# Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa

Antoine Couëdel, Gatien N. Falconnier, Myriam Adam, Rémi Cardinael, Kenneth Boote, Eric Justes, Ward N. Smith, Anthony M. Whitbread, François Affholder, Juraj Balkovic, et al.

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## To cite this version:

Antoine Couëdel, Gatien N. Falconnier, Myriam Adam, Rémi Cardinael, Kenneth Boote, et al. Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa. European Journal of Agronomy, 2024, 155, pp.127109. 10.1016/j.eja.2024.127109. hal-04552856

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Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
1 Long term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa

3 Antoine Couëdel¹, Gatien N. Falconnier¹,², Myriam Adam⁴,⁵, Rémi Cardinael¹,⁶, Kenneth

4 Boote⁷, Eric Justes⁸, Ward N. Smith⁹, Anthony M. Whitbread³, François Affholder¹,¹⁰, Juraj

5 Balkovic¹¹, Bruno Basso¹², Arti Bhatia¹³, Bidisha Chakrabarti¹³, Regis Chikowo⁶,¹⁴, Mathias

6 Christina¹,¹⁵, Babacar Faye¹⁶, Fabien Ferchaud¹⁷,¹⁸, Christian Folberth¹¹, Folorunso M.

7 Akinseye¹⁹,²⁰, Thomas Gaiser²¹, Marcelo Galdos²², Sebastian Gayler²³, Aram Gorooei²⁴, Brian

8 Grant⁹, Hervé Guibert¹, Gerrit Hoogenboom⁷, Bahareh Kamali²⁴, Moritz Laub²⁵, Fidel

9 Maureira¹², Fasil Mequanint²³, Claas Nendel²⁶,²⁷,²⁸, Cheryl H. Porter⁷, Dominique Ripoche²⁹,

10 Alex C. Ruane³⁰, Leonard Rusinamhodzi³¹, Shikha Sharma¹³, Upendra Singh³², Johan Six²⁵, Amit

11 Srivastava²¹, Bernard Vanlauwe³, Antoine Versini³³, Murilo Vianna²⁴, Heidi Webber²⁶,³⁴, Tobias

12 Weber²³,³⁵, Congmu Zhang⁷, Marc Corbeels¹,³

¹ AIDA, Univ Montpellier, CIRAD, Montpellier, France

² International Maize and Wheat Improvement Centre (CIMMYT)-Zimbabwe, 12.5 km Peg Mazwe Road, Harare, Zimbabwe

³ International Institute of Tropical Agriculture (IITA), c/o ICIBE Compound P.O. Box 30772-00100, Nairobi, Kenya

⁴ UMR AGAP Institut, Univ Montpellier

⁵ National University of Battambang, Battambang Province, 021402, Kingdom of Cambodia

⁶ Department of Plant Production Sciences and Technologies, University of Zimbabwe, Harare, Zimbabwe

⁷ University of Florida, Gainesville, FL, USA

⁸ PERSYST, Univ Montpellier, CIRAD, Montpellier, France

⁹ Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON, Canada

¹⁰ CIRAD, UPR AIDA, Maputo, Mozambique

¹¹ Biodiversity and Natural Resources Program, International Institute for Applied Systems Analysis, Laxenburg, Austria

¹² Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA

¹³ Indian Agricultural Research Institute, New Delhi, India

¹⁴ Plant, Soil and Microbial Sciences Department, Michigan State University, East Lansing, MI 48824, USA

¹⁵ CIRAD, UPR AIDA, F-97743, Saint-Denis, La Réunion, France

¹⁶ Université Université du Sine Saloum El Hadj Ibrahima Niass (USSEIN), Kaolack, Senegal

¹⁷ BioEcoAgro Joint Research Unit, INRAE, Université de Liège, Université de Lille, Université de Picardie Jules Verne, 02000, Barenton-Bugny, France

¹⁸ UMR Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, InstitutAgro Montpellier, Montpellier, France

¹⁹ International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), Senegal

²⁰ Centre d'Etude Régional pour l'Amélioration de l'Adaptation à la Sécheresse (CERAAS), Thiès Escale, Senegal

²¹ Institute of Crop Science and Resource Conservation, University of Bonn, Katzenburgweg 5, D-53115, Bonn, Germany

²² Sustainable Soils & Crops, Rothamsted Research, Harpenden, UK

²³ Biogeophysics, Institute of Soil Science and Land Evaluation, Hohenheim University, 70599, Stuttgart, Germany

²⁴ Institute for Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany

²⁵ Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland

²⁶ Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany

²⁷ Global Change Research Institute CAS, Brno, Czech Republic

²⁸ Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

²⁹ AgroClim, INRAE, Avignon, France

³⁰ NASA Goddard Institute for Space Studies, New York, NY 10025, USA

³¹ International Institute of Tropical Agriculture (IITA), PMB L56 Legon-Accra, Ghana

³² International Center for Soil Fertility and Agricultural Development, Muscle Shoals, AL, USA

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33 CIRAD, UPR Recyclage & Risque, F-97743, Saint-Denis, La Réunion, France

34 Brandenburg University of Technology, Faculty of Environment and Natural Sciences, Cottbus, Germany

35 Soil Science Section, Faculty of Organic Agricultural Sciences, University of Kassel, Kassel, Germany

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## 14 Abstract

Food insecurity in sub-Saharan Africa (SSA) is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of farming systems including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model assessments have been shown to be more robust than single models and help understand and reduce model uncertainty. In this study we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedback in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term experiments at sites in SSA with contrasting climates and soils. Each experiment had four experimental treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N), iii) organic amendments, and iv) combined mineral and organic inputs. We assessed model performance in two steps: after blind and full calibration including respectively low and high levels of experimental data observations provided to the modeling teams. Model ensemble accuracy was greater with full calibration than in blind calibration. Calibration improvements were larger for crop yields (nRMSE 48 vs 18%) than for SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the course of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were added to the soil, whilst uncertainty of yield predictions was largest when no inputs were applied. We highlight model discrepancies in simulating i) crop to soil feedback due uncertainties in simulated carbon (C) coming from roots, ii) soil to crop feedback due to large uncertainties in simulated soil N mineralization from soil organic matter. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term

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38 experiments that monitor root and N dynamics and provide the corresponding data to calibrate and improve soil-crop models and to achieve robust and reliable simulation of change in SOC and crop productivity.

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42 **Keywords:** soil-crop simulation, soil organic matter, soil-crop feedback, ensemble modelling, model intercomparison, long-term experiments

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# 1. Introduction

47 Food insecurity in sub-Saharan Africa (SSA) can be largely attributed to low cereal crop yields often caused by low soil fertility and low external input use (Sanchez, 2002). In fact, the large yield gaps of cereal crops in sub-Saharan Africa can mostly be explained by soil nutrient limitations rather than climate factors (Van Ittersum et al., 2016). Soil fertility has declined in many regions of sub-Saharan Africa as a result of the low use of mineral and organic fertilizers, the shortening of fallow periods and soil erosion (Vanlauwe et al., 2015b; Vanlauwe and Giller, 2006). Sustainable intensification schemes, such as Integrated soil fertility management (ISFM), aiming at maximizing the agronomic use efficiency of nutrients through the combined use of mineral and organic fertilizers, are promising entry points to increase both crop yield and soil organic carbon (SOC), with potential positive impacts on food security and adaptation to climate change (Gram et al., 2020; Vanlauwe et al., 2010).

57 Beside the short-term effects brought by the immediate crop response to mineral and organic fertilizers, the performance of ISFM depends on long-term soil-crop feedbacks. Soil-crop feedbacks describes an ecological processes wherein a crop leaves biotic and/or abiotic soil legacies and vice versa, which impact the growth of subsequent crops (Bever, 1994; Pernilla Brinkman et al., 2010).

