothesis. These benefits are likely linked to the positive relationship between cover-crop biomass with temperature increases associated with changing weather. The greater biomass accumulation, along with the animal manure, will provide additional organic-matter inputs that will maintain higher SCI and SOC values despite increased soil respiration under RCP 4.5 and RCP 8.5.
Consistent with the findings of Mubvumba, DeLaune and Gentry (Reference Mubvumba, DeLaune and Gentry2024), the inclusion of CC has a substantial positive effect on the EQ of both CwCC and ICLSwCC production systems. This aligns with studies by Gebremedhin et al. (Reference Gebremedhin, Sarr, Coyne, Sistani and Simmons2019), Denardin et al. (Reference Denardin, Moraes, Tavares, Pires, Silva, Pacheco, Wruck, Carneiro and Souza2025), and He et al. (Reference He, Lu, Cowie, Zhao, Liu, Yi, Shi, Zhang, Qiu, Shi, Huete, Siddique, Yu and Li2025), among others, which highlight that soil-degrading management practices are a leading cause of low agricultural productivity and environmental issues in many regions. Soil quality indicators (SCI and SOC) significantly improved in both CwCC and ICLSwCC systems compared to CwoCC and ICLSwoCC systems, respectively. Similar to Denardin et al. (Reference Denardin, Moraes, Tavares, Pires, Silva, Pacheco, Wruck, Carneiro and Souza2025), He et al. (Reference He, Lu, Cowie, Zhao, Liu, Yi, Shi, Zhang, Qiu, Shi, Huete, Siddique, Yu and Li2025), and Prado et al. (Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025), these increases are attributed to CC’s ability to increase soil organic matter through the incorporation of above-ground and below-ground biomass. Additionally, CC reduces erosion by maintaining soil cover for extended periods, compared to systems without CC (Burkart et al., Reference Burkart, James, Liebman and Herndl2005).
While using CC contributes to increased EQ, this benefit is slightly offset by the fertilizer applied to CC. The rise in synthetic-fertilizer usage is reflected in a minor decline in the N-index of the CwCC system (He et al., Reference He, Lu, Cowie, Zhao, Liu, Yi, Shi, Zhang, Qiu, Shi, Huete, Siddique, Yu and Li2025; Pandey et al., Reference Pandey, Liu, Racelis and Soti2025). Pairing cover crops with adaptive nutrient management, including reduced fall N, in‑season diagnostics, and variable‑rate fertilization, can further improve nitrogen-use efficiency. These practices complement soil‑health gains from cover cropping, such as improved structure, organic matter, and microbial activity, which enhance N retention and cycling (Gebremedhin et al., Reference Gebremedhin, Sarr, Coyne, Sistani and Simmons2019; Ali et al., Reference Ali, Jabeen, Farruhbek, Chachar, Laghari, Chachar, Ahmed, Ahmed and Yang2025). Notably, optimizing nitrogen-use efficiency may not only help mitigate nitrogen loss in the environment but also support the simultaneous maximization of crop yields (Ali et al., Reference Ali, Jabeen, Farruhbek, Chachar, Laghari, Chachar, Ahmed, Ahmed and Yang2025). Continued field‑scale validation of N losses, crop uptake, and system performance is needed to translate modeled outcomes into practical recommendations.
In the ICLSwCC system, the decrease in N-index is much less pronounced due to the synergy of livestock with cover crops, including grazing manure as a source of fertilizer (Prado et al., Reference Prado, Costa, Silva, Severiano, Vilela, Costa, Costa, Habermann, Marques and Martinez2025). The N-index and P-index values are further improved compared to the ICLSwoCC system because of the increment in herd size (Table 1) so that the ICLSwCC system is able to counterbalance the reduction in N-index due to the additional fertilizer need for CC throughout the study period.
Herd size increases are determined by the grazing area supply ratio (or stocking rate), which reflects the number of acres available per cow within the system. The supply ratio exceeded 2.2 acres per head for both integrated systems in this study. Appropriate stocking rates, however, are site-specific and depend on factors such as grazing land quality, animal weight, water availability, and grazing intensity (Scaglia et al., Reference Scaglia, Swecker, Fontenot, Fiske, Fike, Abaye, Clapham and Hall2008). The values in our study align with those reported by ISU Extension (2023), which adopted 2.5 acres per cow-calf, and Scaglia et al. (Reference Scaglia, Swecker, Fontenot, Fiske, Fike, Abaye, Clapham and Hall2008), who studied forage systems for cow-calf production with stocking rates of 2.25 and 1.75 acres per cow (or about 10,117 m2 per cow).
