Clarifying confusions over carbon conclusions: antecedent soil carbon drives gains realised following intervention

Carbon removals associated with incremental gains in soil organic carbon (SOC) at scale have enormous potential to mitigate global warming, yet confusion over contexts that elicit SOC accrual abound. Here, we examine how bespoke interventions (through irrigation, fertiliser, crop type and rotations), antecedent SOC levels and soil type impact on long-term SOC accrual and greenhouse gas (GHG) emissions. Using a whole farm systems modelling approach informed using participatory research, we discovered an inverse relationship between antecedent SOC stocks and SOC gains realised following intervention, with greater initial SOC levels resulting in lower ex poste change in SOC. We found that SOC accrual was greatest for clays and least for sands, although changes in SOC in sandy loam soils were also low. Diversified whole farm adaptations – implemented through inclusion of grain legumes within wheat/canola crop rotations – were more conducive to improvement in SOC stocks, followed by Intensified systems (implemented through greater rates of irrigation, farm areas under irrigation, nitrogen fertiliser and inclusion of rice and maize in crop rotations). Adaptations that Simplified farm systems by reducing irrigation and fertiliser use resulted in the lowest SOC accrual. In most cases, long-term SOC stocks fell when SOC at the outset was greater than 4–5%, regardless of intervention made, soil or crop type, crop rotation, production system or climate. We contend that (1) management interventions primarily impacted SOC in the soil surface (0–30 cm) and had de minimus impact on deep SOC stocks (30–100 cm), (2) crop rotations including wheat, canola and faba beans were more conducive to improvement in SOC stocks, (3) scenarios with high status quo SOC had little impact on crop productivity, and not necessarily the lowest GHG emissions intensity, (4) productivity and GHG emissions intensity were largely a function of the quantum of nitrogenous fertiliser added, rather than SOC stocks, and (5) aspirations for improving SOC are likely to be futile if antecedent SOC stocks are already high (4–5 %). We conclude that potential for improving SOC stocks exists in contexts where antecedent stocks are low (<1%), which may include regions with land degradation, chronic erosion and/ or other constraints to vegetative ground cover that could be sustainably and consistently alleviated

Sustainable intensification with irrigation raises farm profit despite climate emergency

Societal Impact Statement Despite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates. Summary &bull; Research aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land-use changes in space and time. &bull; Here, we demonstrate how a new framework—WaterCan Profit—can be used to elicit such complexity using an irrigated case study farm with four whole-farm adaptation scenarios (Baseline, Diversified, Intensified and Simplified) with four types of irrigated infrastructure (Gravity, Pipe & Riser, Pivot and Drip). &bull; Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole-farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long-term economic returns. &bull; We conclude with four key insights: (1) When assessing whole-farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity-based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole-farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole-farm intensification was able to raise profitability under future climates

A statistical distribution for modelling rainfall with promising application in crop science

The finding of an accurate and reliable rainfall model has been the point of much discussion in previous research and has promising input applications in areas such as crop growth, hydrological systems and simulation studies. In the past it has been necessary to model rainfall as two separate processes: rainfall occurrence (whether the period is dry/wet) and rainfall amounts (rainfall amount observed during a wet period). As the rainfall process involves both discrete (rainfall = 0 mm) and continuous parts (rainfall >0 mm), two separate models have previously been fitted and the information from the two models combined in order to provide a summary of the rainfall model. The Tweedie distribution however is able to combine both aspects to provide one complete rainfall process. This results in a more accurate, reliable and practical model that can then be incorporated into other areas such as crop growth systems

3D characterization of crop water use and the rooting system in field agronomic research

