Simulating Regional Cassava Yield Gap in Nigeria

Cassava production is essential for food security in Sub-Saharan Africa and serves as a major calorie- intake source in Nigeria. Here we use a crop model, LINTUL5, embedded into a modeling framework SIMPLACE to estimate potential cassava yield gaps (Yg) in 30 states of Nigeria. Our study of climate parameter influence on the variability of current and potential yields and Yg shows that cumulative radiation and precipitation were the most significant factors associated with cassava yield variability (p = 0.01). The cumulative Yg mean was estimated as 18202 kg∙ha-1, with a maximum of 31207 kg ha-1 in Kano state. Across the states, nutrient limitation accounts for 55.3% of the total cassava yield gap, while the remaining 44.7% is attributed to water limitation. The highest untapped water-limited yields were estimated in States, such as Bauchi, Gombe, and Sokoto, characterized by the short rainy season. Conclusively, the current cassava yield levels can be increased by a factor of five through soil fertility enhancement and with irrigation, particularly in semi-arid regions.

​Impact of Cluster Front Line Demonstrations on Productivity and Economics of Lentil (Lens culinaris L.), Chickpea (Cicer aeritinum L.) and Field Pea (Pisum sativum L.) in Old Alluvial Zone of Malda, West Bengal, India

Background: There are significant gaps in yields of pulses among potential, demonstration and farmers’ plots in West Bengal. This is mostly due to adoption of traditional local varieties and poor cultivation practices. Methods: The yield, technology and extension gaps were studied on improved varieties of lentil, chickpea and field pea during rabi seasons of 2017/18-2020/21 in 80 hectare area of Malda, West Bengal with 205 farmers. Result: The highest and lowest magnitude of technology index (TI) was observed in field pea var. IPFD-10-12 (44.5%) during 2018/19 and in lentil var. KL-320 (30.1%) during 2020/21. Among lentil varieties, the highest benefit cost ratio was obtained from KL-320 (2.87) whereas in case of chickpea, it was obtained from NBEG-49 (2.90). For field pea var. IPFD-10-12 the benefit cost ratio was 2.70. Maximum and minimum extension yield gaps were observed in field pea var. IPFD-10-12 (451 kg ha-1) during 2018/19 and in lentil var. IPL-406 (145 kg ha-1) during 2018/19. Overall assessment reflects lentil var. KL-320, chickpea variety NBEG-49 and field pea variety IPFD-10-12 have good potentiality and most suitable for this region. So, it is hypothesized that improved farm technology has enormous potential to scale up pulse productivity through cluster front line demonstration (CFLD).

Estimating Yield Components, Limiting Factors, and Yield Gaps of Enset in Ethiopia Using Easily Measurable Above-Ground Plant Traits

The quantification of yield for different enset products has mainly been based on farmers’ estimates, which are often inaccurate. Several allometric models have been developed to overcome this challenge. Building on past work, the current study developed allometric models for enset fiber, kocho, and bula yield estimation. Enset yield limiting factors and associated yield gaps were also determined. In this study, above-ground growth and yield (kocho, bula, and fiber) traits of five-year-old plants of two widely grown enset landraces, ‘Unjame’ and ‘Siskela’, were assessed in farmers’ fields at three contrasting altitude sites. Except for bula, a minor yield component, correlation, and PCA analysis showed strong association between the above-ground and yield traits. Allometric equations based on the above-ground traits significantly (R2 = 25 to 68%) explained the variation in the yield traits. This study, for the first time, generated allometric models that can reliably estimate enset fiber yield. Leaf length, petiole length, and plant height are especially good for estimating fiber and kocho yields. The performance of models for bula were poor possibly due to the very low bula yields per plant. Soil chemical characteristics differently influenced enset yield attributes. For example, improving K supply can potentially enhance fiber yield. Higher yield gaps were observed for bula, with P accounting for the highest yield gaps across yield traits. Through careful targeting, the different yield attributes can thus be enhanced. This and previous studies clearly show that non-destructive enset plant assessments can provide solid information for quick and easy yield assessments for various traits during e.g., agronomic, germplasm evaluation, soil fertility enhancement, and intercropping trials.