Economic return
The ICLSwCC performed best economically compared to the other three production systems under both moderate and extreme weather (Fig. 6). Time series of wealth is presented in Fig. 7. Under RCP 4.5, wealth keeps increasing throughout the simulation period for all four production systems. Under RCP 8.5, however, wealth remains about constant after ~2084 for both cropping-only production systems (CwoCC and CwCC), while the stock wealth keeps increasing until the final year 2122 for both integrated production systems (ICLSwoCC and ICLSwCC), where the increase rate is even higher for the ICLSwCC system than the ICLSwoCC system.
Wealth trends over time for the four agricultural production systems from 2023 to 2122 under RCP 4.5 and RCP 8.5.
Figure 7. Long description
The x-axis represents time in years from 2020 to 2120. The y-axis represents Wealth in U S dollars in billion, ranging from 0 to 600. Eight distinct lines track different economic scenarios.All lines begin near zero in 2020 and show a general upward trend until approximately 2045. After 2045, the trajectories diverge significantly.High-growth scenarios (dashed lines) include:* I C L S w C C underscore R C P 4.5 (yellow dashed) reaches the highest peak at approximately 600 billion by 2120.* I C L S w o C C underscore R C P 4.5 (grey dashed) follows, reaching approximately 500 billion.* C w o C C underscore R C P 4.5 (blue dashed) and C w C C underscore R C P 4.5 (orange dashed) reach between 250 and 320 billion.Moderate to low-growth scenarios (solid lines) include:* I C L S w C C underscore R C P 8.5 (yellow solid) reaches approximately 460 billion.* I C L S w o C C underscore R C P 8.5 (grey solid) reaches approximately 340 billion.* C w o C C underscore R C P 8.5 (blue solid) plateaus around 150 billion after 2080.* C w C C underscore R C P 8.5 (orange solid) shows the lowest growth, plateauing and slightly declining to under 100 billion by 2120.
* I C L S w C C under R C P 4.5 (yellow dashed) reaching approximately 600 billion.
* I C L S w o C C under R C P 4.5 (grey dashed) reaching approximately 500 billion.
* I C L S w C C under R C P 8.5 (yellow solid) reaching approximately 450 billion.
* I C L S w o C C under R C P 8.5 (grey solid) reaching approximately 330 billion.
* C w o C C under R C P 4.5 (blue dashed) reaching approximately 310 billion.
* C w C C under R C P 4.5 (orange dashed) reaching approximately 260 billion.
* C w o C C under R C P 8.5 (blue solid) reaching approximately 150 billion.
* C w C C under R C P 8.5 (orange solid) reaching approximately 90 billion.
Both RCP 4.5 and RCP 8.5 scenarios negatively affect ER of all four production systems over the 100-year simulation. The time series of ER is shown in Fig. 5b for extreme weather, as an example. While the decrease in ER does not demonstrate a linear or predictable response, the long-term effects are inevitably negative for each production system. This trend continues over time, and the ER drops below the threshold for a positive economic return, such that only the ICLSwCC system maintains profitability by the end of the simulation period. Notably, the relationship among the four production systems remains consistent throughout the simulation period. In general, the ER is lowest for the CwCC system and highest for the ICLSwCC system, which aligns with the baseline scenario (Fig. 4).