61 Indeed, SOC build-up through ISFM can i) increase/maintain nutrient supply in the long-term through the decomposition of a greater amounts of soil organic matter (SOM), and ii) enhance/maintain soil aggregation and improve soil structure which can favor root development (Adams et al., 2020). In turn,
<table>
  <tr>
    <td>1</td>
    <td>64</td>
    <td>higher crop productivity with larger amounts of crop residues (roots and shoots) increases C inputs</td>
  </tr>
  <tr>
    <td>2</td>
    <td>65</td>
    <td>into the soil. Other crop-to-soil feedbacks include nutrient cycling (e.g. direct N carry over from</td>
  </tr>
  <tr>
    <td>3</td>
    <td>66</td>
    <td>legumes to cereals), carry-over of pests and diseases (e.g pathogens) and mutualist biota (e.g.,</td>
  </tr>
  <tr>
    <td>4</td>
    <td>67</td>
    <td>symbiotic N-fixing bacteria, mycorrhizal fungi) (Mariotte et al., 2018; Smith-Ramesh and Reynolds,</td>
  </tr>
  <tr>
    <td>5</td>
    <td>68</td>
    <td>2017). However, soil-crop feedback mechanisms are complex. For example, root growth is also known</td>
  </tr>
  <tr>
    <td>6</td>
    <td>69</td>
    <td>to stimulate SOM decomposition through the rhizosphere priming effect (Cheng and Kuzyakov, 2005;</td>
  </tr>
  <tr>
    <td>7</td>
    <td>70</td>
    <td>Dijkstra et al., 2021; Kuzyakov, 2002), and fertilizers do not always increase yields, e.g. in the case of</td>
  </tr>
  <tr>
    <td>8</td>
    <td>71</td>
    <td>non-responsive soils (Vanlauwe et al., 2015a, 2010), but on the other hand may stimulate SOM</td>
  </tr>
  <tr>
    <td>9</td>
    <td>72</td>
    <td>decomposition (Feng et al., 2022; Zhou et al., 2014).</td>
  </tr>
  <tr>
    <td>10</td>
    <td>73</td>
    <td>Long-term experiments on ISFM can provide valuable insights into soil-crop feedback mechanisms</td>
  </tr>
  <tr>
    <td>11</td>
    <td>74</td>
    <td>(Bationo et al., 2012; Cardinael et al., 2022; Laub et al., 2023b). However, such experiments are costly,</td>
  </tr>
  <tr>
    <td>12</td>
    <td>75</td>
    <td>and cannot capture the entire variability of soils, climatic conditions and management practices that</td>
  </tr>
  <tr>
    <td>13</td>
    <td>76</td>
    <td>exists in farmers' context (Körschens, 2021; Mirtl et al., 2018). Alternatively, process-based soil-crop</td>
  </tr>
  <tr>
    <td>14</td>
    <td>77</td>
    <td>models that simulate daily soil processes and plant growth, can help explore long-term effects of ISFM</td>
  </tr>
  <tr>
    <td>15</td>
    <td>78</td>
    <td>on crop growth and soil fertility for different crop genotypes, soil types, and management practices.</td>
  </tr>
  <tr>
    <td>16</td>
    <td>79</td>
    <td>These models extend beyond the confines of experimental settings and allow for the exploration of</td>
  </tr>
  <tr>
    <td>17</td>
    <td>80</td>
    <td>the interactions between these factors (G x E x M). More specifically, the coupled soil-plant models</td>
  </tr>
  <tr>
    <td>18</td>
    <td>81</td>
    <td>can account for some of the complex feedback mechanisms between SOC dynamics and crop growth</td>
  </tr>
  <tr>
    <td>19</td>
    <td>82</td>
    <td>over the long term (Basso et al., 2018; Smith et al., 2020). In fact, the existing models incorporate crop-</td>
  </tr>
  <tr>
    <td>20</td>
    <td>83</td>
    <td>to-soil feedbacks primarily by considering C inputs derived from roots and/or shoots of the cultivated</td>
  </tr>
  <tr>
    <td>21</td>
    <td>84</td>
    <td>crops. As for the soil-to-crop feedbacks, they principally consider N supply originating from the</td>
  </tr>
  <tr>
    <td>22</td>
    <td>85</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>23</td>
    <td>86</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>24</td>
    <td>87</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>25</td>
    <td>88</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>26</td>
    <td>89</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>27</td>
    <td>90</td>
    <td>These models extend beyond the confines of experimental settings and allow for the exploration of</td>
  </tr>
  <tr>
    <td>28</td>
    <td>91</td>
    <td>the interactions between these factors (G x E x M). More specifically, the coupled soil-plant models</td>
  </tr>
  <tr>
    <td>29</td>
    <td>92</td>
    <td>can account for some of the complex feedback mechanisms between SOC dynamics and crop growth</td>
  </tr>
  <tr>
    <td>30</td>
    <td>93</td>
    <td>over the long term (Basso et al., 2018; Smith et al., 2020). In fact, the existing models incorporate crop-</td>
  </tr>
  <tr>
    <td>31</td>
    <td>94</td>
    <td>to-soil feedbacks primarily by considering C inputs derived from roots and/or shoots of the cultivated</td>
  </tr>
  <tr>
    <td>32</td>
    <td>95</td>
    <td>crops. As for the soil-to-crop feedbacks, they principally consider N supply originating from the</td>
  </tr>
  <tr>
    <td>33</td>
    <td>96</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>34</td>
    <td>97</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>35</td>
    <td>98</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>36</td>
    <td>99</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>37</td>
    <td>100</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>38</td>
    <td>101</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>39</td>
    <td>102</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>40</td>
    <td>103</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>41</td>
    <td>104</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>42</td>
    <td>105</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>43</td>
    <td>106</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>44</td>
    <td>107</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>45</td>
    <td>108</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>46</td>
    <td>109</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>47</td>
    <td>110</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>48</td>
    <td>111</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>49</td>
    <td>112</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>50</td>
    <td>113</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>51</td>
    <td>114</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>52</td>
    <td>115</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>53</td>
    <td>116</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>54</td>
    <td>117</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>55</td>
    <td>118</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>56</td>
    <td>119</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>57</td>
    <td>120</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>58</td>
    <td>121</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>59</td>
    <td>122</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>60</td>
    <td>123</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>61</td>
    <td>124</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
  <tr>
    <td>62</td>
    <td>125</td>
    <td>Some studies have assessed the capability of process-based soil-crop models to reproduce the effects</td>
  </tr>
  <tr>
    <td>63</td>
    <td>126</td>
    <td>of drought on crop yield (Kamali et al., 2022) across SSA or mineral and organic fertilizers on SOC</td>
  </tr>
  <tr>
    <td>64</td>
    <td>127</td>
    <td>content (Kamoni et al., 2007) in Kenya. However, feedbacks between SOC and crop biomass have</td>
  </tr>
  <tr>
    <td>65</td>
    <td>128</td>
    <td>decomposition of crop residues and SOM.</td>
  </tr>
</table>
received little scrutiny in these studies. DAYCENT (Del Grosso et al., 2001) and LPJ-GUESS (Smith et al., 2014), two process-based soil-plant models, simulated reasonably average maize yield and SOC responses to ISFM in a long-term experiment in Kenya, but interannual variability in yield was not well captured (Ma et al., 2022; Nyawira et al., 2021). Although these studies highlighted the strengths and weaknesses of a few models, they did not capture the full diversity of existing soil-crop models and evaluated a limited number of climate and soil conditions that prevail in SSA.

Multi-model ensembles can help reduce the uncertainties in the simulation of soil and crop variables as a result of compensation of individual model errors and wider integration of model formalisms (Martre et al., 2015; Wallach et al., 2018). Furthermore, model intercomparisons can help understand to what extent models differ in their design and skills to accurately predict soil and crop variables, which in turn can guide the improvement of these models. Yet, model intercomparisons have mainly focused on the impact of climate change on crop yields, such as wheat, maize and rice yields (e.g. Asseng et al., 2013; Bassu et al., 2014; Kothari et al., 2022; Li et al., 2015). Few studies have focused on simulating SOC dynamics (Bruni et al., 2022; Farina et al., 2020; Smith et al., 1997, 2012), and even fewer have simulated conjointly crop yield and SOC dynamics (Basso et al., 2018; He et al., 2021; Sándor et al., 2020). In fact, multi-model comparisons about SOC dynamics have either been done in the absence of observations for model calibration (Basso et al., 2018), or were conducted for bare soil conditions only, i.e. without plant growth (Bruni et al., 2022; Farina et al., 2020) or for temperate climates only (He et al., 2021; Sándor et al., 2023, 2020; Smith et al., 2012). For example, Sándor et al. (2023) found that some models overestimated observed SOC contents, while others underestimated them, and that the model ensemble median outperformed individual models in 92% of cases. Inconsistencies between model simulations and observed data can be caused by inaccuracies in the simulation of soil and/or crop processes. Comparing models accurately becomes challenging due to the complexities arising from the contribution of individual components (such as soil and crop) to the overall model inaccuracy (Ehrmann and Ritz, 2014).