Most field crop phenotyping research has focused on the above-ground parts of crops, ignoring a “hidden half”: the rooting system and its activity. Here we propose and test a new approach to produce 3D characterizations of crop water use and root activity in large field genotype (G) by environment (E) by management (M) experimentation, using electromagnetic induction (EMI) instrument coupled with a quasi-2D inversion algorithm, and crop canopy sensing technologies. A root activity factor (R) was calculated as a function of crop water use, soil water availability, and an indicator of crop demand. We ask i) can this approach provide accurate 3D characterizations of sorghum water use and root activity?, and (ii) does the approach capture complex GxExM dynamics?. This study was conducted based on an on-farm field experiment consisting of the factorial combination of six commercial sorghum genotypes (G), three times of sowing, two levels of irrigation (E), four plant densities (M), and three replications. Two EMI surveys ten days apart were collected using a DUALEM-21S sensor. An artificial neural network (ANN) model was developed to predict 3D soil moisture (θv) using depth-specific true soil electrical conductivity (σ, mS m−1) estimated by the inversion algorithm. Crop water use between surveys was described as the difference of θv. A multispectral index derived from satellite imagery was used as a proxy for crop demand i.e., size of the crop canopy. Principal components analysis, linear mixed models, and recursive partitioning tree techniques and crop-eco-physiological principles were used to untangle complex GxExM interactions. Results indicate that 3D crop water use could be predicted with high accuracy (LCCC = 0.81) and low prediction error (RMSE = 0.03 cm3 cm−3). The calculated water use and the value of R were significantly affected by depth, crop growth stage, irrigation treatment, plant density, and their interactions. At flowering, roots were most active at 0–1.3 m under irrigation, and deeper (0.5–1.5 m) under dryland treatment. The highest water use was for three genotypes (i.e., C, E and F) grown under irrigation and high plant densities (i.e., 9 and 12 pl m-2). The smallest water use was observed under dryland treatment, particularly for two genotypes (i.e., B and C) and high plant densities. For the crops at vegetative stages, the values of water use and R were highest in the top 0.5 m of soil depth. Larger water use was observed under dryland treatment and high plant densities, while the effects of genotypes were small (not significant). We conclude that the approach provides a rapid, accurate and cost-efficient option to phenotype crop root activity i.e., water use, in large field experimentation. We also argue that the improved understanding of the crop water use dynamics can help inform optimum combinations of genotypes and management options i.e., crop designs, across contrasting environments, and help untangle complex GxExM interactions

Assessing the sustainability of wheat-based cropping systems using simulation modelling: sustainability

Concepts of agricultural sustainability and possible roles of simulation modelling for characterising sustainability were explored by conducting, and reflecting on, a sustainability assessment of rain-fed wheat-based systems in the Middle East and North Africa region. We designed a goal-oriented, model-based framework using the cropping systems model Agricultural Production Systems sIMulator (APSIM). For the assessment, valid (rather than true or false) sustainability goals and indicators were identified for the target system. System-specific vagueness was depicted in sustainability polygons-a system property derived from highly quantitative data-and denoted using descriptive quantifiers. Diagnostic evaluations of alternative tillage practices demonstrated the utility of the framework to quantify key bio-physical and chemical constraints to sustainability. Here, we argue that sustainability is a vague, emergent system property of often wicked complexity that arises out of more fundamental elements and processes. A 'wicked concept of sustainability' acknowledges the breadth of the human experience of sustainability, which cannot be internalised in a model. To achieve socially desirable sustainability goals, our model-based approach can inform reflective evaluation processes that connect with the needs and values of agricultural decision-makers. Hence, it can help to frame meaningful discussions, from which actions might emerge

Untangling genotype x management interactions in multi-environment on-farm experimentation