Among the three key sustainability indicators (ER, EQ, and SQ) presented in Fig. 5, the most complex behavior is shown by the ER. Like the baseline scenario, the ER is strongly related to production dynamics of the different systems. Moreover, the ER is highest in the case of the ICLSwCC system due to the beneficial effects of cereal-rye biomass. The annual gains in weight per head are also accounted for in the ER for both integrated systems. These gains are attained due to the enhanced water-treatment considered here. The production dynamics of cash crops (corn yield and soybean yield), CC (above-ground cereal-rye biomass production), forage (alfalfa production), and livestock (beef cattle) are displayed in Fig. 8 for ICLSwCC under extreme weather. ICLSwCC is selected here as the focus for further analysis because it is the production system that has recently received attention in Iowa. Similarly, the RCP 8.5 scenario is chosen here as it is considered to have a more dynamic behavior and over a longer trajectory compared to the other two scenarios (baseline and RCP 4.5). With the exception of soybeans, all mean yield trends appear to be non-stationary. Throughout the simulation period, soybean yield remains relatively stable without any significant loss or gain, even under extreme weather conditions (Hatfield et al., Reference Hatfield, Boote, Kimball, Ziska, Izaurralde, Ort, Thomson and Wolfe2011). In contrast, corn yields decline sharply over time due to the negative effects of rising temperatures (Lobell and Field, Reference Lobell and Field2007; Hatfield et al., Reference Hatfield, Boote, Kimball, Ziska, Izaurralde, Ort, Thomson and Wolfe2011). While production should show a reduction in soybean yields, as the temperature effect on the ecophysiological response of soybean is included in our model (Alsajri et al., Reference Alsajri, Chathurika Wijewardana, Nacer Bellaloui, Golden, Gao and Reddy2020), we believe the no significant yield loss shown in our results is attributed to the fact that the annual time step considered in our analysis camouflages the adverse effects of temperature change on soybean growth (contrary to corn, which is much more sensitive to temperature change). Given the central role of cash crops in net income, this decline in corn yield emerges as a major driver of reduced economic returns. Therefore, developing genetic strategies, including traditional breeding and genetic engineering, is crucial for enhancing crop yields under weather stress, such as extreme temperatures. By leveraging naturally evolved traits and employing transformative bioengineering, researchers can develop crops with improved resilience and productivity in changing weather conditions (Bailey-Serres et al., Reference Bailey-Serres, Parker, Ainsworth, Oldroyd and Schroeder2019).
Time series of cash crops, cover crops, forage, and livestock production for ICLSwCC under RCP 8.5, smoothed using a 10-year moving average. 1 lb/ac ≈ 2.405 kg/ha.
Figure 8. Long description
The horizontal x-axis represents years from 2020 to 2125. The primary vertical y-axis on the left measures Production in lbs forward slash ac, ranging from 2000 to 12000. The secondary vertical y-axis on the right measures Herd-size per acre of farmland, ranging from 0 to 0.8.Five data series are plotted:- Corn yield (yellow line): Starts at approximately 11500 lbs forward slash ac in 2030 and shows a steady linear decrease to about 7000 lbs forward slash ac by 2120.- Soybean yield (green line): Remains nearly constant and flat at approximately 3500 lbs forward slash ac throughout the entire period.- Alfalfa (purple line): Shows a gradual increase from 6500 lbs forward slash ac in 2030, peaking near 8000 around 2075, and then stabilizing.- Rye biomass (red line): Exhibits a strong upward trend, starting at 5000 lbs forward slash ac in 2030 and rising to over 12000 lbs forward slash ac by 2120.- Herd-size (black line): Measured on the right axis, it rises sharply from 0.3 in 2030 to 0.5 by 2040, fluctuates between 0.5 and 0.6 until 2100, and then stabilizes around 0.6.
* Corn yield (yellow line) starts at approximately 11500 pounds per acre in 2032 and shows a steady, linear decrease to about 7000 pounds per acre by 2120.
* Rye biomass (red line) begins at 5000 pounds per acre and exhibits a significant upward trend with minor fluctuations, reaching a peak of over 12000 pounds per acre by 2120.
* Herd-size (black line) corresponds to the right Y-axis. It starts at 0.3 in 2032, rises sharply to 0.5 by 2040, and then fluctuates between 0.5 and 0.65 for the remainder of the century.
* Alfalfa (purple line) starts near 6500 pounds per acre and shows a gradual increase, stabilizing around 8000 pounds per acre after 2070.
* Soybean yield (green line) remains nearly constant and flat at the bottom of the graph, maintaining a value of approximately 3500 pounds per acre throughout the entire time series.
Integrated production systems (ICLSwoCC and ICLSwCC) demonstrate greater resilience to increased temperature by incorporating livestock-income. In the ICLSwoCC system, this resilience is primarily supported by increased forage production over time, whereas in the ICLSwCC system, both forage and cover-crop production contribute to sustained stability. While diversification of income initially mitigates some economic impacts, its benefits diminish over time as alfalfa-yield increases plateau following a temperature rise of approximately 20 °C. In the case of ICLS, herd size declines by 5.1% under RCP 4.5 and 12.5% under RCP 8.5 compared to the baseline (Table 1). This reduction is largely driven by decreased corn yields, leading to a diminished supply of corn residue as feed in warmer conditions. Cheng, McCarl and Fei (Reference Cheng, McCarl and Fei2022) further support this observation, highlighting that possibly a lower feed intake constitutes a common response to high environmental temperatures.