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When dealing with ISFM in SSA, two additional challenges need to be considered: i) N and water stresses need to be well accounted for in the model to accurately simulate crop yields, especially in low-input systems (Falconnier et al., 2020), and ii) organic fertilizers have to be well represented in the model and their impact on SOC dynamics and crop growth need to be accurately simulated (Cavalli et al., 2019; Levavasseur et al., 2021). While Falconnier et al. (2020) showed the potential of using multi-model ensembles for accurate maize yield simulations in SSA, we are not aware of any model intercomparison that evaluates the accuracy of both crop yield and SOC simulations across a range of experimental sites over the long term (without model reinitialization each year). Indeed, most model intercomparisons use short-term field experiments for model calibration and do not account for carry over effects of soil variables from year to year (i.e. apply model reinitialization every year). Yet, Basso et al. (2018) and Smith et al. (2020) found a large effect of including soil-crop feedbacks from soil C and N processes on simulated crop yields.

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## Methods

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139 2.1 Experimental data for model calibration

140 2.1.1. Experimental design

141 We calibrated 16 soil-crop models using data from long-term maize experiments at four sites across sub-Saharan Africa. The sites span a range of climates (from warm to cool and from semi-arid to humid), soil textures (from sandy to clayey) and soil fertility levels (varying initial SOC contents 0.3-3%) (Table 1). The selected long-term experiments (ranging from 9 to 18 years in duration depending on the site) were conducted on station or on farm, but managed by researchers. In three sites (Côte d'Ivoire-Gagnoa, Kenya-Embu and Kenya-Machanga), there were two growing seasons per year. The crop grown was maize, except for the site in Côte d'Ivoire where in the second growing season soybean was sown (in two years of the experiment) or the soil was left bare (in five years of the experiment).
145 Experiments were rainfed and included four treatments: i) a control without external C and N inputs (-C-N), ii) the application of mineral N fertilizer (between 100 and 160 kg N ha⁻¹ per growing season depending on the site) (-C+N), iii) the application of organic amendment (manure, or compost from crop residues, between 3 and 4 t C ha⁻¹ per year depending on site) (+C-N), and iv) the combined application of mineral N fertilizer and organic amendment (+C+N). Weeds, diseases and pests were controlled using appropriate management to avoid biotic stresses. All experiments were rainfed, Crop aboveground biomass residues were all removed from the fields in each site, thus the only crop residue inputs were from roots.

157 The experiments were extensively described in earlier publications, for Côte d'Ivoire-Gagnoa by Pichot et al. (1977), Guibert (1999) and Cardinael et al. (2022), for the two sites in Kenya by Chivenge et al. (2009), Gentile et al. (2009, 2011) and Laub et al. (2023a, 2023b) and for the site in Zimbabwe by Zingore et al. (2007) and Rusinamhodzi et al. (2013).

161 2.1.2. Meteorological data

162 Weather data (solar radiation, maximum and minimum air temperature, precipitation and relative humidity) over the duration of each experiment were collected from different sources depending on the site. Daily rainfall was recorded using graduated cylinder or tipping bucket rain gauges in all sites
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with some missing years only in Kenya-Embu. Daily temperature (maximum and minimum) was

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recorded with electronic sensing thermometers in Côte d'Ivoire-Gagnoa and Kenya-Machanga in all

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experimental years, but with some missing years in Kenya-Embu; no records were available for

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Zimbabwe-Murewa. Daily incident solar radiation and relative humidity were retrieved from the NASA

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POWER (Prediction Of Worldwide Energy Resource) database for all years and sites

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(https://power.larc.nasa.gov/). Missing daily temperature data (missing years) were gap filled with

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bias-corrected NASA-POWER data. Briefly, for correction of each variable, an equation for a linear

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regression was created using NASA data as the independent variable (x) and observed data as the

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dependent variable (y) at each site. The slope and intercept values obtained from the regression

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equation, y=ax+b, were utilized to generate bias-corrected estimates for the missing

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### 2.1.3. Observations

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experiments. Data on crop phenology were available for two sites (Côte d'Ivoire-Gagnoa and Kenya-

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Embu) while maize cultivar information was provided for Zimbabwe-Murewa and Kenya-Machanga

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(Table 1). Observed data of grain yield was available for all sites while data of total aboveground

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biomass was available for all sites except Zimbabwe-Murewa. Data from SOC measurements were

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available in different years over the length of the experiments (for respectively 4, 5, 8 and 13 years in

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Zimbabwe-Murewa, Côte d'Ivoire-Gagnoa, Kenya-Machanga and Kenya-Embu). The experiment in

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Côte d'Ivoire contained three measurement of soil mineral N (0-20 cm, ...) that were done before the

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start of the first growing season in three different years.

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### 2.2 Model overview

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An ensemble of 16 process-based soil-crop models was used in this study (Table 2). Models differed in

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the way they simulate soil and crop processes. While the simulation of SOC dynamics is principally

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based on first-order decay kinetics for mass loss of SOM pools, models differed in the number and

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190 nature of these pools. Some models have multiple SOM pools (e.g., CENTURY in DSSAT differentiate between an active, slow and passive SOM pool), while other models have a single SOM pool (e.g., CELCIUS). Ten of the 16 models include a soil microbial biomass pool that follow first-order kinetic decay. The representation of fresh OM pools (from above- and belowground crop residues, organic amendments, such as manure and compost) is the most complex in SIMPLACE (based on the SoilCN, Corbeels et al. (2005), Molina et al. (1983)), in which four pools are represented (subdivided in a metabolic, holo-cellulosic, ligno-cellulosic and woody litter pool), while half of the other models have only a single fresh OM pool. All models have a soil N cycle coupled to the (S)OM pool(s). Multi-pool SOC models usually have pool-specific decay coefficients that govern the decomposition rates of each individual pool, leading to a highly non-linear behavior of the entire system (e.g. Caruso et al., 2018). The soil-crop models also differ in the representation of other soil processes (e.g., soil water dynamics, nitrate leaching) and of crop processes (e.g. leaf area development, light interception, above- and belowground biomass accumulation and grain formation) (see Falconnier et al. (2020) for more details on the differences in the simulation of crop processes between the models).

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## 2.3 Calibration procedure

205 The models were calibrated in two phases, namely a blind and a full calibration phase, following a similar approach as other multi-model comparisons within the Agricultural Model Intercomparison and Improvement Project (AgMIP) (Rosenzweig et al., 2013). For the blind calibration, minimum input values (measured or estimated) required to execute the model were provided, i.e. general soil characteristics, initial soil conditions, weather, soil and crop management, and observed dates of emergence, flowering and physiological maturity of the maize crop (Table 1). Model parameter adjustment was limited to fitting maize development stages to observed emergence, anthesis and maturity. The modelling teams could use their own method for initialization of the SOM pools (use initial SOC value, combined with spin-up runs - i.e. running the model iteratively for thousands of years to approximate the steady state solution - or with empirical functions between SOM pools and soil texture). For the full calibration, additional observed crop and soil variables were provided to the
216 modelling teams, i.e. grain yield, total aboveground biomass at harvest, and SOC values (0-15cm for 1
217 Kenya-Embu and Kenya-Machanga and 0-20cm for Côte d'Ivoire-Gagnoa and Zimbabwe- Murewa) at 3
218 various points in time, depending on the site (Table 1). The modeling teams were free to adjust any 4
219 model parameter they found relevant to improve the match between observed data and simulations, 5
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221 2.4 Data analysis

222 2.4.1 Model performance assessment

223 We assessed the accuracy of each individual model and the multi-model ensemble in reproducing crop yield and SOC levels by using the root mean square error (RMSE) and normalised RMSE (nRMSE):

225 $RMSE = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(O_i - S_{i,m})^2}$

226 $nRMSE = \frac{RMSE}{\bar{O}} \times 100$

227 where Oᵢ and Sᵢ,m are the observed and simulated values (for model m) for the nᵗʰ measurement, n is the number of observations and Ŷ is the mean of the observed values.

229 The Nash–Sutcliffe modeling efficiency (EF) was also used to assess the accuracy of multi-model ensemble in reproducing crop yield and SOC levels as follows:

231 $EF = \frac{\sum_{i=1}^{n}(O_i - \bar{O})^2 - \sum_{i=1}^{n}(O_i - S_i)^2}{\sum_{i=1}^{n}(O_i - \bar{O})^2}$

232 where Oᵢ is the observed values of the nᵗʰ measurement, Ŷ is the mean of the observed values and Sᵢ is the simulated value corresponding to Oᵢ.

