Delve, Robert J.

Dimes, John P.

Probert, ME

CGSpace

135Improved Capabilities in Modelling and Recommendations: SummaryR.J. Delve,* M.E. Probert† and J.P. Dimes§The purpose of the project ‘Integrated nutrient man-agement in tropical cropping systems: improvedcapabilities in modelling and recommendations’(ACIAR Project no. LWR2/1999/03) was to test andenhance a modelling capability that can be applied tofarming systems where both organic and inorganicsources of nutrients are used. In tropical regions,organic materials are often more important for main-tenance of soil fertility than fertilisers, yet currentfertiliser recommendations and most crop models areunable to take account of the organic inputs and thedifferent qualities of these organic inputs used byfarmers. When the project commenced, simulation model-ling had a limited ability to predict the effects onsoil processes and crop growth of organic sourcesthat differed in ‘quality’. APSIM was chosenbecause it had draft modules to describe the releaseof nutrients (both nitrogen and phosphorus) frommanures (Manure module) and the dynamics of P insoil (SoilP module), and it contained routines tolimit crop growth under conditions of water, N andP stress. At the start of the project, these moduleswere largely untested. The project tested, and wherenecessary improved, the APSIM Manure and SoilPmodules, so that they can be applied to the manage-ment of soil fertility, especially in low-inputsystems in the tropics.Developments in Improving APSIM Manure and SoilP ModulesResource-poor farmers regularly make decisions onthe use of scarce nutrient sources in crop–livestockproduction systems. The decisions made generallyreflect farmer experience (of expected returns) andlivelihood preferences. However, for resource-poorfarmers, ‘experience’ is often limited by the feasi-bility and capability of the farmer to experiment withalternative management practices. In the case of allo-cating animal manure for crop production, this deci-sion is usually taken with limited knowledge of theimpact of the potential of alternative uses on plantproduction and soil and water resources. A deeperunderstanding of the comparative values and useful-ness of manures and other locally available resourcesand sources of P would help fill such knowledgegaps, offering the possibility for increased produc-tion and efficiency of mixed crop–livestock systems.While efforts are required to expand our knowledgeof the biophysical aspects of alternative uses oforganic nutrient sources, similar efforts are alsorequired on the socioeconomic driving forces behindfarmers’ decision-making. Cross-method analysis of plant qualityN content or C:N ratio are the primary indicators ofdecomposition and N release across a range of plantmaterials of different quality. However, other param-eters (such as lignin and polyphenolics) are needed toexplain the variation observed in N mineralisationstudies. Measurements for assessing plant resource qualityinclude an extensive array of proximate analyses(lignin, acid-detergent fibre (ADF), total soluble* Tropical Soil Biology and Fertility Institute ofInternational Centre for Tropical Agriculture, PO Box6247, Kampala, Uganda <r.delve@cgiar.org>.† CSIRO Sustainable Ecosystems, 306 Carmody Road, St Lucia, Queensland 4067, Australia <merv.probert@csiro.au>.§ International Crops Research Institute for Semi-Arid Tropics, PO Box 776, Bulawayo, Zimbabwe <j.dimes@cgiar.org>.5Nutrients.book  Page 135  Tuesday, February 24, 2004  1:53 PM136polyphenolics, and a variety of condensed tannins).Decomposition is determined by the combination ofthese different factors. Indirect methods that canserve as ‘integrative measures’ of resource qualityare discussed in Section 3 of these proceedings.These include aerobic decomposition, near infraredreflectometry (NIR) and in vitro dry matter digesti-bility. Results from these integrative methods corre-lated well with mineralisation rates estimated in thetraditional leaching tube experiment and have thepotential to predict this laboratory estimation ofresource quality.From this cross-method analysis, the minimumdata set to assess organic resource quality consists ofN, lignin, and soluble polyphenol content. Thisfinding is consistent with conclusions from earlierefforts. Considerations of cost and speed also need tobe compared where more than one method is avail-able. Aerobic incubations are one of the cheapest butslowest methods, whereas NIR is the fastest.Although NIR instrumentation is expensive to set up,for routine analysis of many samples it could becomecost effective. Construction of spectral calibrationlibraries in centralised laboratory facilities wouldgreatly increase the efficiency of NIRS use forroutine organic resource characterisation in laborato-ries and dramatically reduce the costs of such anal-yses.Modifying and testing the APSIM Manure moduleExisting laboratory incubation experiments andSWNM network trials in East and southern Africasituated in diverse agro-ecological zones and soiltypes were used to test the manure module (seeSection 4). The field experiments used manures ofdiffering quality and combined organic and inorganicsources of N. These trials provided data on the shortand long-term effects on nitrogen availability, soilorganic matter and crop production — informationthat is necessary for testing APSIM and to providethe insights needed for making any modifications toAPSIM. Initially, as in other models of soil organic matterturnover, the model assumed