Among the systems studied, ICLSwCC proves the most resilient to weather stress, as cereal-rye biomass continues to increase throughout the study period (Fig. 8). This sustained growth enables the maintenance of a larger herd, generating enough livestock-income to offset declining cash-crop revenue. However, this economic analysis does not factor in potential shifts in crop and livestock prices. Changes in crop production or in herd numbers may influence market dynamics, ultimately affecting overall economic returns.
Social quality
SQ drivers are least sensitive to weather stress, so the indicator values changed very little throughout the 100-year period (Fig. 5c). Like ER and EQ, the relative performance of the production systems did not change under the future long-term weather-variability scenarios (Fig. 6). The very slight decreases in CwoCC, CwCC, and ICLSwoCC systems are likely the result of reduced protein production, primarily due to decreasing corn yields. Leisure time for these three production systems is not affected, as cultural practices are not expected to change due to weather.
The time investment required for livestock production must be considered alongside the time needed for cropping, as the integrated production systems involve both row-cropping and livestock management. The significant decline in SQ level compared to ER and EQ in the ICLSwCC system highlights a clear trade-off associated with the increased management complexity—one not observed to the same extent in the other three production systems. Notably, the total time commitment is higher for integrated production systems (ICLSwoCC and ICLSwCC) compared to cropping-only production systems (CwoCC, CwCC). While the workload associated with livestock management might reduce leisure time in these integrated systems, it may also reduce the need for producers to seasonally work off-farm due to the even distribution of labor demands throughout the production year. While increased labor is modeled as a negative social impact associated with reduced leisure time, individual preferences for off-farm employment and providing labor opportunities within the community likely vary. This highlights the motivation for emerging efforts to develop social well-being indicators for use in agricultural performance assessment (Donovan et al., Reference Donovan, Spiegal, Kaplan, Archer, Bean, Beebout, Bestelmeyer, Clark, DeLong, Fortuna, Friedrichsen, Hoover, Huggins, Kleinman, McIntosh, Renschler, Ritten, Smith, Webb and Wulfhorst2025).
The sharper reduction in SQ level for the ICLSwCC system is primarily attributed to the ongoing decrease in leisure time caused by the expansion of herd size. The additional protein yield from livestock is insufficient to compensate for this loss of leisure time. However, the adoption of advanced technologies—such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)—could potentially alleviate these challenges. These innovations may enhance both livestock management and the overall efficiency of farm operations within the agroecosystem under the ICLSwCC system (Potluri et al., Reference Potluri, Satpathy, Basa and Zuorro2024; Khan and Awais, Reference Khan, Awais, Archangel, Krishnan and Dejey2025; Nirmalkumar, Krishna and Archangel, Reference Nirmalkumar, Krishna, Archangel, Archangel, Krishnan and Dejey2025; Sankararao et al., Reference Sankararao, Rajalakshmi, Choudhary, Kholova, Jones, Archangel, Krishnan and Dejey2025).
Integrated sustainability
Building on the baseline scenario, incorporating CC into the integrated system (i.e., ICLSwCC) emerges as the optimal management strategy, delivering superior environmental and economic performance under Iowa’s future weather conditions. The IS scores for the four production systems show minimal variation over time, reflecting consistent relationships across the three sustainability indicators under both long-term weather variability scenarios (Fig. 6). Notably, while the IS score for the CwoCC system decreases under RCP 8.5 compared to RCP 4.5, the score for the ICLSwoCC system improves, ultimately matching the scores of both the CwoCC and CwCC systems under RCP 8.5.
In summary, the ICLSwCC system achieves the highest EQ value and IS score, establishing it as the most sustainable production system. These results further emphasize its resilience to the challenges of weather stress. Our study suggests that one of the main challenges to the widespread adoption of cover crops—namely, the cost of planting and termination—can be mitigated by incorporating cover crops as a feed source within an integrated crop-livestock system. This approach not only offsets treatment costs but also generates additional income through increased forage acreage and herd size.