234 Uncertainty in multi-model ensemble simulations was assessed using the coefficient of variation (CV) of the 16 model simulations for yield and SOC:

236 $CV_s (\%) = \frac{\sigma_s}{S_s} \times 100$
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where $\sigma_s$ is the standard deviation of the simulated values among models at site s and S is the mean
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## 2.4.2 Evaluation of soil-crop feedbacks

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The relationships between i) maize yield or SOC vs time from the start of the experiment, ii) N mineralized and SOC; and (ii) SOC nRMSE and the number of SOM pools were evaluated with Pearson correlation coefficients, using the cor.test function of the R Stats package (R core team, 2023). For all statistical tests, the significance level, $\alpha$, was set to 0.05.

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# Results

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## 3.1 Accuracy of multi-model ensemble: blind vs full calibration

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In the blind calibration phase, the multi-model ensemble strongly overestimated average maize yields across all years and experimental treatments of the four experiments (median ensemble nRMSE of 48%, Figure 1.A), whilst final SOC stocks of the treatments in the four experiments were simulated with acceptable accuracy (median ensemble nRMSE of 22%, Figure 1.B). Full calibration substantially improved the ability of the multi-model ensemble to reproduce average maize yields and final SOC stocks of the different treatments in the different sites (Figure 1). The increase in model accuracy following full calibration was stronger for the simulations of average maize yields than for SOC simulations, with a reduction of nRMSE by 30 and 8%, respectively (Figure 1). The multi-model ensemble failed, however, to adequately reproduce interannual variability of maize yields (Figure 2, Figure S1, Figure S2) in the blind (R²=0.35) and full (R²=0.47) calibration phases. The evolutions of SOC stocks over time were generally well reproduced for the experimental treatments of the four experiments in both the blind (R²=0.96) and full (R²=0.98) calibration phases (Figure 3, Figure S1, Figure S3). The accuracy of the multi-model ensemble in simulating interannual yield variability was highest in Côte d'Ivoire-Gagnoa where both low and high yields were accurately reproduced for the four experimental treatments in the blind and full calibration phases (Figure 2, Figure S1, Figure S2).

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263 3.2 Accuracy of individual models vs multi-model ensemble

1 264 Larger differences in accuracy between individual models occurred in the blind calibration phase
2 3 (nRMSE ranging from 42 to 150%) compared to the full calibration phase (nRMSE ranging from 17 to
4 5 6 7 265 76%) for the simulations of average maize yield across all sites and treatments (Figure 4). Individual
8 9 266 models also simulated final SOC stocks with larger differences in accuracy in the blind calibration phase
10 11 268 (nRMSE ranging from 17 to 90%) compared to the full calibration phase (nRMSE ranging from 9 to 39%,
12 13 269 with exclusion of one model whose accuracy did not change between the blind and the full calibration)
14 15 16 270 (Figure 4). Only DayCent was more accurate than the median multi-model ensemble for the simulation
15 16 271 of average yields after full calibration (Figure 4). Six models (ST, CE, DN, DS1, DC and EI) were more
17 18 272 accurate than the median multi-model ensemble in simulating the final SOC stocks after full calibration
19 20 21 22 23 273 (Figure 4). Only DayCent had better accuracy than the median multi-model ensemble for both
20 21 22 23 24 25 274 simulated average maize yields and final SOC stocks.

25 26 27 28 275 The ranking of individual models differed between the blind and full calibration phases (Figure 4). In
26 27 28 29 30 the blind calibration phase, model ranking for average maize yield and final SOC stocks were different;
31 32 276 some models were among the best to reproduce final SOC stocks but among the worst to reproduce
33 34 277 average yields (e.g., EI and EC). Conversely, some models were among the best to reproduce average
35 36 278 yields but among the worst to reproduce final SOC stocks (e.g., DS2 and DN). However, with full
36 37 279 calibration, model ranking for averaged yield and final SOC stocks were similar. In other words, models
38 39 280 that performed best to reproduce final SOC stocks were also performing best for average yields (e.g.,
40 41 281 ST, CE, DN).
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3.3 Multi-model ensemble drift over the long run

46 47 48 283 48 49 284 With full calibration, the accuracy of the multi-model ensemble (expressed as RMSE) decreased over
time for the SOC simulations, and at the same time uncertainty (expressed as CV across models)
50 51 52 53 285 increased (Figure 5.C and 5.D). On the other hand, no clear trend could be observed for the maize yield
54 55 286 simulations (Figure 5.A and Figure 5.B). In fact, the evolution of model accuracy over time was not
55 56 287 treatment-specific for yield simulations, except for the -C+N treatment in Kenya-Embu where yields
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were increasingly overestimated towards the end of the experiment (maximum RMSE of 3.7 t/ha,

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Figure 6.A). It should also be noted that the yields were better reproduced in the no input treatment

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(-C-N) than in any other treatments in all sites, except in Côte d'Ivoire-Gagnoa. The evolution of model

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accuracy over time was also not treatment-specific for the SOC simulations (Figure 6.C). However, all

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treatments showed an increase of uncertainty of the SOC simulations over time in all sites; this increase

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was larger in treatments with addition of C inputs (Figure 6.D).

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### 3.4 Causes of uncertainty in soil-crop feedbacks

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#### SOC-yield feedbacks

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In the no input treatments (-C-N), the observed positively correlated SOC and maize yield declines over

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the experimental period (representing significant negative soil-crop feedbacks) in Côte d'Ivoire-

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Gagnoa and Kenya-Machanga were reproduced by 7 out of 16 and 8 out of 16 models, respectively

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(Figure 7). On the other hand, the absence of significantly correlated losses of SOC and maize yield

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over the experimental period in Kenya-Embu and Zimbabwe-Murewa were reproduced by 15 out of

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16 models. Some models simulated a sharp SOC decline without yield loss in Côte d'Ivoire-Gagnoa (IN,

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EG, EC) and Kenya-Machanga (ES, EG), while others simulated a large increase in yield without SOC

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increase in Kenya-Embu (SI1) and Zimbabwe-Murewa (ES, SI1). None of the models simulated a

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significant negative correlation between yield and SOC.

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#### Soil to crop feedbacks (via N mineralization)

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SOM mineralization, the simulated average amounts of N mineralized per year showed large

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differences between models in all sites, ranging from 10 to 100 kg ha⁻¹ year⁻¹ in Côte d'Ivoire, from 10

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to 260 kg ha⁻¹ year⁻¹ in Kenya-Embu, from 10 to 80 kg ha⁻¹ year⁻¹ in Kenya-Machanga, and from 5 to 70 kg ha⁻¹ year⁻¹ in Zimbabwe-Murewa (Figure 8). Consequently, models simulated largely different annual

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N mineralization rates, from 0.5% to 11% of SON in Côte d'Ivoire, 0.5% to 6% in Kenya-Embu, 0.5% to

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5.5% in Kenya-Machanga, and 0.6% to 4% in Zimbabwe-Murewa (Figure S4).

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Models differed in their simulation of changes in annual N mineralization over time in response to changes in SOC (Figure 8). Some models simulated N mineralization remaining constant with an increase in SOC (AP2, MO), while others simulated an increase in N mineralization with SOC remaining constant (SI2). Among the models that simulated a significant relationship between SOC and mineralized N, large differences were found in the slope of the relationship (resulting in varying mineralization rates). The largest N mineralization rates per ton SOC were simulated by CE (10.6 kg N per C) and DN (8.6 kg N per C) in all sites, compared to other models that simulated N mineralization rates of about 1.2 to 2.5 kg N per t C (ST, EG, ES, EC). Interestingly, the models that simulated SOC loss without a decrease in yield (Figure 7) were also those that simulated a weak relationship between mineralized N and SOC (IN, EG, EC) (Figure 8).

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With addition of organic amendments (+C treatments), simulated amounts of mineralized N were higher, with much greater discrepancies between models than without added C (Figure S5), indicating an important but highly uncertain effect of organic amendments on soil-to-crop feedbacks in the model simulations.

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Crop to soil feedbacks (via root inputs)

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Simulated root biomass increased with N fertilization for most models (Figure 9, Figure S6). As a result, in treatments without organic amendments (C input coming only from the roots), most models simulated a decrease in SOC with a decrease in cumulative roots biomass over the duration of the experiment for all sites. Yet, some other models did not consistently reproduce this effect (AP2, CE, MO, IN, SI1) (Figure 9). Interestingly, models that simulated yield gain without SOC increase (Figure 7) were also those that did not simulate a strong relationship between cumulative root biomass and SOC (SI1, ES) (Figure S6).