that the soil organicmatter pools (BIOM and HUM) have C:N ratios thatare unchanging during the decomposition process.Additions of fresh organic matter (FOM) are consid-ered to comprise three pools (FPOOLs): the carbohy-drate-like, cellulose-like and lignin-like fractions.Each FPOOL has its own rate of decomposition,which is modified by factors to allow for effects ofsoil temperature and soil moisture. Although thethree fractions have different rates of decomposition,they did not have different compositions in terms ofC and N content. During this project we concludedthat, to simulate release of N from diverse sources ofmanure, the model could match observed short-termrelease patterns only if the pools had different C:Nratios. This insight came from the results of labora-tory studies that showed variable N-release patternsdepending on the C:N ratio of the soluble fraction ofthe manures (Probert et al., these proceedings). TheAPSIM Manure module has been modified so thatthe pools can now be specified to have different C:Nratios. This enabled the effects of different qualitiesof organic resources on N-mineralisation patterns tobe simulated in accordance with observed responses,especially during periods immediately followingmanure application. Modifying and testing APSIM’s phosphorus routinesThe SoilP and modified maize modules thatexisted when the project commenced explored thefeasibility that it might be possible to include P-stressas a limitation to growth in APSIM crop models.During this project, as it became clear that that wasindeed feasible, two significant advances were made.Firstly, the P status of the crop had been consideredonly on a whole plant basis. This is not consistentwith how N is modelled in the APSIM crop modules;in particular, it is far from ideal when a sequence ofcrops is to be simulated (for example, how to handleP in residues at harvest?). The development was tosimulate the partitioning of P to different plant com-ponents (leaf, stem, flower, grain, root) throughoutthe life of the crop. A consequence of this develop-ment is that the data requirements for specifying theP dynamics in the crop are much greater, because Pconcentrations in the various plant components needto be described. A new parameter set (specifying Pconcentrations and stresses) was created for maize(based on results from an earlier experiment at Katu-mani — ACIAR Project no. 8326). The second improvement was in the P-uptake rou-tine. In the prototype, this was directly related to theamount of labile P in a soil layer. Current under-standing suggests it ought to be related to the P con-centration in solution at the root interface. A newNutrients.book  Page 136  Tuesday, February 24, 2004  1:53 PM137routine was introduced into the code and ‘tuned’ tothe Katumani experiment. Testing against other datasets (particularly that from Maseno on a very dif-ferent soil with much higher P sorption characteris-tics, and from Machang’a) showed that the newuptake routine and parameter set was transferable.Having shown that the model could simulate P-defi-cient maize, the P routines were put into an APSIMcrop template so that, in principle, any model usingthe template can be P-aware, provided the necessaryparameter set exists to define P concentration in theplant and the effects of P stress on the plant growthprocesses.The initialisation of the APSIM SoilP modulerequires inputs for labile P and P sorption on a soillayer basis. In this project, labile P has been identifiedwith bicarbonate extractable P (Olsen P), thoughfurther testing on a wider range of soils is needed. Itis unlikely that there is a 1:1 fit between the concep-tual labile P of the model and any soil P test. Themeasure of ‘P sorption’ used is the amount of Psorbed at 0.2 mg L–1. For model-testing purposes, the only additionalcrop variables beyond those needed to validate the Nand water routines would be P concentrations in plantcomponents and P uptake. The parameter set requiredto make a crop model ‘P-aware’ comprises values forthe maximum, minimum and senesced P concentra-tion in the different plant components through thegrowth cycle of the crop, together with factors spec-ifying how P stress affects photosynthesis, leafexpansion and phenology. Unfortunately, during the project, no data set fromLatin America, Africa or Asia contained all therequired data to thoroughly evaluate the model. Ide-ally, for testing the ability of the SoilP module to sim-ulate effects of P on crop growth, one would want tolook at crop growth (yields, phenology, leaf area,nutrient uptake), soil water and rooting depth,mineral N in soil, soil P test values and, in a long-termexperiment, soil organic matter. To address this,ongoing fieldwork in Latin America will be used forfurther testing of the model.None of the data sets explicitly addressed theeffectiveness of P in organic sources. In the model,mineralisation of P is simulated in a similar mannerto N and will be determined by the C:P ratios of thesubstrate and the soil organic matter being synthe-sised. Using typical values for C:P in soil microbialbiomass of 10–35, leads to the inference that net Pmineralisation from an organic source would requirea C:P of less than 100 (i.e. a P concentration ofgreater than 0.4% in tissue). This does not conformwith published data. The cause of the disparity isagain thought to lie in the C:P ratio not remainingconstant during decomposition. For P, the water-soluble fraction has a much lower C:P ratio than thetotal C:P. Therefore, in