Deconstructing agronomic resource use efficiencies to increase food production

Food production per unit land area needs to be increased, thus cropping systems need to use nutrients, water and solar radiation at as close to maximal efficiencies as possible. We deconstruct these efficiencies into their components to define a theoretical crop ideosystem, in which all resource use efficiencies are maximised. This defines an upper biological limit to food production. We then quantify the difference between maximum use efficiencies and those observed in three agronomic systems (maize, cocksfoot, sugarcane) and identify how, in actual farm systems, efficiencies can be raised to raise food production. We find that crop nutrient use efficiency can be limited by low water availability; thus adding nutrients would not raise production but adding water would. The converse situation of water use efficiency being affected by nutrition is not as evident. Ideosystem thinking can be used to define smalland large-scale agronomic systems that optimize water and nutrient use to maximise food production. Introduction Providing food for an expanding human population using lower levels of resource input, and in the face of an increasingly hostile climate (Porter et al., 2014), has led to the notion of sustainable intensification. This posits a simultaneous increase in primary production and resource use efficiencies (Garnett et al., 2013), with the main resources being water, nutrients and solar radiation. Efficiency is defined as the amount of output per unit input (Fischer et al., 2014). Efforts to find mainly genetics-based solutions to increased crop production efficiency in the field have been Ac ce pt ed p ap er disappointing (Sinclair and Rufty, 2012), mainly because of a lack of focus on and understanding of whole cropping systems. We think, as agronomists, that sustainable intensification lacks operationalization and quantification (Garnett et al., 2013). Thus, in this paper we show two things: deconstruction of cropping system intensification into operational and quantifiable resource (water, nitrogen, radiation) use efficiencies, and an assessment of the maximum level of these efficiencies that sets an absolute upper limit to food production from three systems. We measure the sustainability of intensification via the degree of closure in crop nutrient and water cycles and the minimisation of losses. Other tools, such as complex crop models (Ewert et al., 1999; Jones et al., 2003; Holzworth et al., 2014,), are too detailed to help policy persons and/or farmers define where efforts would best be focused to raise the sustainable intensification of food production. One insight offered by our method is that resource use efficiencies interact asymmetrically and that the efficiency of, for example, nutrient use depends also on water use efficiency. The policy implication is that focussing on raising water use efficiency alone is likely to benefit nutrient use efficiency and contribute to raising crop production without additional nutrient inputs – thus achieving ‘more for less’. Historically, crop physiologists, who understood the processes involved in increasing crop yields, needed to simplify this knowledge and make it accessible to plant breeders. In response to this challenge Colin Donald (Donald, 1962, 1968) developed the notion of the crop ideotype, which represented a crucial conceptual breakthrough in the Green Revolution of the 1960s by defining an ‘ideal’ structure and form of a crop plant. Thus, an ideotype would be high yielding, be resistant to pest damage and weed competition and would maximise the use of environmental resources such as water, nutrients, light and temperature by, for instance, having erect rather than prostrate leaves. The ideotype concept crucially moved thinking away from regarding crops as composed of individual plants, to the crop as a population with yield as the crop outcome . We wish to expand the ideotype idea and develop a parallel concept of an ideosystem to describe and quantify the properties of sustainable and intensified cropping systems, of which ideotypes may be a component – thus Ac ce pt ed p ap er expanding the focus of food production from individual plants to crops to whole farm systems. Theory Increases in yield can occur at many levels in a crop production system. For example, grain yield (i.e. production or yield per area) can be increased by raising any of the elements in the right-hand side of Equation 1. There can be trade-offs between elements, but for the major cereals, raising the number of grains per ear (i.e. total grain sink capacity) has been more important than individual grain size in increasing crop yields (Hay and Porter, 2006). Grain Yield Ear Grains Area Ears Area Yield    (1) Equation 1 is, strictly speaking, a mathematical identity because it deconstructs the element on the left of the ‘≡’ (equivalence) operator into its component parts (Bennett et al., 2012). This enables us to consider