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Simulated average amounts of root biomass per year showed large differences between models. In the no-input treatment (-C-N), simulated root biomass varied from 0.2 to 1.5 t ha⁻¹ per year, while in the +N +C treatments it varied from 0.2 to 3.8 t/ha per year (Figure S6). The simulated root:shoot ratios

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342 3.5 Impact of model structure on model accuracy

343 The type and the number of (S)OM pools had no significant effect on model accuracy for simulations of SOC stocks towards the end of the experiments (Figure S8). In particular, the number of SOM pools had no significant effect on model accuracy in simulating SOC under -C treatments, while the number of residue OM pools did not lead to improved model accuracy in simulating SOC under +C treatments (Figure S8). Moreover, the presence of a microbial SOM pool in the models had no significant effect on model accuracy either (Figure S9).

349 Discussion

350 4.1 Importance of local calibration with detailed observations

351 Our study revealed that full model calibration with observed crop yield and SOC data over time improves accuracy and reduces uncertainty of multi-model long-term simulations of average crop yields and final SOC stocks (Figure 1, 2, 3). This has previously been shown in other crop modeling studies using multiple models, although for short-term model simulations (e.g. Asseng et al., 2013; Bassu et al., 2014; Falconnier et al., 2020). The multi-model ensemble failed however to reproduce the observed interannual yield variability throughout the four long-term experiments (Figure S1), as was also reported by other studies in SSA that, however, used, a single soil-crop model (Ma et al., 2022; Nyawira et al., 2021). In contrast, in some other AgMIP crop modelling studies (Bassu et al., 2014; Falconnier et al., 2020; Li et al., 2015), multi-model ensembles simulated accurately interannual crop yield variability in short-term experiments. This was, however, possible because the full model calibration was done using more detailed data of dynamics of leaf area index (LAI), soil water, soil mineral N and plant N over the growing seasons of the experiments. In particular, the utilization of observed LAI and soil moisture data is extremely valuable when calibrating models, as it enables the model to accurately reproduce yield variations resulting from changes in seasonal rainfall patterns.
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Therefore, in long term experiments, model calibration based on only a few experimental years with detailed observation would enable a better model accuracy than calibration on many years with a low level of information.

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## 4.2 The more soil and crop processes to account for, the higher the uncertainty drift over time

In our study the accuracy of the multi-model ensemble in simulating SOC decreased over time, but that of average crop yield predictions remained constant (Figure 5). This outcome contradicts our initial hypothesis, which suggested that yield predictions would become poorer towards the end of the experiment due to accumulation of errors over time (including those associated with soil-crop feedbacks). It should however be noted that modelling groups may have performed their model calibration against observed crop yields throughout all years of the experiment (and not only using yields from e.g. year 1), masking hereby accuracy drifts over time.

The model ensemble uncertainty in simulating SOC increased over time, especially in the treatments with organic amendments (Figure 6). This can be attributed to the uncertainties in simulating N immobilization-mineralization-processes during decomposition of the organic amendments. For example, in our study we found no impact of the number of residue OM or SOM pools on model accuracy. Cavalli et al. (2019) showed that only a few model parameters, such as those related to substrate C use efficiency, explained most of the accuracy of the models (APSIM, EPIC, FASSET and STICS) in simulating the effect of manure on soil C and N dynamics. Levavasseur et al. (2021) compared two STICS versions that included either one or two residue OM pools, and showed that the one-pool

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391 model simulated soil C and N dynamics as accurately as the two-pool model. Our study confirms that
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decrease in root biomass) and SOC-to-crop feedbacks (a decrease in N mineralization with a decrease in SOC) exhibited the best ability to reproduce the observed SOC and yield trends over the duration of the experiments (Figure 4 and 7). Yet, soil C and N feedbacks on crops have often been overlooked in long-term modelling studies where soil properties were often reset in the models each year (e.g Bassu et al., 2014; Falconnier et al., 2020).

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The simulated N mineralization rates varied considerably among the models (from about 1 to 10 fold) (Figure S4). Some models simulated annual mineralization rates of SON close to and beyond the maximum value of 5% reported in the literature (Bationo et al., 2007; Masvaya et al., 2017; Wetselaar and Ganry, 1982). The high uncertainty in simulated amounts of N mineralized lead to uncertainties in simulated soil mineral N, nitrate leaching and ultimately plant N uptake. For example, in our study most models simulated a strong accumulation of soil mineral N over the years in the fertilized treatments (data not shown). This could have masked feedbacks between SOC and yields (high soil mineral N leads to a weak influence of N mineralization on crop growth and yield). Yet, an accurate prediction of N mineralization rates and a better understanding of the reasons behind the weak correlation between changes in SOC and changes in the amounts of N mineralized in some models are a prerequisite for improved model simulations, especially in low-input cropping systems. From our study, it is evident that more soil mineral N data need to be collected in field experiments in SSA (and elsewhere) for model calibration. The calibration of models for soil N dynamics would be greatly beneficial in better reproducing yield responses to mineral and/or organic fertilizers and better understanding long-term soil-crop feedback mechanisms.

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In low-input cropping systems, the carry-over of root residues from one cropping season to the other is important to quantify because of its critical influence on SOC and soil mineral N dynamics. The root biomass simulated by the models was also subject to large uncertainties (Figure 9 and S6). Some models simulated low root biomass production resulting in very low root:shoot ratios (Figure S7), i.e. below the likely values of 0.08 to 0.43 for maize reported in the literature (Anderson, 1988; Eghball
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and Maranville, 1993; Lopez et al., 2023; Ordóñez et al., 2020). Moreover, model differences in

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simulating root biomass increased with mineral and/or organic fertilizer application (Figure S6).

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Experimental research found either an increase (Eghball and Maranville, 1993), no clear effects

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(Anderson, 1988, 1987) or a decrease (Ordóñez et al., 2021) in root biomass with mineral fertilization.

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Yet, most studies agreed that fertilization tends to cause a decline in the root:shoot ratio (Anderson,

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1988, 1987; Eghball and Maranville, 1993; Lopez et al., 2023), which was, however, not reproduced

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by the models (Figure S7). This outcome calls for more experiments aimed at better understanding

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the effects of fertilization on root dynamics in SSA. Given the large spread between models, such root

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biomass measurements, would prove valuable for improving model formalisms that describe the

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processes and factors of root development. This would in turn allow for a better assessment of root C

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inputs and their contribution to SOC formation (Whitbread et al., 2003). Moreover, more experimental

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research is needed to quantify the effects of roots on SOC dynamics, as roots can stabilize or destabilize

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SOC (Dijkstra et al., 2021).

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Specific experiments in SSA are also needed to unravel the effects of organic amendment from the

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effects of crop residues and SOM turnover on soil-crop feedbacks. Bare soil experiments without C

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inputs, that measure soil C and N, are valuable for calibrating SOM turnover in models. Indeed, with

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bare soils, it is possible to evaluate model performance without crop C inputs and without

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interferences arising from crop water use and N uptake. Such experiments have for example been

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conducted in Australia and enabled to calibrate the SoilN model of APSIM (Dalal et al., 1995; Dimes,

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1996; Probert et al., 1998). On the other hand, long-term experiments with organic inputs on bare soil

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can help to calibrate the residue OM pools associated with the organic amendment, if they are

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established in a way that avoids erosion. Such long-term experiments have for example been

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conducted in Sweden and were used to calibrate the CENTURY model (Gerzabek et al., 1997; Paustian

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et al., 1992). Comparisons of soil C data between bare and cropped soils, and model simulations, can

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then further help to assess the effects of crops on SOC dynamics.

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Besides the data needed for calibrating SOM turnover formalisms already present in models, model improvements are needed to better reproduce soil-crop feedbacks. Soil C and N fluxes remain difficult to simulate due to incomplete representation of key functions/mechanisms in the models. Campbell and Paustian (2015) highlight the need of advances in SOC modelling such as the implementation of microbial pool that plays a role in SOM stabilization, the modelling of SOM saturation kinetics, the impact of temperature on SOM decomposition and deep soil layers SOM dynamics. Other processes such as rhizosphere-SOM interactions such as rhizodeposition and rhizosphere priming effect are mostly not represented in models (Cavalli et al., 2019) while it has been reported that rhizosphere priming effect can offset the SOC increase effect of roots (Dijkstra et al., 2021). Moreover, soils can be unresponsive to fertilization due to limiting non-N-P-K nutrient and/or physical obstruction to crop growth (Vanlauwe et al., 2015a, 2010). Those processes are not represented in most models. More specifically, effects of fertilizers on pH are poorly represented in models. It could, for example, explain model inaccuracy in simulating +N treatments in Embu where observed yield was limited by an observed pH decrease over the years (Laub et al., 2023a).