the enhanced SoilP modulethe release of plant-available P from organic inputsdepends on the FPools having different C:P ratios. Testing of APSIM with long-term tropical data setsLong-term experiments covering a range of soiltypes were identified by project collaborators in Eastand West Africa, Latin America, and Southeast Asia,and were used to test the new APSIM modules forpredicting nutrient release and plant growth underfield conditions. Results show that the model per-formed well across a wide range of applications, fromsimulation of N and P supply to crops where P con-straints were more severe than N, to long-term P andC dynamics, and for crop responses to different ratesand qualities of manures, responses to inorganic andmanure combinations, and residual benefits ofmanure. Using the long-term data for the Machang’a exper-iment, the model was shown to accurately predictcrop responses to inputs of manure or fertiliser, whilethe predicted dynamics of labile P in soil were similarto the measured Olsen P data. Of course, none ofthese simulations was perfect, and discrepanciesbetween observed and predicted data were reported.Notably for the Maseno data set, where soil P wasdetermined irregularly (and soon after fertiliserapplication), the agreement between observed andpredicted values was poor. In some cases, the lack offit between model and observed data will arise fromlimitations in the modelling capability, e.g. there areeffects of manures other than N and P that cannot bemodelled. In other cases, the discrepancies are due toour poor understanding of the observed responses,which limits our interpretation. Generally, experiments, especially long-termones, are not established with model validation ordevelopment in mind. This project used existinglong-term experiments from East and West Africa,Latin America, and Southeast Asia and, of course,found some shortcomings in the available data.While this did not hamper model development, it hasprevented the evaluation of model performance fromNutrients.book  Page 137  Tuesday, February 24, 2004  1:53 PM138being as thorough as one might like. Accordingly,new experimentation was established during thisproject in Latin America, to overcome these data con-straints and for further testing of APSIM.Extension to other cropsThe ‘P-aware’ maize model was a major break-through in our thinking of how to explicitly reflectsoil P dynamics and especially P limitations in cropsimulations. There is a clear need to apply the newcapability to other crops. During this project, field-work was initiated to conduct experiments that wouldpermit parameter sets to be assembled. This workwas co-funded from other projects. The crops beingstudied are cowpea and millet (funded by IFDC inWest Africa), pigeonpea, groundnut and sorghum(funded by DFID and ICRISAT in India), and canola(funded by CSIRO in Australia). Many of the soils in Africa and Latin America areP-fixing and/or P deficient, and these projects arenow contributing further modelling capability for Pdynamics in these farming systems. The SoilPmodule developed and evaluated within this projecthas provided the opportunity for these other projectsto proceed, and this is a major outcome and onemeasure of the project’s success.Future Activities Needed in Developing APSIM The ACIAR-funded project LWR2/1999/03 devel-oped a unique capability in simulation modelling byintroducing the ability to have crops respond to Pconstraints and to model N and P dynamics followingaddition of manures of different quality. Thereremains a need to test these routines against a widersuite of data sets, especially for a wider range of crop-ping systems and soil types. The external review team made several recom-mendations about follow-up activities with projectpartners. In the short and medium terms, these wereto:1. generate a few high-quality contrasting data setsfor validation2. refine and publish the data collection protocolsfor others to use3. start working with end users including farmersto utilise model outputs.A two-year project extension has since beenfunded by ACIAR to address the first two recommen-dations and, at the same time, strengthen the skills ofcurrent APSIM users in the project. The major com-ponents of this extension are as follows:1. The N and P capabilities of the APSIM Manureand SoilP modules are being further testedagainst existing data sets in Latin America andSoutheast Asia. Field studies established duringthe project were designed to provide a compre-hensive data set for testing of the SoilP moduleand the P routines in the maize module. Thesestudies are still in progress. 2. As part of the testing activities above,researchers from Latin America and SoutheastAsia would be exposed to the use of the APSIMmodel and how it might be applied in thecarrying out of their research activities and inextending, to the farming community, theresults of their research. Training in the use ofAPSIM for the partners would be a componentof this activity.3. The data collection protocols for manure char-acterisation are being refined and published, sofuture researchers collect data that are appro-priate to Manure module use. Also, theminimum data set protocols for APSIM arebeing updated. Nutrients.book  Page 138  Tuesday, February 24, 2004  1:53 PM

Improved capabilities in modelling and recommendations: summary

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Improved capabilities in modelling and recommendations: summary

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