Spatial and temporal distribution of optimal maize sowing dates in Nigeria.

Climate change and inter-annual variability cause variation in rainfall commencement and cessation which has consequences for the maize growing season length and thus impact yields. This study therefore sought to determine the spatially explicit optimum maize sowing dates to enable site specific recommendations in Nigeria. Gridded weather and soil data, crop management and cultivar were used to simulate maize yield from 1981-2019 at a scale of 0.5°. A total of 37 potential sowing dates between 1 March and 7 November at an interval of 7 days for each year were evaluated. The optimum sowing date was the date which maximizes yield at harvest, keeping all other management factors constant. The results show that optimum sowing dates significantly vary across the country with northern Nigeria having notably delayed sowing dates compared to southern Nigeria which has earlier planting dates. The long-term optimal sowing dates significantly (p<0.05), shifted between the 1980s (1981-1990), and current (2011-2019), for most of the country. The most optimum planting dates of southern Nigeria shifted to later sowing dates while most optimum sowing dates of central and northern Nigeria shifted to earlier sowing dates. There was more variation in optimum sowing dates in the wetter than the drier agro-ecologies. Changes in climate explain changes in sowing dates in wetter agro-ecologies compared to drier agro-ecologies. The study concludes that the optimum sowing dates derived from this study and the corresponding methodology used to generate them can be used to improve cropping calendars in maize farming in Nigeria.

Cassava-maize intercropping systems in southern Nigeria: Radiation use efficiency, soil moisture dynamics, and yields of component crops.

Efficient utilization of incident solar radiation and rainwater conservation in rain-fed smallholder cropping systems require the development and adoption of cropping systems with high resource use efficiency. Due to the popularity of cassava-maize intercropping and the food security and economic importance of both crops in Nigeria, we investigated options to improve interception of photosynthetically active radiation (IPAR), radiation use efficiency (RUE), soil moisture retention, and yields of cassava and maize in cassava-maize intercropping systems in 8 on-farm researcher-managed multi-location trials between 2017 and 2019 in different agro-ecologies of southern Nigeria. Treatments were a combination of (1) maize planting density (low density at 20,000 maize plants ha-1 versus high density at 40,000 maize plants ha-1, intercropped with 12,500 cassava plants ha-1); (2) fertilizer application and management targeting either the maize crop (90 kg N, 20 kg P and 37 kg K ha-1) or the cassava crop (75 kg N, 20 kg P and 90 kg K ha-1), compared with control without fertilizer application. Cassava and maize development parameters were highest in the maize fertilizer regime, resulting in the highest IPAR at high maize density. The combined intercrop biomass yield was highest at high maize density in the maize fertilizer regime. Without fertilizer application, RUE was highest at low maize density. However, the application of the maize fertilizer regime at high maize density resulted in the highest RUE, soil moisture content, and maize grain yield. Cassava storage root yield was higher in the cassava fertilizer regime than in the maize fertilizer regime. We conclude that improved IPAR, RUE, soil moisture retention, and grain yield on nutrient-limited soils of southern Nigeria, or in similar environments, can be achieved by intercropping 40,000 maize plants ha-1 with 12,500 cassava plants ha-1 and managing the system with the maize fertilizer regime. However, for higher cassava storage root yield, the system should be managed with the cassava fertilizer regime.

Developing recommendations for increased productivity in cassava-maize intercropping systems in Southern Nigeria.

Cassava-maize intercropping is a common practice among smallholder farmers in Southern Nigeria. It provides food security and early access to income from the maize component. However, yields of both crops are commonly low in farmers' fields. Multi-locational trials were conducted in Southern Nigeria in 2016 and 2017 to investigate options to increase productivity and profitability through increased cassava and maize plant densities and fertilizer application. Trials with 4 and 6 treatments in 2016 and 2017, respectively were established on 126 farmers' fields over two seasons with a set of different designs, including combinations of two levels of crop density and three levels of fertilizer rates. The maize crop was tested at low density (LM) with 20,000 plants ha-1 versus high density (HM) with 40,000 plants ha-1. For cassava, low density (LC) had had 10,000 plants ha-1 versus the high density (HC) with 12,500 plants ha-1.; The fertilizer application followed a regime favouring either the maize crop (FM: 90 kg N, 20 kg P and 37 kg K ha-1) or the cassava crop (FC: 75 kg N, 20 kg P and 90 kg K ha-1), next to control without fertilizer application (F0). Higher maize density (HM) increased marketable maize cob yield by 14 % (3700 cobs ha-1) in the first cycle and by 8% (2100 cobs ha-1) in the second cycle, relative to the LM treatment. Across both cropping cycles, fertilizer application increased cob yield by 15 % (5000 cobs ha-1) and 19 % (6700 cobs ha-1) in the FC and FM regime, respectively. Cassava storage root yield increased by 16 % (4 Mg ha-1) due to increased cassava plant density, and by 14 % (4 Mg ha-1) due to fertilizer application (i.e., with both fertilizer regimes) but only in the first cropping cycle. In the second cycle, increased maize plant density (HM) reduced cassava storage root yield by 7% (1.5 Mg ha-1) relative to the LM treatment. However, the negative effect of high maize density on storage root yield was counteracted by fertilizer application. Fresh storage root yield increased by 8% (2 Mg ha-1) in both fertilizer regimes compared to the control without fertilizer application. Responses to fertilizer by cassava and maize varied between fields. Positive responses tended to decline with increasing yields in the control treatment. The average value-to-cost ratio (VCR) of fertilizer use for the FM regime was 3.6 and higher than for the FC regime (VCR = 1.6), resulting from higher maize yields when FM than when FC was applied. Revenue generated by maize constituted 84-91% of the total revenue of the cropping system. The highest profits were achieved with the FM regime when both cassava and maize were grown at high density. However, fertilizer application was not always advisable as 34 % of farmers did not realize a profit. For higher yields and profitability, fertilizer recommendations should be targeted to responsive fields based on soil fertility knowledge.

Modeling growth, development and yield of cassava: A review.

Cassava is an important crop in the developing world. The goal of this study was to review published cassava models (18) for their capability to simulate storage root biomass and to categorize them into static and dynamic models. The majority (14) are dynamic and capture within season growth dynamics. Most (13) of the dynamic models consider environmental factors such as temperature, solar radiation, soil water and nutrient restrictions. More than half (10) have been calibrated for a distinct genotype. Only one of the four static models includes environmental variables. While the static regression models are useful to estimate final yield, their application is limited to the locations or varieties used for their development unless recalibrated for distinct conditions. Dynamic models simulate growth process and provide estimates of yield over time with, in most cases, no fixed maturity date. The dynamic models that simulate the detailed development of nodal units tend to be less accurate in determining final yield compared to the simpler dynamic and statistic models. However, they can be more safely applied to novel environmental conditions that can be explored in silico. Deficiencies in the current models are highlighted including suggestions on how they can be improved. None of the current dynamic cassava models adequately simulates the starch content of fresh cassava roots with almost all models based on dry biomass simulations. Further studies are necessary to develop a new module for existing cassava models to simulate cassava quality.