Institut für Bodenkunde und Standortslehre

Permanent URI for this collectionhttps://hohpublica.uni-hohenheim.de/handle/123456789/10

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Proposal and extensive test of a calibration protocol for crop phenology models
(2023) Wallach, Daniel; Palosuo, Taru; Thorburn, Peter; Mielenz, Henrike; Buis, Samuel; Hochman, Zvi; Gourdain, Emmanuelle; Andrianasolo, Fety; Dumont, Benjamin; Ferrise, Roberto; Gaiser, Thomas; Garcia, Cecile; Gayler, Sebastian; Harrison, Matthew; Hiremath, Santosh; Horan, Heidi; Hoogenboom, Gerrit; Jansson, Per-Erik; Jing, Qi; Justes, Eric; Kersebaum, Kurt-Christian; Launay, Marie; Lewan, Elisabet; Liu, Ke; Mequanint, Fasil; Moriondo, Marco; Nendel, Claas; Padovan, Gloria; Qian, Budong; Schütze, Niels; Seserman, Diana-Maria; Shelia, Vakhtang; Souissi, Amir; Specka, Xenia; Srivastava, Amit Kumar; Trombi, Giacomo; Weber, Tobias K. D.; Weihermüller, Lutz; Wöhling, Thomas; Seidel, Sabine J.
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.
Sanitized human urine (Oga) as a fertilizer auto-innovation from women farmers in Niger
(2021) Moussa, Hannatou O.; Nwankwo, Charles I.; Aminou, Ali M.; Stern, David A.; Haussmann, Bettina I. G.; Herrmann, Ludger
Poor soil chemical fertility and climate change restrict pearl millet grain yield in Niger Republic. Apart from the seedball technology, which targets majorly early phosphorus supply to the plants, the recommended practices of mineral fertilization and seed treatments (coating and priming) are barely affordable to the local farmers in particular. In the case of female farmers, who usually have chemically infertile farmlands often located far away from their homestead, low pearl millet grain yield can be exacerbated. In quest for a cheap, affordable, and effective solution, we hypothesized that the application of sanitized human urine (Oga), in combination with organic manure (OM) or solely, increases pearl millet panicle yield in women’s fields and on different local soils. In on-farm large-N trials (N = 681) with women farmers in two regions of Niger (Maradi, Tillabery), pearl millet panicle yields were compared between the control (farmer practice), and a combination of Oga and OM in the first and second year, and Oga alone in the third year. Our results showed an average panicle yield increase of about +30%, representing +200 to +300 kg ha−1. Major factors determining the yield effect are season, village, and local soil type. This study shows for the first time that Oga innovation can be used to increase pearl millet panicle yield particularly in the low fertile soils of women’s farmlands in Niger. Oga innovation is affordable, locally available, and does not pose a risk to resource-poor female farmers of Niger.

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