Digital technology in maize nutrient management research in northern Nigeria amid COVID-19 pandemic.

The COVID-19 pandemic has been a life threatening and spreads wildly with physical human contact. Physical distancing is recommended by health experts to prevent the spread; thus, agronomic research has to be designed in conformity to this preventive standard during the pandemic. Consequently, this study was designed to evaluate the reliability of using digital tools in nutrient management research amid the COVID-19 pandemic in northern Nigeria. Fifty extension agents (EAs) were selected across 15 LGAs of Kaduna and Kano states. The EAs were trained on how to generate fertilizer recommendation using an android mobile phone-based nutrient expert (NE), to measure farmers' field sizes using UTM Area measure mobile phone app, and open data kit to record, submit and aggregate data during the exercise. Each EA covered 50 farms, where two nutrient management practices-one determined by the farmers: farmer fertilizer practice (FFP), and the other generated using the NE were evaluated. Results show that around 90% of the farmers have an average field size of 1.13 ha. All selected farmers used improved maize varieties for planting, among which 21% been able to use the exact recommended or lower seed rate. Use of inorganic fertilizer was 33% higher than the average recommended NE rate, while average yield of the NE fields was 48% higher than for the FFP. The results of this study indicate that yield can be improved with site-specific nutrient management (SSNM) extension approach. The SSNM using digital tools as the NE seem promising and befits to agronomic research in northern Nigeria amid the COVID-19 pandemic.

Simulating potential yield of rainfed soybean in northeast Nigeria.

We used the CROPGRO-Soybean model to simulate the production potential of rainfed soybean in northeast Nigeria. Data from ten soybean experiments conducted under optimal conditions in 2016-2018 at Kano and Dambatta in the Sudan savanna (SS) agroecological zone were used to determine the cultivar coefficients and calibrate the model for the varieties TGX 1448-2E and TGX1951-3 F. The model was evaluated with data from four phosphorous response trials conducted at Zaria and Doguwa in the northern Guinea savanna (GS) of Nigeria between 2016 and 2018. Results show that the CROPGRO-Soybean model was able to accurately simulate soybean growth and grain yield with low RMSE and high d-index values. Consequently, the model was used to investigate the rainfed yield potential of the two varieties in 24 sites in northeast Nigeria under different sowing windows using 30-year (1985-2014) weather data. The result shows that soybean can be grown in northeast Nigeria, but yield performance is dependent on location, variety and sowing window. The simulated yield was higher in the SS than in the GS agro-ecozone despite the longer growing period in the later. Low yield was simulated for TGX 1448-2E for most of the sites. The yield of TGX1951-3 F was above a threshold of 1500 kg ha-1 in 5 out of 12 sites in the GS and 7 out of 12 sites in the SS, suggesting that this variety is the most suitable for cultivation in northeast Nigeria. Sowing TGX 1951-3 F can be delayed to July 16 at Gwaskara, Nasarawo Demsa and Tawa in the GS and at Briyel, Lakundum, Jara Dali, Kurbo Gayi, and Mathau in the SS with a low-risk of crop failure. The desired yield will be achieved at Chikala and Puba Vidau with a significantly low risk of crop failure for all sowing windows. The results from this study suggest that the CSM-CROPGRO-Soybean model can be a valuable tool in determining the right variety and sowing window for soybean production in targeted agroecological zones in northeast Nigeria.

Understanding nutrient imbalances in maize (Zea mays L.) using the diagnosis and recommendation integrated system (DRIS) approach in the Maize belt of Nigeria.

Low nutrient use efficiency in maize as a result of imbalanced nutrition has been reported to drastically reduce yield. We implemented a nutrient omission experiment to assess the effect of nutrient application on maize yield and nutritional balance. Maize ear leaves were analyzed for nutrients, to identify nutrient balance status using the Diagnostic and Recommendation Integrated System (DRIS) approach. Results indicated that omission of N or P resulted in highly imbalanced DRIS indices respectively, and significantly lower grain yield. A strong inverse relationship between K ear leaf content with DRIS index suggests that K application negatively increases K imbalance in many situations. Imbalances of Mg, Ca and Cu were more associated with higher yielding treatments. A Which-Won-Where result show that nutrient imbalances in the diagnosis were systematically frequent when N was omitted. All the diagnosed nutrients were imbalanced even under the highest yielding NPKZn treatment; indicating further opportunity for yield increase with more balanced nutrition. Balanced nutrition of maize in the maize belt of Nigeria should target application of varying rates of N, P, K, Mg, S and Zn, depending on the soil conditions. But, because of complexities of nutrient interactions during uptake, it is hardly possible to realize a balanced nutrition. However, differentiating the application of antagonistic nutrients into foliar or soil-based methods is recommended for a more balanced maize nutrition.