how crop structure and function will influence each of the elements on the right to increase (or decrease) the element on the left. Each of the right-hand side components can increase or decrease yield per unit area, with the net result of yield/area being the trade-off between the three elements on the right of the ‘≡’ (equivalence) sign. Recently, the identity approach, as the KayaPorter identity, has been used to deconstruct and estimate greenhouse gas emissions from agriculture from 1970, with extrapolation to 2050 (Bennetzen et al., 2016). The advantage of this approach being that emissions can be linked to actual agricultural practices, cropping area and emissions per unit agricultural product. Transferring this idea to a higher crop system level, identities can also be used to consider resource use efficiencies (Van Noordwijk and De Willigen, 1986; Porter and Christensen, 2013, Wang et al., 2020). Nitrogen use efficiency (NUE) can be deconstructed as follows: Ac ce pt ed p ap er Nuptake Biomass bleN SoilAvaila Nuptake s FertNinput bleN SoilAvaila s FertNinput Biomass NUE    = (2) The net effect, as elements in the identity cancel, is Nitrogen Use Efficiency (NUE) defined as biomass per unit of nitrogen input; the conventional and basic definition. As in the components of yield example above, NUE can be raised or lowered via changes in the ratios on the RHS of the ≡ sign, with the proviso that the denominator values must be larger than zero. Similarly, water use efficiency (WUE) can be deconstructed as: e WaterUptak Biomass bleWater SoilAvaila e WaterUptak Inputs Irrigation bleWater SoilAvaila Inputs Irrigation Biomass WUE    = (3) In each case, use efficiencies are reduced to NUE = Biomass / FertNIinputs and, similarly, WUE = Biomass/Irrigation inputs, also known as the marginal irrigation WUE (Fischer et al., 2014). The identity for radiation use efficiency (Porter and Christensen, 2013) (Equation 4) can be written as: esis Photosynth Biomass dRad Intercepte esis Photosynth SolarRad dRad Intercepte SolarRad Biomass RUE    = (4) Each of the elements in the resource use efficiency identities (Equations 2 and 3) can be drawn as four connected quadrants (Van Noordwijk and De Willigen, 1986) (Fig. 1). Quadrant A (the fieldcrop quadrant) represents biomass production per unit of resource input and is represented by Biomass/FertNinputs in Equation 2, coming closest to the agronomic definition of resource efficiency (Sinclair and Rufty, 2012). Quadrant B (the soil quadrant; SoilAvailableN/FertNinputs in Equation 2) represents the resource available in the soil per unit of resource input from Quadrant A and has a non-zero intercept because there is usually some water or nutrient in the soil when the crop is planted. Quadrant C (the root quadrant; Nuptake/SoilAvailableN in Equation 2) represents Ac ce pt ed p ap er resource uptake per unit resource available in the soil from Quadrant B. Finally, D (the canopy quadrant; Biomass/Nuptake in Equation 2) represents biomass production per unit of resource uptake from Quadrant C. The curvilinear relationship in Quadrant D reflects the biological limits of conversion of nutrient into biomass and will change for different crop species and stage of biomass accumulation (Lemaire and Gastal, 2009). The dashed lines in Quadrants A and D represent what would happen for crops with excessive resource uptake. A crucial element in Figure 1 are the black lines at angles of 45o, since these represent the 100% efficiencies in each of the quadrants. A perfect ideosystem would occur if all use efficiencies, for all resources in all quadrants, were found on the 45o lines, as resource use efficiencies in both a relative (as % of maximum) and an absolute (as output divided by input) sense are then at maximum. The questions are how close actual cropping systems come to this ‘ideal’ state and what maximum use efficiencies can be reached and with what consequences for crop biomass production. The usefulness of the ideosystem concept can be seen by taking the outer black line in Fig. 1, which has a higher biomass production than the inner grey line, which has zero added nutrients. However, the inner grey line shows higher resource use efficiency because the biomass per unit nutrient uptake is on the linear part of the curve in Quadrant D. The efficiency associated with the black line is lower because the high inputs cause availability to exceed potential uptake (Quadrant C) and the biomass production per unit uptake reduces at higher uptake levels (Quadrant D). This example shows that high biomass production and high resource use efficiency are not necessarily, and perhaps rarely, connected and makes the point that differences in nutrient resource efficiency needs to be determined at the whole, and not part, system level, since differences in component efficiencies at any level in the cropping system can affect biomass and/or food production (Equations 2-4). In order to get an appreciation of the functioning of a whole