Conclusion
This study shows that integrated crop–livestock systems (ICLS) that include cover crops offer stronger long‑term environmental and economic resilience than conventional corn–soybean rotations in Iowa. Using a SD modeling framework, we evaluated the hypothesis that mixed farming systems—especially those combining livestock with grazed rye cover crops—would outperform cropping‑only systems under both current and projected warming scenarios. Across all weather conditions, the ICLSwCC system consistently produced the highest EQ and economic returns, demonstrating strong potential to maintain soil health and profitability under future weather stress.
Despite these advantages, the ICLS with cover crops showed clear labor–leisure trade-offs. The system had the lowest SQ scores, driven mainly by increased year‑round labor demands associated with livestock management. These findings highlight important considerations for producers transitioning from row‑crop agriculture to mixed farming.
The analysis also underscores the practical and logistical challenges of scaling ICLS adoption. While some Iowa producers already integrate livestock into row‑crop systems, broader adoption would require substantial changes to infrastructure, management, and labor allocation. SD modeling helps address these complexities by enabling integrated assessment of agronomic, environmental, and socio-economic interactions.
Finally, this study illustrates the value of SD modeling for exploring transformational farming systems that are difficult to evaluate empirically. Mixed farming introduces non-linear dynamics and feedbacks, making SD models particularly useful for examining long‑term system behavior under stressors such as rising temperature.
Recommendations
Based on this study and evidence from the wider literature, we recommend the following strategies for strengthening integrated crop–livestock systems by incorporating cereal-rye cover crops into corn–soybean rotations for livestock grazing and supporting future adoption in Iowa:
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1. Strengthen nitrogen-use efficiency (NUE) in cover crop systems.
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• Select cover crop species or mixtures with strong N‑scavenging or N‑fixing capacity—such as cereal rye or legume–grass combinations—to improve N retention and reduce off‑season losses (Ali et al., Reference Ali, Jabeen, Farruhbek, Chachar, Laghari, Chachar, Ahmed, Ahmed and Yang2025; He et al., Reference He, Lu, Cowie, Zhao, Liu, Yi, Shi, Zhang, Qiu, Shi, Huete, Siddique, Yu and Li2025).
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• Integrate adaptive nutrient management approaches, including reduced fall fertilizer, in‑season diagnostics for side‑dressing, and variable‑rate fertilization (Malone et al., Reference Malone, O’Brien, Herbstritt, Emmett, Karlen, Kaspar, Kohler, Radke, Lence, Wu and Richard2022; Pandey et al., Reference Pandey, Liu, Racelis and Soti2025).
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2. Enhance soil health and environmental quality.
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• Use cover crops to increase soil organic matter, strengthen aggregate stability, and reduce erosion, especially during silage-maize phases.
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• Combine cover crops with managed grazing to stimulate microbial activity, enhance soil-carbon inputs, and improve nutrient cycling (Mubvumba, DeLaune and Gentry, Reference Mubvumba, DeLaune and Gentry2024; Denardin et al., Reference Denardin, Moraes, Tavares, Pires, Silva, Pacheco, Wruck, Carneiro and Souza2025).
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• Avoid over‑grazing to reduce compaction risk and limit grazing to dry or frozen periods when possible.
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3. Improve economic resilience and system viability.
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• Expand opportunities for grazing cover crops to lower feed costs, lengthen the grazing season, and provide fall and spring forage (Practical Farmers of Iowa, 2021; SARE, 2023).
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• Diversify revenue streams in mixed systems to buffer economic losses under warming scenarios (Poffenbarger et al., Reference Poffenbarger, Artz, Dahlke, Edwards, Hanna, Russell, Sellers and Liebman2017; Gonçalves et al., Reference Gonçalves, Silva and de Camargo2025).
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• Reduce risks such as compaction and labor demand by grazing during dry or frozen periods and adopting precision-livestock farming technologies (Liebig et al., Reference Liebig, Tanaka, Kronberg, Scholljegerdes and Karn2011; Nirmalkumar, Krishna and Archangel, Reference Nirmalkumar, Krishna, Archangel, Archangel, Krishnan and Dejey2025).
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4. Address management and social constraints.
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• Promote precision-livestock farming tools—such as automated monitoring, sensors, water-supply systems, and robotics—to reduce labor and improve management efficiency (Rajagopal et al., Reference Rajagopal, Kumar, Choudhury, Archangel, Archangel, Krishnan and Dejey2025).