Identifying optimum combinations of genotype (G) and agronomic management (M) i.e. crop design, to match the environment (E) i.e. site and expected seasonal conditions, is a useful concept to maximise crop yields and farmers’ profits. However, operationalising the concept requires practitioners to understand the likelihood of different E outcomes and GxM combinations that would maximise yields while managing risks. Here we propose and demonstrate an analysis framework to inform crop designs (GxM) at the time of sowing of a dryland maize crop, that combines data sets from multi-environment field experimentation and crop simulation modelling, and that accounts for risk preference. A network of replicated, G by M on-farm and on-research station trials (n = 10), conducted across New South Wales and Queensland, Australia, over three seasons (2014–2016) was collected. The trials consisted of combinations of commercial maize hybrids, sown at a range of plant densities and row configurations producing site average yields (Environment-yield) that varied between 1576 and 7914 kg ha−1. Experimental data were used to test the capacity of APSIM-Maize 7.10 to simulate the experimental results, and to in-silico create a large synthetic data set of multi-E (sites x seasons) factorial combination of crop designs. Data mining techniques were applied on the synthetic data set, to derive a probabilistic model to predict the likely Environment-yield and associated risk from variables known at sowing, and to derive simple “rules of thumb” for farmers that discriminate high and low yielding crop designs across the lower, middle and upper tercile of the predicted Environment-yields. Four risk profiles are described, a “Dynamic” (i.e. each year the farmer would adopt a crop design based on the predicted Environment-yield tercile and corresponding “rules of thumb”), “High rewards seeker” (i.e. each year the farmer would adopt the crop design that optimises yield for the higher tercile of Environment-yields), “Middle’er” (i.e. each year the farmer would adopt the crop design that optimises yield for the middle tercile of Environment-yields), and “Risk averse” (i.e. each year the farmer would adopt the crop design that optimises yield for the lower tercile of Environment-yields). The difference in yield between the lowest and highest performing crop design was ca. 50 % which translates into a ca. 2-fold change in water use efficiency, i.e. from 8 to 15 kg grain mm−1 rainfall. APSIM-Maize explained 88 % of the variability in the experimental data set. The validated model was used to extend the number of E sampled by adding additional sites within the same region and using historical climate records for the period 1950–2018. Crop available water at the time of sowing was a good predictor for the likelihood of the season falling within each of the three Environment-yield terciles. Recursive partitioning trees showed that plant density and hybrid were the main variables discriminating crop performance within the upper, middle and lower terciles of Environment-yields. The probability distribution functions for yield resulting from the alternative risk management strategies were tested in terms of changes in the mean yield, an index of yield stability, and down-side risk i.e. the likelihood of achieving a non-economic yield. We conclude that (i) for dryland maize cropping, the crop water availability at the time of sowing can be used to inform optimum crop designs, increase yields and yield stability and reduce down-side risks; and (ii) the proposed framework is useful to untangle complex GxExM interactions in field experimentation that provide a transferable platform to develop simple rules to identify optimum crop designs early in the season

Modelling interactions between cowpea cover crops and residue retention in Australian dryland cropping systems under climate change

Conservation agriculture management practices (e.g., cover crops and residue retention) have been widely promoted to improve soil quality and environmental sustainability. However, little is known about the long-term interactive effects of cover crops and residue retention on yield of the cash crops and environmental outcomes in dryland cropping systems under climate change. We used the pre-validated APSIM model, driven by statistically downscaled daily climate data from 27 Global Climate Models (GCMs) under two Shared Socioeconomic Pathways (SSP245 and SSP585), to assess the combined influences of cowpea cover crops and three residue retention levels on soil water balance, soil organic carbon (SOC), nitrogen (N) dynamics, crop yield and gross margin across six crop rotation systems during the historical period (1985–2020), near future (2021–2056), and far future (2057–2092) in southeast Australia. Our results showed that, on average, cover crops decreased soil moisture on the day of sowing the succeeding cash crop (by 22%), but led to greater SOC stock (21%), reduced N loss through leaching (71%), and enhanced N uptake and yield of cereals, but decreased N uptake and yield of field pea. The effects of cover crops on yield and gross margin became more positive in the far future under both SSPs, which may be attributed to the SOC increase and greater N availability in the long term. These benefits were more evident under residue removal due to the partly compensatory effects from cover crop residues. Furthermore, cover crops were profitable in the wetter parts of the study region (east), but reduced gross margin in the drier west due to depletion of soil water reserves for the next cash crop. We conclude that particularly where residues are removed, the long-term adoption of cowpea cover crops could be a potential practice to sustain crop productivity with environmental co-benefits under climate change in the wetter parts of the dryland cropping region of southeast Australia

Modelling the manager: Representing rule-based management in farming systems simulation models

We trace the evolution of the representation of management in cropping and grazing systems models, from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules. Attempts to define higher-level organizing concepts in management policies, and to analyse them to identify optimal plans, have focussed on questions relating to grazing management owing to its inherent complexity. “Rule templates” assist the re-use of complex management scripts by bundling commonly-used collections of rules with an interface through which key parameters can be input by a simulation builder. Standard issues relating to parameter estimation and uncertainty apply to management sub-models and need to be addressed. Techniques for embodying farmers' expectations and plans for the future within modelling analyses need to be further developed, especially better linking planning- and rule-based approaches to farm management and analysing the ways that managers can learn