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## Conclusion

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We conducted the first multi-model evaluation for long-term simulations of crop (maize) yields and SOC trends and their feedbacks in SSA. The models differed considerably in their ability to reproduce such trends, with increased uncertainty in reproducing yields without inputs and in reproducing SOC with the addition of organic amendments. Models exhibited large differences in simulating i) soil to crop feedbacks through different simulated N mineralized from SOM and ii) soil to crop feedback through different simulated root biomass. Models that accounted for such feedbacks showed the best accuracy. As a result, we argue for an urgent need for detailed experimental data on N dynamics and roots in SSA, and probably elsewhere in the world. Such experimental efforts are critical to ensure that models accurately incorporate soil-crop feedbacks and that they are suitable for use in SSA.

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## Acknowledgments
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We are grateful to the support provided by the AgMIP leadership team. Part of the study was funded

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by the DSCATT project "Agricultural Intensification and Dynamics of Soil Carbon Sequestration in

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Tropical and Temperate Farming Systems" (N° AF 1802-001, N° FT C002181), supported by the

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Agropolis Foundation ("Programme d' Investissement d' Avenir" Labex Agro, ANR-10-LABX0001-01)

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and by the TOTAL Foundation within a patronage agreement.

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Competing interests

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Authors have no competing interests to declare

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Data Availability

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Data Availability Data will be made available on request.

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Tables and Figures

<table><caption>Table 1. Long term experiments characteristics and observed data available for model calibration</caption><thead><tr><th></th><th></th><th colspan="4">Site</th></tr></thead><tbody><tr><td rowspan="5">General information</td><td>Country</td><td>Côte d'Ivoire</td><td>Kenya</td><td>Kenya</td><td>Zimbabwe</td></tr><tr><td>Location</td><td>Gagnoa</td><td>Embu</td><td>Machanga</td><td>Murewa</td></tr><tr><td>Latitude</td><td>6.13</td><td>-0.52</td><td>-0.79</td><td>-17.81</td></tr><tr><td>Longitude</td><td>-5.93</td><td>37.46</td><td>37.66</td><td>31.56</td></tr><tr><td>Elevation</td><td>213</td><td>1380</td><td>1022</td><td>1262</td></tr><tr><td rowspan="4">Climate</td><td>Average annual rainfall (mm)*</td><td>1402</td><td>1175</td><td>795</td><td>898</td></tr><tr><td>Mean annual temperature (°C)*</td><td>25.5</td><td>20.1</td><td>23.7</td><td>20.9</td></tr><tr><td>FAO Agro-ecological zone</td><td>warm-humid</td><td>cool sub-humid</td><td>warm semi-arid</td><td>cool-semi arid</td></tr><tr><td>Rainfall pattern</td><td>bimodal</td><td>bimodal</td><td>bimodal</td><td>unimodal</td></tr><tr><td rowspan="4">Soil</td><td>Type (FAO)</td><td>Lixisols</td><td>Humic Nitisols</td><td>Ferric Alisols</td><td>Lixisols</td></tr><tr><td>Texture</td><td>sandy clay loam</td><td>clay</td><td>sandy clay loam</td><td>sandy</td></tr><tr><td>initial SOC (%) (0-20cm)</td><td>1.1</td><td>2.9</td><td>0.3</td><td>0.5</td></tr><tr><td>initial SON (%) (0-20cm)</td><td>0.11</td><td>0.3</td><td>0.02</td><td>0.04</td></tr><tr><td rowspan="10">Management</td><td>Experiment duration (years)</td><td>13</td><td>18</td><td>10</td><td>9</td></tr><tr><td>First crop cycle</td><td>maize</td><td>maize</td><td>maize</td><td>maize</td></tr><tr><td>Second crop cycle</td><td>maize or soybean or fallow</td><td>maize</td><td>maize</td><td>none</td></tr><tr><td>Maize Cultivar</td><td>Hybrid (medium maturity, variety H507 and IRAT83)</td><td>Hybrid (early maturity drought tolerant, variety H513)</td><td>Hybrid (early maturity drought tolerant, variety Katumani)</td><td>Hybrid (Medium maturity, drought tolerant, variety SC525)</td></tr><tr><td>mineral P and K fertilisation (kg/ha/cycle)</td><td>100 P + 150 K</td><td>60 P + 60 K</td><td>60 P + 60 K</td><td>/</td></tr><tr><td>Treatments</td><td></td><td></td><td></td><td></td></tr><tr><td>1- No N input</td><td>0 N</td><td>0 N</td><td>0 N</td><td>0 N</td></tr></tbody></table>

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<table><tbody><tr><td></td><td>2- Mineral N fertiliser (kg/ha/cycle)</td><td>160 N</td><td>120 N</td><td>120 N</td><td>100 N + 30 P</td></tr><tr><td></td><td>3- Organic fertiliser (/ha/year)**</td><td>3 tC/ha compost of crop residues</td><td>4 tC/ha manure</td><td>4 tC/ha manure</td><td>none</td></tr><tr><td></td><td>4- Mineral N fertiliser (kg/ha/cycle) + Organic fertiliser (/ha/year)**</td><td>160 N + 100 P + 150 K + 3 tC/ha compost of crop residues</td><td>120 N + 60 P + 60 K + 4 tC/ha manure</td><td>120 N + 60 P + 60 K + 4 tC/ha manure</td><td>100 N + 3 tC/ha manure</td></tr><tr><td></td><td>Organic fertilizer composition</td><td>29.3%C, 2.0%N, 1.0%P, 5.9%K</td><td>28.4%C, 2.4%N, 0.4%P, 3.6%K</td><td>28.4%C, 2.4%N, 0.4%P, 3.6%K</td><td>20%C, 1.1%N, 0.2%P, 0,6%K</td></tr><tr><td rowspan="4">Obsverved data (blind phase)</td><td>Emergence date</td><td>few years</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Anthesis date</td><td>few years</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Physiological maturity date</td><td>NA</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Harvest date</td><td>All years</td><td>All years</td><td>All years</td><td>All years</td></tr><tr><td rowspan="4">Obsverved data (full phase)</td><td>Harvested grain yield (Dry Matter)</td><td>All years</td><td>All years</td><td>All years</td><td>All years</td></tr><tr><td>Total above ground biomass (Dry Matter)</td><td>All years</td><td>NA</td><td>All years</td><td>All years</td></tr><tr><td>Soil organic C &amp; N (from 5 to 8 points depending on sites)</td><td>5 observations</td><td>13 observations</td><td>5 observations</td><td>4 observations</td></tr><tr><td>Soil mineral N (one point before sowing for 3 years)</td><td>3 observations</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>