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• Develop policy incentives that reward environmental stewardship and support transitions to integrated systems (Moojen et al., Reference Moojen, Ryschawy, Wulfhorst, Archer, Carvalho and Hendrickson2024; Lou et al., Reference Lou, Mo, Shao, Liu, Chong, Yu, Zheng and Qiu2025).
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• Recognize diverse farmer preferences and risk tolerance, tailoring extension outreach to producers most ready to adopt mixed systems (Abbott and Wallace, Reference Abbott and Wallace2012; Smith et al., Reference Smith, Case, Smith, Harwell and Summers2013; Pun and Baral, Reference Pun and Baral2024).
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5. Validate model outputs through field‑scale research.
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• Conduct trials measuring N dynamics, crop uptake, erosion responses, livestock performance, and nutrient cycling across varied ICLS configurations.
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• Implement long‑term experiments assessing stocking rates, grazing windows, residue interactions, and cover‑crop biomass under variable weather.
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• Use field data to refine model assumptions on manure contributions, forage digestibility, compaction risks, and temperature sensitivity.
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6. Expand future modeling efforts.
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• Develop ensemble SD Monte Carlo approaches to better quantify uncertainty in long‑term simulations (Hayden et al., Reference Hayden, Rocker, Phillips, Heins, Smith and Delate2018).
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• Embed agent‑based adoption modules to represent farmer decision-making and heterogeneity (Lardner et al., Reference Lardner, Kirychuk, Braul, Willms and Yarotski2005).
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• Explore additional social‑quality indicators—such as human well‑being, animal welfare, and community resilience—and connect frameworks to broader concepts like One Health (Lal, Reference Lal2024; Pun and Baral, Reference Pun and Baral2024).
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Abbreviations
Agricultural production systems
- CwoCC
-
Cropping without cover crop
- CwCC
-
Cropping with cover crop
- ICLSwoCC
-
Integrated crop-livestock system without cover crop
- ICLSwCC
-
Integrated crop-livestock system with cover crop
Sustainability indicators
- EQ
-
Environmental quality
- ER
-
Economic return
- SQ
-
Social quality
- IS
-
Integrated sustainability
Other abbreviations
- CC
-
Cover crop
- EQA
-
Environmental quality assessment
- RCP
-
Representative concentration pathways
- SCI
-
Soil Conditioning Index
- SD
-
System dynamics
- SOC
-
Soil organic carbon
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S174217052610060X.
Acknowledgments
This research is a contribution of the Upper Mississippi River Basin location of the Long-Term Agroecosystem Research (LTAR) Network. LTAR is supported by the U.S. Department of Agriculture—Agricultural Research Service. We would like to thank all the LTAR scientists, especially the members of the CENSA (SCAN) Working Group across the country, who are working to integrate cropping and livestock systems as one system into their research and engage a diversity of stakeholders. Most importantly, we would like to thank the stakeholders themselves, who are the primary drivers of agroecosystem transformation through efficient use of resources and regenerative agriculture practices. Many thanks to the Practical Farmers of Iowa for sharing information from their pilot studies. The authors extend their gratitude to Dr. Juan Martín-García, a globally renowned expert in system dynamics, for his invaluable technical support during the development of the SD model in this research study. Participation of Keshav Basnet was supported in part by an appointment to the Agricultural Research Service (ARS) Research Participation Program administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy (DOE) and the U.S. Department of Agriculture (USDA). ORISE is managed by ORAU under DOE contract number DE-SC0014664. All opinions expressed in this article are the authors’ and do not necessarily reflect the policies and views of USDA, ARS, DOE, or ORAU/ORISE. The USDA is an equal-opportunity provider and employer.
AI statement: The authors have not used AI to generate any of the material presented in the submitted manuscript, including figures.
Author contributions
Formulating research questions (Papanicolaou). Designing the study (Papanicolaou). Carrying out the study (Papanicolaou, Basnet). Analyzing the data (Papanicolaou, Basnet, Basnet, O’Brien, Wacha). Interpreting the findings (Papanicolaou, Basnet, O’Brien, Wacha). Writing the article (Papanicolaou, Basnet, O’Brien, Wacha, Archer, Malone, Tsegaye, Kovar).
Open access
