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.

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.

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.

Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model.

We identified the most sensitive genotype-specific parameters (GSPs) and their contribution to the uncertainty of the MANIHOT simulation model. We applied a global sensitivity and uncertainty analysis (GSUA) of the GSPs to the simulation outputs for the cassava development, growth, and yield in contrasting environments. We compared enhanced Sampling for Uniformity, a qualitative screening method new to crop simulation modeling, and Sobol, a quantitative, variance-based method. About 80% of the GSPs contributed to most of the variation in maximum leaf area index (LAI), yield, and aboveground biomass at harvest. Relative importance of the GSPs varied between warm and cool temperatures but did not differ between rainfed and no water limitation conditions. Interactions between GSPs explained 20% of the variance in simulated outputs. Overall, the most important GSPs were individual node weight, radiation use efficiency, and maximum individual leaf area. Base temperature for leaf development was more important for cool compared to warm temperatures. Parameter uncertainty had a substantial impact on model predictions in MANIHOT simulations, with the uncertainty 2-5 times larger for warm compared to cool temperatures. Identification of important GSPs provides an objective way to determine the processes of a simulation model that are critical versus those that have little relevance.

Datasets from fertilized improved and local varieties of cassava grown in the highlands of South Kivu, Democratic Republic of Congo.

The use of mineral fertilizer and organic inputs with an improved and local variety of cassava allows (i) to identify nutrient limitations to cassava production, (ii) to investigate the effects of variety and combined application of mineral and organic inputs on cassava growth and yield and (iii) to evaluate the profitability of the improved variety and fertilizer use in cassava production. Data on growth, yield and yield components of an improved and local variety of cassava, economic analysis, soil and weather, collected during two growing cycles of cassava in farmer's fields in the highlands of the Democratic Republic of Congo (DR Congo) are presented. The data complement the recently published paper "Increased cassava growth and yields through improved variety use and fertilizer application in the highlands of South Kivu, Democratic Republic of Congo" (Munyahali et al., 2023) [1]. Data on plant height and diameter were collected throughout the growing period of the crop while the data on the storage root, stem, tradable storage root, non-tradable storage root and harvest index were determined at 12 months after planting (MAP). An economic analysis was performed using a simplified financial analysis whereby additional benefits were calculated relative to the respective control treatments; the total costs included the purchasing price of fertilizers and the additional net benefits represented the revenue from the increased storage root yield due to fertilizer application. The value cost ratio (VCR) was calculated as the additional net benefits over the cost of fertilizer purchase.

Correction: Combining organic and mineral fertilizers as a climate-smart integrated soil fertility management practice in sub-Saharan Africa: A meta-analysis.

[This corrects the article DOI: 10.1371/journal.pone.0239552.].

Combining organic and mineral fertilizers as a climate-smart integrated soil fertility management practice in sub-Saharan Africa: A meta-analysis.

Low productivity and climate change require climate-smart agriculture (CSA) for sub-Saharan Africa (SSA), through (i) sustainably increasing crop productivity, (ii) enhancing the resilience of agricultural systems, and (iii) offsetting greenhouse gas emissions. We conducted a meta-analysis on experimental data to evaluate the contributions of combining organic and mineral nitrogen (N) applications to the three pillars of CSA for maize (Zea mays). Linear mixed effect modeling was carried out for; (i) grain productivity and agronomic efficiency of N (AE) inputs, (ii) inter-seasonal yield variability, and (iii) changes in soil organic carbon (SOC) content, while accounting for the quality of organic amendments and total N rates. Results showed that combined application of mineral and organic fertilizers leads to greater responses in productivity and AE as compared to sole applications when more than 100 kg N ha-1 is used with high-quality organic matter. For yield variability and SOC, no significant interactions were found when combining mineral and organic fertilizers. The variability of maize yields in soils amended with high-quality organic matter, except manure, was equal or smaller than for sole mineral fertilizer. Increases of SOC were only significant for organic inputs, and more pronounced for high-quality resources. For example, at a total N rate of 150 kg N ha-1 season-1, combining mineral fertilizer with the highest quality organic resources (50:50) increased AE by 20% and reduced SOC losses by 18% over 7 growing seasons as compared to sole mineral fertilizer. We conclude that combining organic and mineral N fertilizers can have significant positive effects on productivity and AE, but only improves the other two CSA pillars yield variability and SOC depending on organic resource input and quality. The findings of our meta-analysis help to tailor a climate smart integrated soil fertility management in SSA.