* Means calculated over the length of the long term experiment

** tC/ha/year in dry matter
<table><caption>Table 2. The 16 soil-crop process-based models used in the model intercomparison study.</caption><thead><tr><th>Model</th><th>Abbreviation</th><th>Total C pools</th><th>Residues C pools</th><th>Microbial C pools</th><th>SOM pools</th><th>Soil sub-model</th><th>Crop sub-model</th><th>Model reference</th></tr></thead><tbody><tr><td>29 APSIM</td><td>AP2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>APSIM</td><td>Holzworth et al. (2014)</td></tr><tr><td>30 CELCIUS</td><td>CE</td><td>2</td><td>1</td><td>0</td><td>1</td><td>CELCIUS</td><td>CELCIUS</td><td>Ricome et al. (2017)</td></tr><tr><td>31 DayCent</td><td>DC</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>DayCent</td><td>Del Grosso et al. (2001)</td></tr><tr><td>32 DNDC</td><td>DN</td><td>8</td><td>3</td><td>2</td><td>3</td><td>DNDC</td><td>DNDC</td><td>Smith et al. (2020)</td></tr><tr><td>34 DSSAT-CERES-Maize + Century</td><td>DS1</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>CERES maize</td><td>Ritchie et al. (1998); Gijsman et al. (2002)</td></tr><tr><td>36 DSSAT-CERES-Maize+Ceres-SOM</td><td>DS2</td><td>2</td><td>1</td><td>0</td><td>1</td><td>CERES</td><td>CERES maize</td><td>Godwin and Jones (1991)</td></tr><tr><td>37 EPIC</td><td>EI</td><td>5</td><td>2</td><td>0</td><td>3</td><td>EPIC</td><td>EPIC</td><td>Izaurralde et al. (2006)</td></tr><tr><td>39 Expert-N_v5.1-Gecros</td><td>EG</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>similar to SUCROS</td><td>Biernath et al. (2011)</td></tr><tr><td>40 Expert-N_v5.1-Spass</td><td>ES</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>SPASS</td><td>Biernath et al. (2011)</td></tr><tr><td>41 Expert-N_v5.1-Ceres</td><td>EC</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>CERES maize</td><td>Biernath et al. (2011)</td></tr><tr><td>43 InfoCrop</td><td>IN</td><td>3</td><td>3</td><td>1</td><td>1</td><td>InfoCrop</td><td>similar to SUCROS</td><td>Aggarwal et al. (2006)</td></tr><tr><td>44 Monica</td><td>MO</td><td>6</td><td>2</td><td>2</td><td>2</td><td>DAISY</td><td>HERMES/AGROSIM</td><td>Nendel et al. (2011)</td></tr><tr><td>45 SALUS</td><td>SA</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>SALUS</td><td>Basso et al. (2010)</td></tr><tr><td>47 SIMPLACE-Lintul + Option 1</td><td>SI1</td><td>7</td><td>4</td><td>1</td><td>2</td><td>SoilCN</td><td>Lintul</td><td>Enders et al. (2023); Gaiser et al. (2013)</td></tr><tr><td>49 SIMPLACE-Lintul + Option 2</td><td>SI2</td><td>7</td><td>4</td><td>1</td><td>2</td><td>SoilCN</td><td>Lintul</td><td>Faye et al. (2018)</td></tr><tr><td>50 STICS</td><td>ST</td><td>4</td><td>1</td><td>1</td><td>2</td><td>STICS</td><td>STICS</td><td>Brisson et al. (2003); Beaudoin et al. (2023)</td></tr></tbody></table>
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

Figure 1. Observed and simulated average maize yield across all the years of the long-term experiments (A) and soil organic carbon at the end of the long-term experiment (B) for blind and fully calibrated models, at four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. The simulations are the median of an ensemble of 16 soil-crop models. Treatments are without inputs (-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N). nRMSE is the normalized Root Mean Square Error while EF is the Model Efficiency.

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<figure><img src="image_5.png" /><figcaption>Figure 2. Observed (red dots) and simulated maize yield across long-term experiments for fully calibrated models, in four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. Black solid lines are medians of the simulations. Dark gray areas indicate the 25th to 75th percentile range of the values simulated. Treatments are without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N).</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 3 Observed (red dots) and simulated relative soil organic carbon from the start of long-term experiments (100 basis) for fully calibrated models, in four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. Black solid lines are medians of the simulations. Dark gray areas indicate the 25th to 75th percentile range of the values simulated. Treatments are without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N)</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 4. Normalized root mean square error (nRMSE) for yield and soil organic carbon simulations by individual models and ensembles created from the mean (e.mean) and median (e.median) across all models for blind and full calibration. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>

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<figure><img src="image_8.png" /><figcaption>Figure 5. Yield (A) and SOC (C) RMSE and Yield (B) and SOC (D) coefficient of variation for each experimental year for fully calibrated models, in four sites of sub-Saharan Africa, across all treatments. RMSE was computed from the median of an ensemble of 16 soil-crop models while coefficient of variation was based on the 16 models' simulations.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Figure 6. Yield (A) and SOC (C) RMSE and Yield (B) and SOC (D) coefficient of variation for each experimental year for fully calibrated models, in four sites of sub-Saharan Africa. Treatments were without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N). RMSE was computed from the median of an ensemble of 16 soil-crop models while coefficient of variation was computed from the 16 models individual simulations.</figcaption></figure>

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<figure><img src="image_10.png" /><figcaption>Figure 7. Simulated (dots) and observed (triangle) yearly change in yield and soil organic carbon over the duration of long-term experiments in four sites of sub-Saharan Africa for no input (-C-N) treatment. Colors correspond to simulations or observations where SOC and yield change were conjointly significantly different from 0 (in blue) or not (in red). Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SII (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Figure 8. Simulated SOC and cumulated N mineralised per year by individual models in four sites of sub-Saharan Africa for no input treatments only. Each point corresponds to one year of simulation. Only significant regression lines at p<0.05 are displayed. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS). 12 of the 16 models provided N mineralized.</figcaption></figure>

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<figure><img src="image_12.png" /><figcaption>Figure 9. Simulated SOC change from the start of long-term experiments (100 basis) and cumulated roots biomass produced during each crop cycle (t/ha) over the length of the experiment in four sites of sub-Saharan Africa for no input (-C-N) and N fertilized (-C+N) treatments. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>Figures</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Figure 1. Observed and simulated average maize yield across all the years of the long-term experiments (A) and soil organic carbon at the end of the long-term experiment (B) for blind and fully calibrated models, at four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. The simulations are the median of an ensemble of 16 soil-crop models. Treatments are without inputs (-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N). nRMSE is the normalized Root Mean Square Error while EF is the Model Efficiency.</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure 2. Observed (red dots) and simulated maize yield across long-term experiments for fully calibrated models, in four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. Black solid lines are medians of the simulations. Dark gray areas indicate the 25th to 75th percentile range of the values simulated. Treatments are without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N).</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Figure 3 Observed (red dots) and simulated relative soil organic carbon from the start of long-term experiments (100 basis) for fully calibrated models, in four sites of sub-Saharan Africa, for four N fertilizer and organic treatments. Black solid lines are medians of the simulations. Dark gray areas indicate the 25th to 75th percentile range of the values simulated. Treatments are without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N)</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Figure 4. Normalized root mean square error (nRMSE) for yield and soil organic carbon simulations by individual models and ensembles created from the mean (e.mean) and median (e.median) across all models for blind and full calibration. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Figure 5. Yield (A) and SOC (C) RMSE and Yield (B) and SOC (D) coefficient of variation for each experimental year for fully calibrated models, in four sites of sub-Saharan Africa, across all treatments. RMSE was computed from the median of an ensemble of 16 soil-crop models while coefficient of variation was based on the 16 models' simulations.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>Figure 6. Yield (A) and SOC (C) RMSE and Yield (B) and SOC (D) coefficient of variation for each experimental year for fully calibrated models, in four sites of sub-Saharan Africa. Treatments were without inputs (-C-N), with mineral N inputs only (-C+N), with C inputs only (+C-N) and with combined C and N inputs (+C+N). RMSE was computed from the median of an ensemble of 16 soil-crop models while coefficient of variation was computed from the 16 models individual simulations.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>Figure 7. Simulated (dots) and observed (triangle) yearly change in yield and soil organic carbon over the duration of long-term experiments in four sites of sub-Saharan Africa for no input (-C-N) treatment. Colors correspond to simulations or observations where SOC and yield change were conjointly significantly different from 0 (in blue) or not (in red). Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SII (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>Figure 8. Simulated SOC and cumulated N mineralised per year by individual models in four sites of sub-Saharan Africa for no input treatments only. Each point corresponds to one year of simulation. Only significant regression lines at p<0.05 are displayed. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS). 12 of the 16 models provided N mineralized.</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>Figure 9. Simulated SOC change from the start of long-term experiments (100 basis) and cumulated roots biomass produced during each crop cycle (t/ha) over the length of the experiment in four sites of sub-Saharan Africa for no input (-C-N) and N fertilized (-C+N) treatments. Model abbreviations mean AP2 (APSIM), CE (CELCIUS), DS1 (DSSAT-Century), DS2 (DSSAT-Ceres), DC (DayCent), DN (DNDC), EC (Expert-N-Ceres), EG (Expert-N-Gecros), EI (EPIC), ES (Expert-N-Spass), IN (InfoCrop), MO (MONICA), SI1 (SIMPLACE-1), SI2 (SIMPLACE-2), SA (SALUS), ST (STICS).</figcaption></figure>
Tables and Figures

