APSIM – maize model was validated with the experimental data on three maize cultivars (Sammaz 33,... more APSIM – maize model was validated with the experimental data on three maize cultivars (Sammaz 33, Sammaz 37 and Sammaz 27) sown on three different dates during 2015 wet season at the Institute for Agricultural Research Samaru Zaria Nigeria (Lat. 7R” 38’N, Long. 11R” 11’E Lat. 686m). For the testing efficiency of the model performance, R2, RMSE and RMSEn was computed. RMSEn betweenobserved and simulated values by APSIM for grain yield was lowest (5.4%) for Sammaz 33 cultivar as compared to Sammaz 37 (10.5%) and Sammaz 26 (27.6%). Similar results were obtained for the other parameters, with Sammaz 33 out yielding the other two cultivars. The observed values under second sowing date showed better performance of days to flowering, physiological maturity and leaf area index for all the varieties. The grain yield performance were higher under first sowing date. The results led to the conclusion that APSIM model is efficient in simulating maize growth and development in arid environment of...
<p>In rain-fed systems, efficient and timely crop planning is crucial to ma... more <p>In rain-fed systems, efficient and timely crop planning is crucial to maximize crop production, adapt to climate variability, and increase the sustainability and resilience of the production systems. Smallholder farmers plan and anticipate possible interventions during the season based on the actual onset of the monsoon. However, their knowledge to define and predict the monsoon onset is limited to traditional methods whose predictive skill decreases significantly with a recent increase in both temperature and rainfall variability in the region. Therefore, defining the start of the monsoon accurately is a priority for improving crop production in rain-fed systems. Since the 1970s, researchers have produced more than 18 definitions—from local to regional scale—to define the start of the monsoon in the Sahel region which makes it difficult for one to find a suitable definition for a specific application. The present study compared and analyzed the West African Monsoon (WAM) onset according to Raman’s, Stern’s, Yamada’s, and Liebman’s definitions using station data from 13 locations in Senegal i.e. Dakar, Louga, Matam, St. Louis, Thies, Diourbel, Fatick, Kaffrine, Kaolack, Kedougou, Kolda, Tambacounda, and Ziguinchor from 1981 to 2020. To this end, we applied machine learning algorithms—<em>K-means clustering and Decision Tree</em>—to cluster the Sea Surface Temperature anomalies (SSTa) obtained from different regions of the Mediterranean and the Atlantic Ocean. We then used the clusters in the decision tree model to predict the onset and intensity of seasonal rainfall in the study locations according to the four definitions. Subsequently, we applied the set of the generated onset dates according to the four definitions as sowing dates in simulations of maize growth and yields using the  Agricultural Production Systems sIMulator (APSIM). Our analysis showed a statistically significant difference between the onset dates defined by the four definitions. Raman’s and Stern’s definitions delayed the monsoon onset at least two to four weeks after 1st June while Yamada’s and Liebman’s definitions delayed the onset one to two weeks after 1st June. Moreover, the amounts of seasonal rainfall in the season defined by Raman’s and Stern’s definitions were on average lower and more variable compared to those defined by Yamada’s and Liebman’s definitions. Similarly, we found statistically significant differences between the means of simulated maize yields in the four sets of sowing dates used. The highest yields with the lowest interannual variability were found in Yamada followed by Liebman’s sowing dates. The other sets of sowing dates had very low yields and higher variability compared to Yamada’s and Liebman’s sowing dates. We found the SSTa from the Southern Atlantic Ocean, Mediterranean Sea, and Tropical Atlantic Ocean regions as good predictors of both onset dates and intensity of the monsoon. The accuracy ranged from 50% to 80% depending on the location. </p>
APSIM – maize model was validated with the experimental data on three maize cultivars (Sammaz 33,... more APSIM – maize model was validated with the experimental data on three maize cultivars (Sammaz 33, Sammaz 37 and Sammaz 27) sown on three different dates during 2015 wet season at the Institute for Agricultural Research Samaru Zaria Nigeria (Lat. 7R” 38’N, Long. 11R” 11’E Lat. 686m). For the testing efficiency of the model performance, R2, RMSE and RMSEn was computed. RMSEn betweenobserved and simulated values by APSIM for grain yield was lowest (5.4%) for Sammaz 33 cultivar as compared to Sammaz 37 (10.5%) and Sammaz 26 (27.6%). Similar results were obtained for the other parameters, with Sammaz 33 out yielding the other two cultivars. The observed values under second sowing date showed better performance of days to flowering, physiological maturity and leaf area index for all the varieties. The grain yield performance were higher under first sowing date. The results led to the conclusion that APSIM model is efficient in simulating maize growth and development in arid environment of...
<p>In rain-fed systems, efficient and timely crop planning is crucial to ma... more <p>In rain-fed systems, efficient and timely crop planning is crucial to maximize crop production, adapt to climate variability, and increase the sustainability and resilience of the production systems. Smallholder farmers plan and anticipate possible interventions during the season based on the actual onset of the monsoon. However, their knowledge to define and predict the monsoon onset is limited to traditional methods whose predictive skill decreases significantly with a recent increase in both temperature and rainfall variability in the region. Therefore, defining the start of the monsoon accurately is a priority for improving crop production in rain-fed systems. Since the 1970s, researchers have produced more than 18 definitions—from local to regional scale—to define the start of the monsoon in the Sahel region which makes it difficult for one to find a suitable definition for a specific application. The present study compared and analyzed the West African Monsoon (WAM) onset according to Raman’s, Stern’s, Yamada’s, and Liebman’s definitions using station data from 13 locations in Senegal i.e. Dakar, Louga, Matam, St. Louis, Thies, Diourbel, Fatick, Kaffrine, Kaolack, Kedougou, Kolda, Tambacounda, and Ziguinchor from 1981 to 2020. To this end, we applied machine learning algorithms—<em>K-means clustering and Decision Tree</em>—to cluster the Sea Surface Temperature anomalies (SSTa) obtained from different regions of the Mediterranean and the Atlantic Ocean. We then used the clusters in the decision tree model to predict the onset and intensity of seasonal rainfall in the study locations according to the four definitions. Subsequently, we applied the set of the generated onset dates according to the four definitions as sowing dates in simulations of maize growth and yields using the  Agricultural Production Systems sIMulator (APSIM). Our analysis showed a statistically significant difference between the onset dates defined by the four definitions. Raman’s and Stern’s definitions delayed the monsoon onset at least two to four weeks after 1st June while Yamada’s and Liebman’s definitions delayed the onset one to two weeks after 1st June. Moreover, the amounts of seasonal rainfall in the season defined by Raman’s and Stern’s definitions were on average lower and more variable compared to those defined by Yamada’s and Liebman’s definitions. Similarly, we found statistically significant differences between the means of simulated maize yields in the four sets of sowing dates used. The highest yields with the lowest interannual variability were found in Yamada followed by Liebman’s sowing dates. The other sets of sowing dates had very low yields and higher variability compared to Yamada’s and Liebman’s sowing dates. We found the SSTa from the Southern Atlantic Ocean, Mediterranean Sea, and Tropical Atlantic Ocean regions as good predictors of both onset dates and intensity of the monsoon. The accuracy ranged from 50% to 80% depending on the location. </p>
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Papers by Folorunso M AKINSEYE