ABSTRACT
While many have extolled the potential impacts of digital advisory services for smallholder agriculture, the evidence for sustained uptake of such tools remains limited. This paper utilizes a survey of tool developers and researchers, as well as a systematic meta-analysis of prior studies, to assess the extent and challenges of scaling decision support tools for site-specific soil nutrient management (SSNM-DST) across smallholder farming systems, where "scaling" is defined as a significant increase in tool usage beyond pilot levels. Our evaluation draws on relevant literature, expert opinion and apps available in different repositories. Despite their acclaimed yield benefits, we find that SSNM-DST have struggled to reach scale over the last few decades and, with strong heterogeneity in adoption among intended stakeholders and tools. For example, the log odds of a SSNM-DST reaching 5-10 % of the target farmers compared with reaching none, decreases by â¼200% when a technical problem is stated as a reason for the tools' failure to be used at scale. We find a similar decrease in odds ratios when technical, socioeconomic, policy, and R&D constraints were identified as barriers to scaling by national extension and private systems. Meta-regression analysis indicates that the response ratio of using SSNM-DST over Farmer Fertilizer Practice (FFP) varies by non-tool related covariates, such as initial crop yield potential under FFP, current and past crop types, acidity class of the soil, temperature and rainfall regimes, and the amount of input under FFP. In general, the SSNM-DST have moved one step forward compared with the traditional 'blanket' fertilizer recommendation by accounting for in-field heterogeneities in soil and crop characteristics, while remaining undifferentiated in terms of demographic and socioeconomic heterogeneities among users, which potentially constrains adoption at scale. The SSNM-DSTs possess reasonable applicability and can be labeled 'ready' from purely scientific viewpoints, although their readiness for system-level uptake at scale remains limited, especially where socio-technical and institutional constraints are prevalent.
ABSTRACT
Intra-plot heterogeneity in yield is often observed in smallholder farming systems, although its implications for yield measurement remain under-investigated. Using a unique dataset on smallholder maize production in Ethiopia, we quantify the magnitude of inter- and intra-plot heterogeneity, describe the relationship between intra-plot heterogeneity and maize productivity, and document the implications of intra-field heterogeneity on the accuracy of alternative yield estimation protocols. Our data include five common yield estimation protocols, as well as full plot harvests of 230 smallholder maize fields. We surveyed agronomic decisions, biophysical variables, and accessibility characteristics of the surveyed fields. We quantify intra-plot heterogeneity using the coefficient of variation (CV) of stand density, cob weight, and maize grain yield. A generalized linear mixed model is used to explore the relationship between these variables and the method- and heterogeneity-dependence of yield estimation accuracy. We find inter-plot CV values ranging from 32 to 56 %, 22 to 73 % and 39 to 49 % in population density, cob weight and grain yield, respectively. Intra-plot heterogeneity constituted most of this variation, with across-method mean CV values of 41 %, 82 % and 63 %, respectively, of the total variability in population density, cob weight and grain yield. A rise in intra-plot heterogeneity of 0.5 % to 0.8 % is associated with a significant increase in yield estimation error under alternative yield estimation protocols. Regression analysis shows that interactions in agronomic decisions, input intensity and plot accessibility factors dictate intra-plot heterogeneity and method accuracy in smallholder systems. Intra-plot heterogeneity is larger than inter-plot heterogeneity in the current study area. Our analysis shows that the effect of intra-plot heterogeneity on yield estimation accuracies is method-dependent and yield estimation methods that fail to capture true intra-plot heterogeneity are more error-prone. Results of such estimations should be considered with caution when used as the basis of decision-making.
ABSTRACT
Variability in crop response and nutrient use efficiencies to fertilizer application is quite common under varying soil and climatic conditions. Understanding such variability is vital to develop farm- and area- specific soil nutrient management and fertilizer recommendations. Hence the objectives of this study were to assess maize grain yield response to nutrient applications for identifying yield-limiting nutrients and to understand the magnitude of nutrient use efficiencies under varying soil and rainfall conditions. A total of 150 on-farm nutrient omission trials (NOTs) were conducted on farmers' field in high rainfall and moisture stress areas. The treatments were control, PK, NK, NP, NPK and NPK+ secondary and micronutrients. Maize grain yield, nutrient uptake, agronomic and recovery efficiencies of N and P differed between fertilizer treatments and between the contrasting agro-ecologies. The AEN ranged from 24.8 to 32.5 kg grain kg-1 N in Jimma area and from 1.0 kg grain kg-1 N (NK treatment) to 10.2 kg grain kg-1 N (NPK treatment) at Adami Tullu and from 0.1 kg grain kg-1 N (NK treatment) to 8.3 kg grain kg-1 N (NPK treatment) at Bulbula. The differing parameters between the agro-ecologies were related to difference in rainfall amount and not to soil factors. Grain yield response to N application and agronomic efficiencies of N and P were higher in the high rainfall area than in the moisture stress areas. Grain yield responded the most to nitrogen (N) application than to any other nutrients at most of the experimental sites. Owing to the magnificent yield response to N fertilizer in the current study, proper management of nitrogen is very essential for intensification of maize productivity in most maize growing areas of Ethiopia.