<table><caption>Table 1. Long term experiments characteristics and observed data available for model calibration</caption><thead><tr><th></th><th></th><th></th><th></th><th></th><th></th></tr></thead><tbody><tr><td></td><td>Site</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="5">General information</td><td>Country</td><td>Côte d'Ivoire</td><td>Kenya</td><td>Kenya</td><td>Zimbabwe</td></tr><tr><td>Location</td><td>Gagnoa</td><td>Embu</td><td>Machanga</td><td>Murewa</td></tr><tr><td>Latitude</td><td>6.13</td><td>-0.52</td><td>-0.79</td><td>-17.81</td></tr><tr><td>Longitude</td><td>-5.93</td><td>37.46</td><td>37.66</td><td>31.56</td></tr><tr><td>Elevation</td><td>213</td><td>1380</td><td>1022</td><td>1262</td></tr><tr><td rowspan="4">Climate</td><td>Average annual rainfall (mm)*</td><td>1402</td><td>1175</td><td>795</td><td>898</td></tr><tr><td>Mean annual temperature (°C)*</td><td>25.5</td><td>20.1</td><td>23.7</td><td>20.9</td></tr><tr><td>FAO Agro-ecological zone</td><td>warm-humid</td><td>cool sub-humid</td><td>warm semi-arid</td><td>cool-semi arid</td></tr><tr><td>Rainfall pattern</td><td>bimodal</td><td>bimodal</td><td>bimodal</td><td>unimodal</td></tr><tr><td rowspan="4">Soil</td><td>Type (FAO)</td><td>Lixisols</td><td>Humic Nitisols</td><td>Ferric Alisols</td><td>Lixisols</td></tr><tr><td>Texture</td><td>sandy clay loam</td><td>clay</td><td>sandy clay loam</td><td>sandy</td></tr><tr><td>initial SOC (%) (0-20cm)</td><td>1.1</td><td>2.9</td><td>0.3</td><td>0.5</td></tr><tr><td>initial SON (%) (0-20cm)</td><td>0.11</td><td>0.3</td><td>0.02</td><td>0.04</td></tr><tr><td rowspan="10">Management</td><td>Experiment duration (years)</td><td>13</td><td>18</td><td>10</td><td>9</td></tr><tr><td>First crop cycle</td><td>maize</td><td>maize</td><td>maize</td><td>maize</td></tr><tr><td>Second crop cycle</td><td>maize or soybean or fallow</td><td>maize</td><td>maize</td><td>none</td></tr><tr><td>Maize Cultivar</td><td>Hybrid (medium maturity, variety H507 and IRAT83)</td><td>Hybrid (early maturity drought tolerant, variety H513)</td><td>Hybrid (early maturity drought tolerant, variety Katumani)</td><td>Hybrid (Medium maturity, drought tolerant, variety SC525)</td></tr><tr><td>mineral P and K fertilisation (kg/ha/cycle)</td><td>100 P + 150 K</td><td>60 P + 60 K</td><td>60 P + 60 K</td><td>/</td></tr><tr><td>Treatments</td><td></td><td></td><td></td><td></td></tr><tr><td>1- No N input</td><td>0 N</td><td>0 N</td><td>0 N</td><td>0 N</td></tr></tbody></table>
<table><tbody><tr><td></td><td>2- Mineral N fertiliser (kg/ha/cycle)</td><td>160 N</td><td>120 N</td><td>120 N</td><td>100 N + 30 P</td></tr><tr><td></td><td>3- Organic fertiliser (/ha/year)**</td><td>3 tC/ha compost of crop residues</td><td>4 tC/ha manure</td><td>4 tC/ha manure</td><td>none</td></tr><tr><td></td><td>4- Mineral N fertiliser (kg/ha/cycle) + Organic fertiliser (/ha/year)**</td><td>160 N + 100 P + 150 K + 3 tC/ha compost of crop residues</td><td>120 N + 60 P + 60 K + 4 tC/ha manure</td><td>120 N + 60 P + 60 K + 4 tC/ha manure</td><td>100 N + 3 tC/ha manure</td></tr><tr><td></td><td>Organic fertilizer composition</td><td>29.3%C, 2.0%N, 1.0%P, 5.9%K</td><td>28.4%C, 2.4%N, 0.4%P, 3.6%K</td><td>28.4%C, 2.4%N, 0.4%P, 3.6%K</td><td>20%C, 1.1%N, 0.2%P, 0,6%K</td></tr><tr><td rowspan="4">Observed data (blind phase)</td><td>Emergence date</td><td>few years</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Anthesis date</td><td>few years</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Physiological maturity date</td><td>NA</td><td>for 3 crop cycles</td><td>for 3 crop cycles</td><td>NA</td></tr><tr><td>Harvest date</td><td>All years</td><td>All years</td><td>All years</td><td>All years</td></tr><tr><td rowspan="4">Observed data (full phase)</td><td>Harvested grain yield (Dry Matter)</td><td>All years</td><td>All years</td><td>All years</td><td>All years</td></tr><tr><td>Total above ground biomass (Dry Matter)</td><td>All years</td><td>NA</td><td>All years</td><td>All years</td></tr><tr><td>Soil organic C &amp; N (from 5 to 8 points depending on sites)</td><td>5 observations</td><td>13 observations</td><td>5 observations</td><td>4 observations</td></tr><tr><td>Soil mineral N (one point before sowing for 3 years)</td><td>3 observations</td><td>NA</td><td>NA</td><td>NA</td></tr></tbody></table>

<footnote>* Means calculated over the length of the long term experiment</footnote>

<footnote>** tC/ha/year in dry matter</footnote>
<table><caption>Table 2. The 16 soil-crop process-based models used in the model intercomparison study.</caption><thead><tr><th>Model</th><th>Abbreviation</th><th>Total C pools</th><th>Residues C pools</th><th>Microbial C pools</th><th>SOM pools</th><th>Soil sub-model</th><th>Crop sub-model</th><th>Model reference</th></tr></thead><tbody><tr><td>APSIM</td><td>AP2</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>APSIM</td><td>Holzworth et al. (2014)</td></tr><tr><td>CELCIUS</td><td>CE</td><td>2</td><td>1</td><td>0</td><td>1</td><td>CELCIUS</td><td>CELCIUS</td><td>Ricome et al. (2017)</td></tr><tr><td>DayCent</td><td>DC</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>DayCent</td><td>Del Grosso et al. (2001)</td></tr><tr><td>DNDC</td><td>DN</td><td>8</td><td>3</td><td>2</td><td>3</td><td>DNDC</td><td>DNDC</td><td>Smith et al. (2020)</td></tr><tr><td>DSSAT-CERES-Maize + Century</td><td>DS1</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>CERES maize</td><td>Ritchie et al. (1998); Gijsman et al. (2002)</td></tr><tr><td>DSSAT-CERES-Maize+Ceres-SOM</td><td>DS2</td><td>2</td><td>1</td><td>0</td><td>1</td><td>CERES</td><td>CERES maize</td><td>Godwin and Jones (1991)</td></tr><tr><td>EPIC</td><td>EI</td><td>5</td><td>2</td><td>0</td><td>3</td><td>EPIC</td><td>EPIC</td><td>Izaurralde et al. (2006)</td></tr><tr><td>Expert-N_v5.1-Gecros</td><td>EG</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>similar to SUCROS</td><td>Biernath et al. (2011)</td></tr><tr><td>Expert-N_v5.1-Spass</td><td>ES</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>SPASS</td><td>Biernath et al. (2011)</td></tr><tr><td>Expert-N_v5.1-Ceres</td><td>EC</td><td>3</td><td>1</td><td>1</td><td>1</td><td>SoilN</td><td>CERES maize</td><td>Biernath et al. (2011)</td></tr><tr><td>InfoCrop</td><td>IN</td><td>3</td><td>3</td><td>1</td><td>1</td><td>InfoCrop</td><td>similar to SUCROS</td><td>Aggarwal et al. (2006)</td></tr><tr><td>Monica</td><td>MO</td><td>6</td><td>2</td><td>2</td><td>2</td><td>DAISY</td><td>HERMES/AGROSIM</td><td>Nendel et al. (2011)</td></tr><tr><td>SALUS</td><td>SA</td><td>5</td><td>2</td><td>0</td><td>3</td><td>CENTURY</td><td>SALUS</td><td>Basso et al. (2010)</td></tr><tr><td>SIMPLACE-Lintul + Option 1</td><td>SI1</td><td>7</td><td>4</td><td>1</td><td>2</td><td>SoilCN</td><td>Lintul</td><td>Enders et al. (2023); Gaiser et al. (2013)</td></tr><tr><td>SIMPLACE-Lintul + Option 2</td><td>SI2</td><td>7</td><td>4</td><td>1</td><td>2</td><td>SoilCN</td><td>Lintul</td><td>Faye et al. (2018)</td></tr><tr><td>STICS</td><td>ST</td><td>4</td><td>1</td><td>1</td><td>2</td><td>STICS</td><td>STICS</td><td>Brisson et al. (2003); Beaudoin et al. (2023)</td></tr></tbody></table>